[split-required] Split remaining Python monoliths (Phase 1 continued)

klausur-service (7 monoliths):
- grid_editor_helpers.py (1,737 → 5 files: columns, filters, headers, zones)
- cv_cell_grid.py (1,675 → 7 files: build, legacy, streaming, merge, vocab)
- worksheet_editor_api.py (1,305 → 4 files: models, AI, reconstruct, routes)
- legal_corpus_ingestion.py (1,280 → 3 files: registry, chunking, ingestion)
- cv_review.py (1,248 → 4 files: pipeline, spell, LLM, barrel)
- cv_preprocessing.py (1,166 → 3 files: deskew, dewarp, barrel)
- rbac.py, admin_api.py, routes/eh.py remain (next batch)

backend-lehrer (1 monolith):
- classroom_engine/repository.py (1,705 → 7 files by domain)

All re-export barrels preserve backward compatibility.
Zero import errors verified.

Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>

.claude/rules/loc-exceptions.txt

@@ -17,6 +17,7 @@
 
 # Pure Data Registries (keine Logik, nur Daten-Definitionen)
 **/dsfa_sources_registry.py | owner=klausur | reason=Pure data registry (license + source definitions, no logic) | review=2027-01-01
+**/legal_corpus_registry.py | owner=klausur | reason=Pure data registry (Regulation dataclass + 47 regulation definitions, no logic) | review=2027-01-01
 **/backlog/backlog-items.ts | owner=admin-lehrer | reason=Pure data array (506 LOC, no logic, only BacklogItem[] literals) | review=2027-01-01
 **/lib/module-registry-data.ts | owner=admin-lehrer | reason=Pure data array (510 LOC, no logic, only BackendModule[] literals) | review=2027-01-01
 

backend-lehrer/classroom_engine/repository_context.py

@@ -0,0 +1,453 @@
+"""
+Teacher Context, Schoolyear Event & Recurring Routine Repositories.
+
+CRUD-Operationen fuer Schuljahres-Kontext (Phase 8).
+"""
+from datetime import datetime
+from typing import Optional, List, Dict, Any
+
+from sqlalchemy.orm import Session as DBSession
+
+from .context_models import (
+    TeacherContextDB, SchoolyearEventDB, RecurringRoutineDB,
+    MacroPhaseEnum, EventTypeEnum, EventStatusEnum,
+    RoutineTypeEnum, RecurrencePatternEnum,
+    FEDERAL_STATES, SCHOOL_TYPES,
+)
+
+
+class TeacherContextRepository:
+    """Repository fuer Lehrer-Kontext CRUD-Operationen (Phase 8)."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    # ==================== CREATE / GET-OR-CREATE ====================
+
+    def get_or_create(self, teacher_id: str) -> TeacherContextDB:
+        """
+        Holt den Kontext eines Lehrers oder erstellt einen neuen.
+
+        Args:
+            teacher_id: ID des Lehrers
+
+        Returns:
+            TeacherContextDB Model
+        """
+        context = self.get_by_teacher_id(teacher_id)
+        if context:
+            return context
+
+        # Neuen Kontext erstellen
+        from uuid import uuid4
+        context = TeacherContextDB(
+            id=str(uuid4()),
+            teacher_id=teacher_id,
+            macro_phase=MacroPhaseEnum.ONBOARDING,
+        )
+        self.db.add(context)
+        self.db.commit()
+        self.db.refresh(context)
+        return context
+
+    # ==================== READ ====================
+
+    def get_by_teacher_id(self, teacher_id: str) -> Optional[TeacherContextDB]:
+        """Holt den Kontext eines Lehrers."""
+        return self.db.query(TeacherContextDB).filter(
+            TeacherContextDB.teacher_id == teacher_id
+        ).first()
+
+    # ==================== UPDATE ====================
+
+    def update_context(
+        self,
+        teacher_id: str,
+        federal_state: str = None,
+        school_type: str = None,
+        schoolyear: str = None,
+        schoolyear_start: datetime = None,
+        macro_phase: str = None,
+        current_week: int = None,
+    ) -> Optional[TeacherContextDB]:
+        """Aktualisiert den Kontext eines Lehrers."""
+        context = self.get_or_create(teacher_id)
+
+        if federal_state is not None:
+            context.federal_state = federal_state
+        if school_type is not None:
+            context.school_type = school_type
+        if schoolyear is not None:
+            context.schoolyear = schoolyear
+        if schoolyear_start is not None:
+            context.schoolyear_start = schoolyear_start
+        if macro_phase is not None:
+            context.macro_phase = MacroPhaseEnum(macro_phase)
+        if current_week is not None:
+            context.current_week = current_week
+
+        self.db.commit()
+        self.db.refresh(context)
+        return context
+
+    def complete_onboarding(self, teacher_id: str) -> TeacherContextDB:
+        """Markiert Onboarding als abgeschlossen."""
+        context = self.get_or_create(teacher_id)
+        context.onboarding_completed = True
+        context.macro_phase = MacroPhaseEnum.SCHULJAHRESSTART
+        self.db.commit()
+        self.db.refresh(context)
+        return context
+
+    def update_flags(
+        self,
+        teacher_id: str,
+        has_classes: bool = None,
+        has_schedule: bool = None,
+        is_exam_period: bool = None,
+        is_before_holidays: bool = None,
+    ) -> TeacherContextDB:
+        """Aktualisiert die Status-Flags eines Kontexts."""
+        context = self.get_or_create(teacher_id)
+
+        if has_classes is not None:
+            context.has_classes = has_classes
+        if has_schedule is not None:
+            context.has_schedule = has_schedule
+        if is_exam_period is not None:
+            context.is_exam_period = is_exam_period
+        if is_before_holidays is not None:
+            context.is_before_holidays = is_before_holidays
+
+        self.db.commit()
+        self.db.refresh(context)
+        return context
+
+    def to_dict(self, context: TeacherContextDB) -> Dict[str, Any]:
+        """Konvertiert DB-Model zu Dictionary."""
+        return {
+            "id": context.id,
+            "teacher_id": context.teacher_id,
+            "school": {
+                "federal_state": context.federal_state,
+                "federal_state_name": FEDERAL_STATES.get(context.federal_state, ""),
+                "school_type": context.school_type,
+                "school_type_name": SCHOOL_TYPES.get(context.school_type, ""),
+            },
+            "school_year": {
+                "id": context.schoolyear,
+                "start": context.schoolyear_start.isoformat() if context.schoolyear_start else None,
+                "current_week": context.current_week,
+            },
+            "macro_phase": {
+                "id": context.macro_phase.value,
+                "label": self._get_phase_label(context.macro_phase),
+            },
+            "flags": {
+                "onboarding_completed": context.onboarding_completed,
+                "has_classes": context.has_classes,
+                "has_schedule": context.has_schedule,
+                "is_exam_period": context.is_exam_period,
+                "is_before_holidays": context.is_before_holidays,
+            },
+            "created_at": context.created_at.isoformat() if context.created_at else None,
+            "updated_at": context.updated_at.isoformat() if context.updated_at else None,
+        }
+
+    def _get_phase_label(self, phase: MacroPhaseEnum) -> str:
+        """Gibt den Anzeigenamen einer Makro-Phase zurueck."""
+        labels = {
+            MacroPhaseEnum.ONBOARDING: "Einrichtung",
+            MacroPhaseEnum.SCHULJAHRESSTART: "Schuljahresstart",
+            MacroPhaseEnum.UNTERRICHTSAUFBAU: "Unterrichtsaufbau",
+            MacroPhaseEnum.LEISTUNGSPHASE_1: "Leistungsphase 1",
+            MacroPhaseEnum.HALBJAHRESABSCHLUSS: "Halbjahresabschluss",
+            MacroPhaseEnum.LEISTUNGSPHASE_2: "Leistungsphase 2",
+            MacroPhaseEnum.JAHRESABSCHLUSS: "Jahresabschluss",
+        }
+        return labels.get(phase, phase.value)
+
+
+class SchoolyearEventRepository:
+    """Repository fuer Schuljahr-Events (Phase 8)."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    def create(
+        self,
+        teacher_id: str,
+        title: str,
+        start_date: datetime,
+        event_type: str = "other",
+        end_date: datetime = None,
+        class_id: str = None,
+        subject: str = None,
+        description: str = "",
+        needs_preparation: bool = True,
+        reminder_days_before: int = 7,
+        extra_data: Dict[str, Any] = None,
+    ) -> SchoolyearEventDB:
+        """Erstellt ein neues Schuljahr-Event."""
+        from uuid import uuid4
+        event = SchoolyearEventDB(
+            id=str(uuid4()),
+            teacher_id=teacher_id,
+            title=title,
+            event_type=EventTypeEnum(event_type),
+            start_date=start_date,
+            end_date=end_date,
+            class_id=class_id,
+            subject=subject,
+            description=description,
+            needs_preparation=needs_preparation,
+            reminder_days_before=reminder_days_before,
+            extra_data=extra_data or {},
+        )
+        self.db.add(event)
+        self.db.commit()
+        self.db.refresh(event)
+        return event
+
+    def get_by_id(self, event_id: str) -> Optional[SchoolyearEventDB]:
+        """Holt ein Event nach ID."""
+        return self.db.query(SchoolyearEventDB).filter(
+            SchoolyearEventDB.id == event_id
+        ).first()
+
+    def get_by_teacher(
+        self,
+        teacher_id: str,
+        status: str = None,
+        event_type: str = None,
+        limit: int = 50,
+    ) -> List[SchoolyearEventDB]:
+        """Holt Events eines Lehrers."""
+        query = self.db.query(SchoolyearEventDB).filter(
+            SchoolyearEventDB.teacher_id == teacher_id
+        )
+        if status:
+            query = query.filter(SchoolyearEventDB.status == EventStatusEnum(status))
+        if event_type:
+            query = query.filter(SchoolyearEventDB.event_type == EventTypeEnum(event_type))
+
+        return query.order_by(SchoolyearEventDB.start_date).limit(limit).all()
+
+    def get_upcoming(
+        self,
+        teacher_id: str,
+        days: int = 30,
+        limit: int = 10,
+    ) -> List[SchoolyearEventDB]:
+        """Holt anstehende Events der naechsten X Tage."""
+        from datetime import timedelta
+        now = datetime.utcnow()
+        end = now + timedelta(days=days)
+
+        return self.db.query(SchoolyearEventDB).filter(
+            SchoolyearEventDB.teacher_id == teacher_id,
+            SchoolyearEventDB.start_date >= now,
+            SchoolyearEventDB.start_date <= end,
+            SchoolyearEventDB.status != EventStatusEnum.CANCELLED,
+        ).order_by(SchoolyearEventDB.start_date).limit(limit).all()
+
+    def update_status(
+        self,
+        event_id: str,
+        status: str,
+        preparation_done: bool = None,
+    ) -> Optional[SchoolyearEventDB]:
+        """Aktualisiert den Status eines Events."""
+        event = self.get_by_id(event_id)
+        if not event:
+            return None
+
+        event.status = EventStatusEnum(status)
+        if preparation_done is not None:
+            event.preparation_done = preparation_done
+
+        self.db.commit()
+        self.db.refresh(event)
+        return event
+
+    def delete(self, event_id: str) -> bool:
+        """Loescht ein Event."""
+        event = self.get_by_id(event_id)
+        if not event:
+            return False
+        self.db.delete(event)
+        self.db.commit()
+        return True
+
+    def to_dict(self, event: SchoolyearEventDB) -> Dict[str, Any]:
+        """Konvertiert DB-Model zu Dictionary."""
+        return {
+            "id": event.id,
+            "teacher_id": event.teacher_id,
+            "event_type": event.event_type.value,
+            "title": event.title,
+            "description": event.description,
+            "start_date": event.start_date.isoformat() if event.start_date else None,
+            "end_date": event.end_date.isoformat() if event.end_date else None,
+            "class_id": event.class_id,
+            "subject": event.subject,
+            "status": event.status.value,
+            "needs_preparation": event.needs_preparation,
+            "preparation_done": event.preparation_done,
+            "reminder_days_before": event.reminder_days_before,
+            "extra_data": event.extra_data,
+            "created_at": event.created_at.isoformat() if event.created_at else None,
+        }
+
+
+class RecurringRoutineRepository:
+    """Repository fuer wiederkehrende Routinen (Phase 8)."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    def create(
+        self,
+        teacher_id: str,
+        title: str,
+        routine_type: str = "other",
+        recurrence_pattern: str = "weekly",
+        day_of_week: int = None,
+        day_of_month: int = None,
+        time_of_day: str = None,  # Format: "14:00"
+        duration_minutes: int = 60,
+        description: str = "",
+        valid_from: datetime = None,
+        valid_until: datetime = None,
+    ) -> RecurringRoutineDB:
+        """Erstellt eine neue wiederkehrende Routine."""
+        from uuid import uuid4
+        from datetime import time as dt_time
+
+        time_obj = None
+        if time_of_day:
+            parts = time_of_day.split(":")
+            time_obj = dt_time(int(parts[0]), int(parts[1]))
+
+        routine = RecurringRoutineDB(
+            id=str(uuid4()),
+            teacher_id=teacher_id,
+            title=title,
+            routine_type=RoutineTypeEnum(routine_type),
+            recurrence_pattern=RecurrencePatternEnum(recurrence_pattern),
+            day_of_week=day_of_week,
+            day_of_month=day_of_month,
+            time_of_day=time_obj,
+            duration_minutes=duration_minutes,
+            description=description,
+            valid_from=valid_from,
+            valid_until=valid_until,
+        )
+        self.db.add(routine)
+        self.db.commit()
+        self.db.refresh(routine)
+        return routine
+
+    def get_by_id(self, routine_id: str) -> Optional[RecurringRoutineDB]:
+        """Holt eine Routine nach ID."""
+        return self.db.query(RecurringRoutineDB).filter(
+            RecurringRoutineDB.id == routine_id
+        ).first()
+
+    def get_by_teacher(
+        self,
+        teacher_id: str,
+        is_active: bool = True,
+        routine_type: str = None,
+    ) -> List[RecurringRoutineDB]:
+        """Holt Routinen eines Lehrers."""
+        query = self.db.query(RecurringRoutineDB).filter(
+            RecurringRoutineDB.teacher_id == teacher_id
+        )
+        if is_active is not None:
+            query = query.filter(RecurringRoutineDB.is_active == is_active)
+        if routine_type:
+            query = query.filter(RecurringRoutineDB.routine_type == RoutineTypeEnum(routine_type))
+
+        return query.all()
+
+    def get_today(self, teacher_id: str) -> List[RecurringRoutineDB]:
+        """Holt Routinen die heute stattfinden."""
+        today = datetime.utcnow()
+        day_of_week = today.weekday()  # 0 = Montag
+        day_of_month = today.day
+
+        routines = self.get_by_teacher(teacher_id, is_active=True)
+        today_routines = []
+
+        for routine in routines:
+            if routine.recurrence_pattern == RecurrencePatternEnum.DAILY:
+                today_routines.append(routine)
+            elif routine.recurrence_pattern == RecurrencePatternEnum.WEEKLY:
+                if routine.day_of_week == day_of_week:
+                    today_routines.append(routine)
+            elif routine.recurrence_pattern == RecurrencePatternEnum.BIWEEKLY:
+                # Vereinfacht: Pruefen ob Tag passt (echte Logik braucht Startdatum)
+                if routine.day_of_week == day_of_week:
+                    today_routines.append(routine)
+            elif routine.recurrence_pattern == RecurrencePatternEnum.MONTHLY:
+                if routine.day_of_month == day_of_month:
+                    today_routines.append(routine)
+
+        return today_routines
+
+    def update(
+        self,
+        routine_id: str,
+        title: str = None,
+        is_active: bool = None,
+        day_of_week: int = None,
+        time_of_day: str = None,
+    ) -> Optional[RecurringRoutineDB]:
+        """Aktualisiert eine Routine."""
+        routine = self.get_by_id(routine_id)
+        if not routine:
+            return None
+
+        if title is not None:
+            routine.title = title
+        if is_active is not None:
+            routine.is_active = is_active
+        if day_of_week is not None:
+            routine.day_of_week = day_of_week
+        if time_of_day is not None:
+            from datetime import time as dt_time
+            parts = time_of_day.split(":")
+            routine.time_of_day = dt_time(int(parts[0]), int(parts[1]))
+
+        self.db.commit()
+        self.db.refresh(routine)
+        return routine
+
+    def delete(self, routine_id: str) -> bool:
+        """Loescht eine Routine."""
+        routine = self.get_by_id(routine_id)
+        if not routine:
+            return False
+        self.db.delete(routine)
+        self.db.commit()
+        return True
+
+    def to_dict(self, routine: RecurringRoutineDB) -> Dict[str, Any]:
+        """Konvertiert DB-Model zu Dictionary."""
+        return {
+            "id": routine.id,
+            "teacher_id": routine.teacher_id,
+            "routine_type": routine.routine_type.value,
+            "title": routine.title,
+            "description": routine.description,
+            "recurrence_pattern": routine.recurrence_pattern.value,
+            "day_of_week": routine.day_of_week,
+            "day_of_month": routine.day_of_month,
+            "time_of_day": routine.time_of_day.isoformat() if routine.time_of_day else None,
+            "duration_minutes": routine.duration_minutes,
+            "is_active": routine.is_active,
+            "valid_from": routine.valid_from.isoformat() if routine.valid_from else None,
+            "valid_until": routine.valid_until.isoformat() if routine.valid_until else None,
+            "created_at": routine.created_at.isoformat() if routine.created_at else None,
+        }

backend-lehrer/classroom_engine/repository_feedback.py

@@ -0,0 +1,182 @@
+"""
+Teacher Feedback Repository.
+
+CRUD-Operationen fuer Lehrer-Feedback (Phase 7).
+Ermoeglicht Lehrern, Bugs, Feature-Requests und Verbesserungen zu melden.
+"""
+from datetime import datetime
+from typing import Optional, List, Dict, Any
+
+from sqlalchemy.orm import Session as DBSession
+
+from .db_models import (
+    TeacherFeedbackDB, FeedbackTypeEnum, FeedbackStatusEnum,
+    FeedbackPriorityEnum,
+)
+
+
+class TeacherFeedbackRepository:
+    """
+    Repository fuer Lehrer-Feedback CRUD-Operationen.
+
+    Ermoeglicht Lehrern, Feedback (Bugs, Feature-Requests, Verbesserungen)
+    direkt aus dem Lehrer-Frontend zu senden.
+    """
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    def create(
+        self,
+        teacher_id: str,
+        title: str,
+        description: str,
+        feedback_type: str = "improvement",
+        priority: str = "medium",
+        teacher_name: str = "",
+        teacher_email: str = "",
+        context_url: str = "",
+        context_phase: str = "",
+        context_session_id: str = None,
+        user_agent: str = "",
+        related_feature: str = None,
+    ) -> TeacherFeedbackDB:
+        """Erstellt neues Feedback."""
+        import uuid
+
+        db_feedback = TeacherFeedbackDB(
+            id=str(uuid.uuid4()),
+            teacher_id=teacher_id,
+            teacher_name=teacher_name,
+            teacher_email=teacher_email,
+            title=title,
+            description=description,
+            feedback_type=FeedbackTypeEnum(feedback_type),
+            priority=FeedbackPriorityEnum(priority),
+            status=FeedbackStatusEnum.NEW,
+            related_feature=related_feature,
+            context_url=context_url,
+            context_phase=context_phase,
+            context_session_id=context_session_id,
+            user_agent=user_agent,
+        )
+
+        self.db.add(db_feedback)
+        self.db.commit()
+        self.db.refresh(db_feedback)
+        return db_feedback
+
+    def get_by_id(self, feedback_id: str) -> Optional[TeacherFeedbackDB]:
+        """Holt Feedback nach ID."""
+        return self.db.query(TeacherFeedbackDB).filter(
+            TeacherFeedbackDB.id == feedback_id
+        ).first()
+
+    def get_all(
+        self,
+        status: str = None,
+        feedback_type: str = None,
+        limit: int = 100,
+        offset: int = 0
+    ) -> List[TeacherFeedbackDB]:
+        """Holt alle Feedbacks mit optionalen Filtern."""
+        query = self.db.query(TeacherFeedbackDB)
+
+        if status:
+            query = query.filter(TeacherFeedbackDB.status == FeedbackStatusEnum(status))
+        if feedback_type:
+            query = query.filter(TeacherFeedbackDB.feedback_type == FeedbackTypeEnum(feedback_type))
+
+        return query.order_by(
+            TeacherFeedbackDB.created_at.desc()
+        ).offset(offset).limit(limit).all()
+
+    def get_by_teacher(self, teacher_id: str, limit: int = 50) -> List[TeacherFeedbackDB]:
+        """Holt Feedback eines bestimmten Lehrers."""
+        return self.db.query(TeacherFeedbackDB).filter(
+            TeacherFeedbackDB.teacher_id == teacher_id
+        ).order_by(
+            TeacherFeedbackDB.created_at.desc()
+        ).limit(limit).all()
+
+    def update_status(
+        self,
+        feedback_id: str,
+        status: str,
+        response: str = None,
+        responded_by: str = None
+    ) -> Optional[TeacherFeedbackDB]:
+        """Aktualisiert den Status eines Feedbacks."""
+        db_feedback = self.get_by_id(feedback_id)
+        if not db_feedback:
+            return None
+
+        db_feedback.status = FeedbackStatusEnum(status)
+        if response:
+            db_feedback.response = response
+            db_feedback.responded_at = datetime.utcnow()
+            db_feedback.responded_by = responded_by
+
+        self.db.commit()
+        self.db.refresh(db_feedback)
+        return db_feedback
+
+    def delete(self, feedback_id: str) -> bool:
+        """Loescht ein Feedback."""
+        db_feedback = self.get_by_id(feedback_id)
+        if not db_feedback:
+            return False
+
+        self.db.delete(db_feedback)
+        self.db.commit()
+        return True
+
+    def get_stats(self) -> Dict[str, Any]:
+        """Gibt Statistiken ueber alle Feedbacks zurueck."""
+        all_feedback = self.db.query(TeacherFeedbackDB).all()
+
+        stats = {
+            "total": len(all_feedback),
+            "by_status": {},
+            "by_type": {},
+            "by_priority": {},
+        }
+
+        for fb in all_feedback:
+            # By Status
+            status = fb.status.value
+            stats["by_status"][status] = stats["by_status"].get(status, 0) + 1
+
+            # By Type
+            fb_type = fb.feedback_type.value
+            stats["by_type"][fb_type] = stats["by_type"].get(fb_type, 0) + 1
+
+            # By Priority
+            priority = fb.priority.value
+            stats["by_priority"][priority] = stats["by_priority"].get(priority, 0) + 1
+
+        return stats
+
+    def to_dict(self, db_feedback: TeacherFeedbackDB) -> Dict[str, Any]:
+        """Konvertiert DB-Model zu Dictionary."""
+        return {
+            "id": db_feedback.id,
+            "teacher_id": db_feedback.teacher_id,
+            "teacher_name": db_feedback.teacher_name,
+            "teacher_email": db_feedback.teacher_email,
+            "title": db_feedback.title,
+            "description": db_feedback.description,
+            "feedback_type": db_feedback.feedback_type.value,
+            "priority": db_feedback.priority.value,
+            "status": db_feedback.status.value,
+            "related_feature": db_feedback.related_feature,
+            "context_url": db_feedback.context_url,
+            "context_phase": db_feedback.context_phase,
+            "context_session_id": db_feedback.context_session_id,
+            "user_agent": db_feedback.user_agent,
+            "response": db_feedback.response,
+            "responded_at": db_feedback.responded_at.isoformat() if db_feedback.responded_at else None,
+            "responded_by": db_feedback.responded_by,
+            "created_at": db_feedback.created_at.isoformat() if db_feedback.created_at else None,
+            "updated_at": db_feedback.updated_at.isoformat() if db_feedback.updated_at else None,
+        }

backend-lehrer/classroom_engine/repository_homework.py

@@ -0,0 +1,382 @@
+"""
+Homework & Material Repositories.
+
+CRUD-Operationen fuer Hausaufgaben (Feature f20) und Phasen-Materialien (Feature f19).
+"""
+from datetime import datetime
+from typing import Optional, List
+
+from sqlalchemy.orm import Session as DBSession
+
+from .db_models import (
+    HomeworkDB, HomeworkStatusEnum, PhaseMaterialDB, MaterialTypeEnum,
+)
+from .models import (
+    Homework, HomeworkStatus, PhaseMaterial, MaterialType,
+)
+
+
+class HomeworkRepository:
+    """Repository fuer Hausaufgaben-Tracking (Feature f20)."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    # ==================== CREATE ====================
+
+    def create(self, homework: Homework) -> HomeworkDB:
+        """Erstellt eine neue Hausaufgabe."""
+        db_homework = HomeworkDB(
+            id=homework.homework_id,
+            teacher_id=homework.teacher_id,
+            class_id=homework.class_id,
+            subject=homework.subject,
+            title=homework.title,
+            description=homework.description,
+            session_id=homework.session_id,
+            due_date=homework.due_date,
+            status=HomeworkStatusEnum(homework.status.value),
+        )
+        self.db.add(db_homework)
+        self.db.commit()
+        self.db.refresh(db_homework)
+        return db_homework
+
+    # ==================== READ ====================
+
+    def get_by_id(self, homework_id: str) -> Optional[HomeworkDB]:
+        """Holt eine Hausaufgabe nach ID."""
+        return self.db.query(HomeworkDB).filter(
+            HomeworkDB.id == homework_id
+        ).first()
+
+    def get_by_teacher(
+        self,
+        teacher_id: str,
+        status: Optional[str] = None,
+        limit: int = 50
+    ) -> List[HomeworkDB]:
+        """Holt alle Hausaufgaben eines Lehrers."""
+        query = self.db.query(HomeworkDB).filter(
+            HomeworkDB.teacher_id == teacher_id
+        )
+        if status:
+            query = query.filter(HomeworkDB.status == HomeworkStatusEnum(status))
+        return query.order_by(
+            HomeworkDB.due_date.asc().nullslast(),
+            HomeworkDB.created_at.desc()
+        ).limit(limit).all()
+
+    def get_by_class(
+        self,
+        class_id: str,
+        teacher_id: str,
+        include_completed: bool = False,
+        limit: int = 20
+    ) -> List[HomeworkDB]:
+        """Holt alle Hausaufgaben einer Klasse."""
+        query = self.db.query(HomeworkDB).filter(
+            HomeworkDB.class_id == class_id,
+            HomeworkDB.teacher_id == teacher_id
+        )
+        if not include_completed:
+            query = query.filter(HomeworkDB.status != HomeworkStatusEnum.COMPLETED)
+        return query.order_by(
+            HomeworkDB.due_date.asc().nullslast(),
+            HomeworkDB.created_at.desc()
+        ).limit(limit).all()
+
+    def get_by_session(self, session_id: str) -> List[HomeworkDB]:
+        """Holt alle Hausaufgaben einer Session."""
+        return self.db.query(HomeworkDB).filter(
+            HomeworkDB.session_id == session_id
+        ).order_by(HomeworkDB.created_at.desc()).all()
+
+    def get_pending(
+        self,
+        teacher_id: str,
+        days_ahead: int = 7
+    ) -> List[HomeworkDB]:
+        """Holt anstehende Hausaufgaben der naechsten X Tage."""
+        from datetime import timedelta
+        cutoff = datetime.utcnow() + timedelta(days=days_ahead)
+        return self.db.query(HomeworkDB).filter(
+            HomeworkDB.teacher_id == teacher_id,
+            HomeworkDB.status.in_([HomeworkStatusEnum.ASSIGNED, HomeworkStatusEnum.IN_PROGRESS]),
+            HomeworkDB.due_date <= cutoff
+        ).order_by(HomeworkDB.due_date.asc()).all()
+
+    # ==================== UPDATE ====================
+
+    def update_status(
+        self,
+        homework_id: str,
+        status: HomeworkStatus
+    ) -> Optional[HomeworkDB]:
+        """Aktualisiert den Status einer Hausaufgabe."""
+        db_homework = self.get_by_id(homework_id)
+        if not db_homework:
+            return None
+
+        db_homework.status = HomeworkStatusEnum(status.value)
+        self.db.commit()
+        self.db.refresh(db_homework)
+        return db_homework
+
+    def update(self, homework: Homework) -> Optional[HomeworkDB]:
+        """Aktualisiert eine Hausaufgabe."""
+        db_homework = self.get_by_id(homework.homework_id)
+        if not db_homework:
+            return None
+
+        db_homework.title = homework.title
+        db_homework.description = homework.description
+        db_homework.due_date = homework.due_date
+        db_homework.status = HomeworkStatusEnum(homework.status.value)
+
+        self.db.commit()
+        self.db.refresh(db_homework)
+        return db_homework
+
+    # ==================== DELETE ====================
+
+    def delete(self, homework_id: str) -> bool:
+        """Loescht eine Hausaufgabe."""
+        db_homework = self.get_by_id(homework_id)
+        if not db_homework:
+            return False
+
+        self.db.delete(db_homework)
+        self.db.commit()
+        return True
+
+    # ==================== CONVERSION ====================
+
+    def to_dataclass(self, db_homework: HomeworkDB) -> Homework:
+        """Konvertiert DB-Model zu Dataclass."""
+        return Homework(
+            homework_id=db_homework.id,
+            teacher_id=db_homework.teacher_id,
+            class_id=db_homework.class_id,
+            subject=db_homework.subject,
+            title=db_homework.title,
+            description=db_homework.description or "",
+            session_id=db_homework.session_id,
+            due_date=db_homework.due_date,
+            status=HomeworkStatus(db_homework.status.value),
+            created_at=db_homework.created_at,
+            updated_at=db_homework.updated_at,
+        )
+
+
+class MaterialRepository:
+    """Repository fuer Phasen-Materialien (Feature f19)."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    # ==================== CREATE ====================
+
+    def create(self, material: PhaseMaterial) -> PhaseMaterialDB:
+        """Erstellt ein neues Material."""
+        db_material = PhaseMaterialDB(
+            id=material.material_id,
+            teacher_id=material.teacher_id,
+            title=material.title,
+            material_type=MaterialTypeEnum(material.material_type.value),
+            url=material.url,
+            description=material.description,
+            phase=material.phase,
+            subject=material.subject,
+            grade_level=material.grade_level,
+            tags=material.tags,
+            is_public=material.is_public,
+            usage_count=material.usage_count,
+            session_id=material.session_id,
+        )
+        self.db.add(db_material)
+        self.db.commit()
+        self.db.refresh(db_material)
+        return db_material
+
+    # ==================== READ ====================
+
+    def get_by_id(self, material_id: str) -> Optional[PhaseMaterialDB]:
+        """Holt ein Material nach ID."""
+        return self.db.query(PhaseMaterialDB).filter(
+            PhaseMaterialDB.id == material_id
+        ).first()
+
+    def get_by_teacher(
+        self,
+        teacher_id: str,
+        phase: Optional[str] = None,
+        subject: Optional[str] = None,
+        limit: int = 50
+    ) -> List[PhaseMaterialDB]:
+        """Holt alle Materialien eines Lehrers."""
+        query = self.db.query(PhaseMaterialDB).filter(
+            PhaseMaterialDB.teacher_id == teacher_id
+        )
+        if phase:
+            query = query.filter(PhaseMaterialDB.phase == phase)
+        if subject:
+            query = query.filter(PhaseMaterialDB.subject == subject)
+
+        return query.order_by(
+            PhaseMaterialDB.usage_count.desc(),
+            PhaseMaterialDB.created_at.desc()
+        ).limit(limit).all()
+
+    def get_by_phase(
+        self,
+        phase: str,
+        teacher_id: str,
+        include_public: bool = True
+    ) -> List[PhaseMaterialDB]:
+        """Holt alle Materialien fuer eine bestimmte Phase."""
+        if include_public:
+            return self.db.query(PhaseMaterialDB).filter(
+                PhaseMaterialDB.phase == phase,
+                (PhaseMaterialDB.teacher_id == teacher_id) |
+                (PhaseMaterialDB.is_public == True)
+            ).order_by(
+                PhaseMaterialDB.usage_count.desc()
+            ).all()
+        else:
+            return self.db.query(PhaseMaterialDB).filter(
+                PhaseMaterialDB.phase == phase,
+                PhaseMaterialDB.teacher_id == teacher_id
+            ).order_by(
+                PhaseMaterialDB.created_at.desc()
+            ).all()
+
+    def get_by_session(self, session_id: str) -> List[PhaseMaterialDB]:
+        """Holt alle Materialien einer Session."""
+        return self.db.query(PhaseMaterialDB).filter(
+            PhaseMaterialDB.session_id == session_id
+        ).order_by(PhaseMaterialDB.phase, PhaseMaterialDB.created_at).all()
+
+    def get_public_materials(
+        self,
+        phase: Optional[str] = None,
+        subject: Optional[str] = None,
+        limit: int = 20
+    ) -> List[PhaseMaterialDB]:
+        """Holt oeffentliche Materialien."""
+        query = self.db.query(PhaseMaterialDB).filter(
+            PhaseMaterialDB.is_public == True
+        )
+        if phase:
+            query = query.filter(PhaseMaterialDB.phase == phase)
+        if subject:
+            query = query.filter(PhaseMaterialDB.subject == subject)
+
+        return query.order_by(
+            PhaseMaterialDB.usage_count.desc()
+        ).limit(limit).all()
+
+    def search_by_tags(
+        self,
+        tags: List[str],
+        teacher_id: Optional[str] = None
+    ) -> List[PhaseMaterialDB]:
+        """Sucht Materialien nach Tags."""
+        query = self.db.query(PhaseMaterialDB)
+        if teacher_id:
+            query = query.filter(
+                (PhaseMaterialDB.teacher_id == teacher_id) |
+                (PhaseMaterialDB.is_public == True)
+            )
+        else:
+            query = query.filter(PhaseMaterialDB.is_public == True)
+
+        # Filter by tags - vereinfachte Implementierung
+        results = []
+        for material in query.all():
+            if material.tags and any(tag in material.tags for tag in tags):
+                results.append(material)
+        return results[:50]
+
+    # ==================== UPDATE ====================
+
+    def update(self, material: PhaseMaterial) -> Optional[PhaseMaterialDB]:
+        """Aktualisiert ein Material."""
+        db_material = self.get_by_id(material.material_id)
+        if not db_material:
+            return None
+
+        db_material.title = material.title
+        db_material.material_type = MaterialTypeEnum(material.material_type.value)
+        db_material.url = material.url
+        db_material.description = material.description
+        db_material.phase = material.phase
+        db_material.subject = material.subject
+        db_material.grade_level = material.grade_level
+        db_material.tags = material.tags
+        db_material.is_public = material.is_public
+
+        self.db.commit()
+        self.db.refresh(db_material)
+        return db_material
+
+    def increment_usage(self, material_id: str) -> Optional[PhaseMaterialDB]:
+        """Erhoeht den Usage-Counter eines Materials."""
+        db_material = self.get_by_id(material_id)
+        if not db_material:
+            return None
+
+        db_material.usage_count += 1
+        self.db.commit()
+        self.db.refresh(db_material)
+        return db_material
+
+    def attach_to_session(
+        self,
+        material_id: str,
+        session_id: str
+    ) -> Optional[PhaseMaterialDB]:
+        """Verknuepft ein Material mit einer Session."""
+        db_material = self.get_by_id(material_id)
+        if not db_material:
+            return None
+
+        db_material.session_id = session_id
+        db_material.usage_count += 1
+        self.db.commit()
+        self.db.refresh(db_material)
+        return db_material
+
+    # ==================== DELETE ====================
+
+    def delete(self, material_id: str) -> bool:
+        """Loescht ein Material."""
+        db_material = self.get_by_id(material_id)
+        if not db_material:
+            return False
+
+        self.db.delete(db_material)
+        self.db.commit()
+        return True
+
+    # ==================== CONVERSION ====================
+
+    def to_dataclass(self, db_material: PhaseMaterialDB) -> PhaseMaterial:
+        """Konvertiert DB-Model zu Dataclass."""
+        return PhaseMaterial(
+            material_id=db_material.id,
+            teacher_id=db_material.teacher_id,
+            title=db_material.title,
+            material_type=MaterialType(db_material.material_type.value),
+            url=db_material.url,
+            description=db_material.description or "",
+            phase=db_material.phase,
+            subject=db_material.subject or "",
+            grade_level=db_material.grade_level or "",
+            tags=db_material.tags or [],
+            is_public=db_material.is_public,
+            usage_count=db_material.usage_count,
+            session_id=db_material.session_id,
+            created_at=db_material.created_at,
+            updated_at=db_material.updated_at,
+        )

backend-lehrer/classroom_engine/repository_reflection.py

@@ -0,0 +1,315 @@
+"""
+Reflection & Analytics Repositories.
+
+CRUD-Operationen fuer Lesson-Reflections und Analytics-Abfragen (Phase 5).
+"""
+from datetime import datetime
+from typing import Optional, List, Dict, Any
+
+from sqlalchemy.orm import Session as DBSession
+
+from .db_models import LessonSessionDB, LessonPhaseEnum, LessonReflectionDB
+from .analytics import (
+    LessonReflection, SessionSummary, TeacherAnalytics, AnalyticsCalculator,
+)
+
+
+class ReflectionRepository:
+    """Repository fuer LessonReflection CRUD-Operationen."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    # ==================== CREATE ====================
+
+    def create(self, reflection: LessonReflection) -> LessonReflectionDB:
+        """Erstellt eine neue Reflection."""
+        db_reflection = LessonReflectionDB(
+            id=reflection.reflection_id,
+            session_id=reflection.session_id,
+            teacher_id=reflection.teacher_id,
+            notes=reflection.notes,
+            overall_rating=reflection.overall_rating,
+            what_worked=reflection.what_worked,
+            improvements=reflection.improvements,
+            notes_for_next_lesson=reflection.notes_for_next_lesson,
+        )
+        self.db.add(db_reflection)
+        self.db.commit()
+        self.db.refresh(db_reflection)
+        return db_reflection
+
+    # ==================== READ ====================
+
+    def get_by_id(self, reflection_id: str) -> Optional[LessonReflectionDB]:
+        """Holt eine Reflection nach ID."""
+        return self.db.query(LessonReflectionDB).filter(
+            LessonReflectionDB.id == reflection_id
+        ).first()
+
+    def get_by_session(self, session_id: str) -> Optional[LessonReflectionDB]:
+        """Holt die Reflection einer Session."""
+        return self.db.query(LessonReflectionDB).filter(
+            LessonReflectionDB.session_id == session_id
+        ).first()
+
+    def get_by_teacher(
+        self,
+        teacher_id: str,
+        limit: int = 20,
+        offset: int = 0
+    ) -> List[LessonReflectionDB]:
+        """Holt alle Reflections eines Lehrers."""
+        return self.db.query(LessonReflectionDB).filter(
+            LessonReflectionDB.teacher_id == teacher_id
+        ).order_by(
+            LessonReflectionDB.created_at.desc()
+        ).offset(offset).limit(limit).all()
+
+    # ==================== UPDATE ====================
+
+    def update(self, reflection: LessonReflection) -> Optional[LessonReflectionDB]:
+        """Aktualisiert eine Reflection."""
+        db_reflection = self.get_by_id(reflection.reflection_id)
+        if not db_reflection:
+            return None
+
+        db_reflection.notes = reflection.notes
+        db_reflection.overall_rating = reflection.overall_rating
+        db_reflection.what_worked = reflection.what_worked
+        db_reflection.improvements = reflection.improvements
+        db_reflection.notes_for_next_lesson = reflection.notes_for_next_lesson
+
+        self.db.commit()
+        self.db.refresh(db_reflection)
+        return db_reflection
+
+    # ==================== DELETE ====================
+
+    def delete(self, reflection_id: str) -> bool:
+        """Loescht eine Reflection."""
+        db_reflection = self.get_by_id(reflection_id)
+        if not db_reflection:
+            return False
+
+        self.db.delete(db_reflection)
+        self.db.commit()
+        return True
+
+    # ==================== CONVERSION ====================
+
+    def to_dataclass(self, db_reflection: LessonReflectionDB) -> LessonReflection:
+        """Konvertiert DB-Model zu Dataclass."""
+        return LessonReflection(
+            reflection_id=db_reflection.id,
+            session_id=db_reflection.session_id,
+            teacher_id=db_reflection.teacher_id,
+            notes=db_reflection.notes or "",
+            overall_rating=db_reflection.overall_rating,
+            what_worked=db_reflection.what_worked or [],
+            improvements=db_reflection.improvements or [],
+            notes_for_next_lesson=db_reflection.notes_for_next_lesson or "",
+            created_at=db_reflection.created_at,
+            updated_at=db_reflection.updated_at,
+        )
+
+
+class AnalyticsRepository:
+    """Repository fuer Analytics-Abfragen."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    def get_session_summary(self, session_id: str) -> Optional[SessionSummary]:
+        """
+        Berechnet die Summary einer abgeschlossenen Session.
+
+        Args:
+            session_id: ID der Session
+
+        Returns:
+            SessionSummary oder None wenn Session nicht gefunden
+        """
+        db_session = self.db.query(LessonSessionDB).filter(
+            LessonSessionDB.id == session_id
+        ).first()
+
+        if not db_session:
+            return None
+
+        # Session-Daten zusammenstellen
+        session_data = {
+            "session_id": db_session.id,
+            "teacher_id": db_session.teacher_id,
+            "class_id": db_session.class_id,
+            "subject": db_session.subject,
+            "topic": db_session.topic,
+            "lesson_started_at": db_session.lesson_started_at,
+            "lesson_ended_at": db_session.lesson_ended_at,
+            "phase_durations": db_session.phase_durations or {},
+        }
+
+        # Phase History aus DB oder JSON
+        phase_history = db_session.phase_history or []
+
+        # Summary berechnen
+        return AnalyticsCalculator.calculate_session_summary(
+            session_data, phase_history
+        )
+
+    def get_teacher_analytics(
+        self,
+        teacher_id: str,
+        period_start: Optional[datetime] = None,
+        period_end: Optional[datetime] = None
+    ) -> TeacherAnalytics:
+        """
+        Berechnet aggregierte Statistiken fuer einen Lehrer.
+
+        Args:
+            teacher_id: ID des Lehrers
+            period_start: Beginn des Zeitraums (default: 30 Tage zurueck)
+            period_end: Ende des Zeitraums (default: jetzt)
+
+        Returns:
+            TeacherAnalytics mit aggregierten Statistiken
+        """
+        from datetime import timedelta
+
+        if not period_end:
+            period_end = datetime.utcnow()
+        if not period_start:
+            period_start = period_end - timedelta(days=30)
+
+        # Sessions im Zeitraum abfragen
+        sessions_query = self.db.query(LessonSessionDB).filter(
+            LessonSessionDB.teacher_id == teacher_id,
+            LessonSessionDB.lesson_started_at >= period_start,
+            LessonSessionDB.lesson_started_at <= period_end
+        ).all()
+
+        # Sessions zu Dictionaries konvertieren
+        sessions_data = []
+        for db_session in sessions_query:
+            sessions_data.append({
+                "session_id": db_session.id,
+                "teacher_id": db_session.teacher_id,
+                "class_id": db_session.class_id,
+                "subject": db_session.subject,
+                "topic": db_session.topic,
+                "lesson_started_at": db_session.lesson_started_at,
+                "lesson_ended_at": db_session.lesson_ended_at,
+                "phase_durations": db_session.phase_durations or {},
+                "phase_history": db_session.phase_history or [],
+            })
+
+        return AnalyticsCalculator.calculate_teacher_analytics(
+            sessions_data, period_start, period_end
+        )
+
+    def get_phase_duration_trends(
+        self,
+        teacher_id: str,
+        phase: str,
+        limit: int = 20
+    ) -> List[Dict[str, Any]]:
+        """
+        Gibt die Dauer-Trends fuer eine bestimmte Phase zurueck.
+
+        Args:
+            teacher_id: ID des Lehrers
+            phase: Phasen-ID (einstieg, erarbeitung, etc.)
+            limit: Max Anzahl der Datenpunkte
+
+        Returns:
+            Liste von Datenpunkten [{date, planned, actual, difference}]
+        """
+        sessions = self.db.query(LessonSessionDB).filter(
+            LessonSessionDB.teacher_id == teacher_id,
+            LessonSessionDB.current_phase == LessonPhaseEnum.ENDED
+        ).order_by(
+            LessonSessionDB.lesson_ended_at.desc()
+        ).limit(limit).all()
+
+        trends = []
+        for db_session in sessions:
+            history = db_session.phase_history or []
+            for entry in history:
+                if entry.get("phase") == phase:
+                    planned = (db_session.phase_durations or {}).get(phase, 0) * 60
+                    actual = entry.get("duration_seconds", 0) or 0
+                    trends.append({
+                        "date": db_session.lesson_started_at.isoformat() if db_session.lesson_started_at else None,
+                        "session_id": db_session.id,
+                        "subject": db_session.subject,
+                        "planned_seconds": planned,
+                        "actual_seconds": actual,
+                        "difference_seconds": actual - planned,
+                    })
+                    break
+
+        return list(reversed(trends))  # Chronologisch sortieren
+
+    def get_overtime_analysis(
+        self,
+        teacher_id: str,
+        limit: int = 30
+    ) -> Dict[str, Any]:
+        """
+        Analysiert Overtime-Muster.
+
+        Args:
+            teacher_id: ID des Lehrers
+            limit: Anzahl der zu analysierenden Sessions
+
+        Returns:
+            Dict mit Overtime-Statistiken pro Phase
+        """
+        sessions = self.db.query(LessonSessionDB).filter(
+            LessonSessionDB.teacher_id == teacher_id,
+            LessonSessionDB.current_phase == LessonPhaseEnum.ENDED
+        ).order_by(
+            LessonSessionDB.lesson_ended_at.desc()
+        ).limit(limit).all()
+
+        phase_overtime: Dict[str, List[int]] = {
+            "einstieg": [],
+            "erarbeitung": [],
+            "sicherung": [],
+            "transfer": [],
+            "reflexion": [],
+        }
+
+        for db_session in sessions:
+            history = db_session.phase_history or []
+            phase_durations = db_session.phase_durations or {}
+
+            for entry in history:
+                phase = entry.get("phase", "")
+                if phase in phase_overtime:
+                    planned = phase_durations.get(phase, 0) * 60
+                    actual = entry.get("duration_seconds", 0) or 0
+                    overtime = max(0, actual - planned)
+                    phase_overtime[phase].append(overtime)
+
+        # Statistiken berechnen
+        result = {}
+        for phase, overtimes in phase_overtime.items():
+            if overtimes:
+                result[phase] = {
+                    "count": len([o for o in overtimes if o > 0]),
+                    "total": len(overtimes),
+                    "avg_overtime_seconds": sum(overtimes) / len(overtimes),
+                    "max_overtime_seconds": max(overtimes),
+                    "overtime_percentage": len([o for o in overtimes if o > 0]) / len(overtimes) * 100,
+                }
+            else:
+                result[phase] = {
+                    "count": 0,
+                    "total": 0,
+                    "avg_overtime_seconds": 0,
+                    "max_overtime_seconds": 0,
+                    "overtime_percentage": 0,
+                }
+
+        return result

backend-lehrer/classroom_engine/repository_session.py

@@ -0,0 +1,248 @@
+"""
+Session & Teacher Settings Repositories.
+
+CRUD-Operationen fuer LessonSessions und Lehrer-Einstellungen.
+"""
+from typing import Optional, List, Dict
+
+from sqlalchemy.orm import Session as DBSession
+
+from .db_models import (
+    LessonSessionDB, LessonPhaseEnum, TeacherSettingsDB,
+)
+from .models import (
+    LessonSession, LessonPhase, get_default_durations,
+)
+
+
+class SessionRepository:
+    """Repository fuer LessonSession CRUD-Operationen."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    # ==================== CREATE ====================
+
+    def create(self, session: LessonSession) -> LessonSessionDB:
+        """
+        Erstellt eine neue Session in der Datenbank.
+
+        Args:
+            session: LessonSession Dataclass
+
+        Returns:
+            LessonSessionDB Model
+        """
+        db_session = LessonSessionDB(
+            id=session.session_id,
+            teacher_id=session.teacher_id,
+            class_id=session.class_id,
+            subject=session.subject,
+            topic=session.topic,
+            current_phase=LessonPhaseEnum(session.current_phase.value),
+            is_paused=session.is_paused,
+            lesson_started_at=session.lesson_started_at,
+            lesson_ended_at=session.lesson_ended_at,
+            phase_started_at=session.phase_started_at,
+            pause_started_at=session.pause_started_at,
+            total_paused_seconds=session.total_paused_seconds,
+            phase_durations=session.phase_durations,
+            phase_history=session.phase_history,
+            notes=session.notes,
+            homework=session.homework,
+        )
+        self.db.add(db_session)
+        self.db.commit()
+        self.db.refresh(db_session)
+        return db_session
+
+    # ==================== READ ====================
+
+    def get_by_id(self, session_id: str) -> Optional[LessonSessionDB]:
+        """Holt eine Session nach ID."""
+        return self.db.query(LessonSessionDB).filter(
+            LessonSessionDB.id == session_id
+        ).first()
+
+    def get_active_by_teacher(self, teacher_id: str) -> List[LessonSessionDB]:
+        """Holt alle aktiven Sessions eines Lehrers."""
+        return self.db.query(LessonSessionDB).filter(
+            LessonSessionDB.teacher_id == teacher_id,
+            LessonSessionDB.current_phase != LessonPhaseEnum.ENDED
+        ).all()
+
+    def get_history_by_teacher(
+        self,
+        teacher_id: str,
+        limit: int = 20,
+        offset: int = 0
+    ) -> List[LessonSessionDB]:
+        """Holt Session-History eines Lehrers (Feature f17)."""
+        return self.db.query(LessonSessionDB).filter(
+            LessonSessionDB.teacher_id == teacher_id,
+            LessonSessionDB.current_phase == LessonPhaseEnum.ENDED
+        ).order_by(
+            LessonSessionDB.lesson_ended_at.desc()
+        ).offset(offset).limit(limit).all()
+
+    def get_by_class(
+        self,
+        class_id: str,
+        limit: int = 20
+    ) -> List[LessonSessionDB]:
+        """Holt Sessions einer Klasse."""
+        return self.db.query(LessonSessionDB).filter(
+            LessonSessionDB.class_id == class_id
+        ).order_by(
+            LessonSessionDB.created_at.desc()
+        ).limit(limit).all()
+
+    # ==================== UPDATE ====================
+
+    def update(self, session: LessonSession) -> Optional[LessonSessionDB]:
+        """
+        Aktualisiert eine bestehende Session.
+
+        Args:
+            session: LessonSession Dataclass mit aktualisierten Werten
+
+        Returns:
+            Aktualisierte LessonSessionDB oder None
+        """
+        db_session = self.get_by_id(session.session_id)
+        if not db_session:
+            return None
+
+        db_session.current_phase = LessonPhaseEnum(session.current_phase.value)
+        db_session.is_paused = session.is_paused
+        db_session.lesson_started_at = session.lesson_started_at
+        db_session.lesson_ended_at = session.lesson_ended_at
+        db_session.phase_started_at = session.phase_started_at
+        db_session.pause_started_at = session.pause_started_at
+        db_session.total_paused_seconds = session.total_paused_seconds
+        db_session.phase_durations = session.phase_durations
+        db_session.phase_history = session.phase_history
+        db_session.notes = session.notes
+        db_session.homework = session.homework
+
+        self.db.commit()
+        self.db.refresh(db_session)
+        return db_session
+
+    def update_notes(
+        self,
+        session_id: str,
+        notes: str,
+        homework: str
+    ) -> Optional[LessonSessionDB]:
+        """Aktualisiert nur Notizen und Hausaufgaben."""
+        db_session = self.get_by_id(session_id)
+        if not db_session:
+            return None
+
+        db_session.notes = notes
+        db_session.homework = homework
+
+        self.db.commit()
+        self.db.refresh(db_session)
+        return db_session
+
+    # ==================== DELETE ====================
+
+    def delete(self, session_id: str) -> bool:
+        """Loescht eine Session."""
+        db_session = self.get_by_id(session_id)
+        if not db_session:
+            return False
+
+        self.db.delete(db_session)
+        self.db.commit()
+        return True
+
+    # ==================== CONVERSION ====================
+
+    def to_dataclass(self, db_session: LessonSessionDB) -> LessonSession:
+        """
+        Konvertiert DB-Model zu Dataclass.
+
+        Args:
+            db_session: LessonSessionDB Model
+
+        Returns:
+            LessonSession Dataclass
+        """
+        return LessonSession(
+            session_id=db_session.id,
+            teacher_id=db_session.teacher_id,
+            class_id=db_session.class_id,
+            subject=db_session.subject,
+            topic=db_session.topic,
+            current_phase=LessonPhase(db_session.current_phase.value),
+            phase_started_at=db_session.phase_started_at,
+            lesson_started_at=db_session.lesson_started_at,
+            lesson_ended_at=db_session.lesson_ended_at,
+            is_paused=db_session.is_paused,
+            pause_started_at=db_session.pause_started_at,
+            total_paused_seconds=db_session.total_paused_seconds or 0,
+            phase_durations=db_session.phase_durations or get_default_durations(),
+            phase_history=db_session.phase_history or [],
+            notes=db_session.notes or "",
+            homework=db_session.homework or "",
+        )
+
+
+class TeacherSettingsRepository:
+    """Repository fuer Lehrer-Einstellungen (Feature f16)."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    def get_or_create(self, teacher_id: str) -> TeacherSettingsDB:
+        """Holt oder erstellt Einstellungen fuer einen Lehrer."""
+        settings = self.db.query(TeacherSettingsDB).filter(
+            TeacherSettingsDB.teacher_id == teacher_id
+        ).first()
+
+        if not settings:
+            settings = TeacherSettingsDB(
+                teacher_id=teacher_id,
+                default_phase_durations=get_default_durations(),
+            )
+            self.db.add(settings)
+            self.db.commit()
+            self.db.refresh(settings)
+
+        return settings
+
+    def update_phase_durations(
+        self,
+        teacher_id: str,
+        durations: Dict[str, int]
+    ) -> TeacherSettingsDB:
+        """Aktualisiert die Standard-Phasendauern."""
+        settings = self.get_or_create(teacher_id)
+        settings.default_phase_durations = durations
+        self.db.commit()
+        self.db.refresh(settings)
+        return settings
+
+    def update_preferences(
+        self,
+        teacher_id: str,
+        audio_enabled: Optional[bool] = None,
+        high_contrast: Optional[bool] = None,
+        show_statistics: Optional[bool] = None
+    ) -> TeacherSettingsDB:
+        """Aktualisiert UI-Praeferenzen."""
+        settings = self.get_or_create(teacher_id)
+
+        if audio_enabled is not None:
+            settings.audio_enabled = audio_enabled
+        if high_contrast is not None:
+            settings.high_contrast = high_contrast
+        if show_statistics is not None:
+            settings.show_statistics = show_statistics
+
+        self.db.commit()
+        self.db.refresh(settings)
+        return settings

backend-lehrer/classroom_engine/repository_template.py

@@ -0,0 +1,167 @@
+"""
+Template Repository.
+
+CRUD-Operationen fuer Stunden-Vorlagen (Feature f37).
+"""
+from typing import Optional, List
+
+from sqlalchemy.orm import Session as DBSession
+
+from .db_models import LessonTemplateDB
+from .models import LessonTemplate, get_default_durations
+
+
+class TemplateRepository:
+    """Repository fuer Stunden-Vorlagen (Feature f37)."""
+
+    def __init__(self, db: DBSession):
+        self.db = db
+
+    # ==================== CREATE ====================
+
+    def create(self, template: LessonTemplate) -> LessonTemplateDB:
+        """Erstellt eine neue Vorlage."""
+        db_template = LessonTemplateDB(
+            id=template.template_id,
+            teacher_id=template.teacher_id,
+            name=template.name,
+            description=template.description,
+            subject=template.subject,
+            grade_level=template.grade_level,
+            phase_durations=template.phase_durations,
+            default_topic=template.default_topic,
+            default_notes=template.default_notes,
+            is_public=template.is_public,
+            usage_count=template.usage_count,
+        )
+        self.db.add(db_template)
+        self.db.commit()
+        self.db.refresh(db_template)
+        return db_template
+
+    # ==================== READ ====================
+
+    def get_by_id(self, template_id: str) -> Optional[LessonTemplateDB]:
+        """Holt eine Vorlage nach ID."""
+        return self.db.query(LessonTemplateDB).filter(
+            LessonTemplateDB.id == template_id
+        ).first()
+
+    def get_by_teacher(
+        self,
+        teacher_id: str,
+        include_public: bool = True
+    ) -> List[LessonTemplateDB]:
+        """
+        Holt alle Vorlagen eines Lehrers.
+
+        Args:
+            teacher_id: ID des Lehrers
+            include_public: Auch oeffentliche Vorlagen anderer Lehrer einbeziehen
+        """
+        if include_public:
+            return self.db.query(LessonTemplateDB).filter(
+                (LessonTemplateDB.teacher_id == teacher_id) |
+                (LessonTemplateDB.is_public == True)
+            ).order_by(
+                LessonTemplateDB.usage_count.desc()
+            ).all()
+        else:
+            return self.db.query(LessonTemplateDB).filter(
+                LessonTemplateDB.teacher_id == teacher_id
+            ).order_by(
+                LessonTemplateDB.created_at.desc()
+            ).all()
+
+    def get_public_templates(self, limit: int = 20) -> List[LessonTemplateDB]:
+        """Holt oeffentliche Vorlagen, sortiert nach Beliebtheit."""
+        return self.db.query(LessonTemplateDB).filter(
+            LessonTemplateDB.is_public == True
+        ).order_by(
+            LessonTemplateDB.usage_count.desc()
+        ).limit(limit).all()
+
+    def get_by_subject(
+        self,
+        subject: str,
+        teacher_id: Optional[str] = None
+    ) -> List[LessonTemplateDB]:
+        """Holt Vorlagen fuer ein bestimmtes Fach."""
+        query = self.db.query(LessonTemplateDB).filter(
+            LessonTemplateDB.subject == subject
+        )
+        if teacher_id:
+            query = query.filter(
+                (LessonTemplateDB.teacher_id == teacher_id) |
+                (LessonTemplateDB.is_public == True)
+            )
+        else:
+            query = query.filter(LessonTemplateDB.is_public == True)
+
+        return query.order_by(
+            LessonTemplateDB.usage_count.desc()
+        ).all()
+
+    # ==================== UPDATE ====================
+
+    def update(self, template: LessonTemplate) -> Optional[LessonTemplateDB]:
+        """Aktualisiert eine Vorlage."""
+        db_template = self.get_by_id(template.template_id)
+        if not db_template:
+            return None
+
+        db_template.name = template.name
+        db_template.description = template.description
+        db_template.subject = template.subject
+        db_template.grade_level = template.grade_level
+        db_template.phase_durations = template.phase_durations
+        db_template.default_topic = template.default_topic
+        db_template.default_notes = template.default_notes
+        db_template.is_public = template.is_public
+
+        self.db.commit()
+        self.db.refresh(db_template)
+        return db_template
+
+    def increment_usage(self, template_id: str) -> Optional[LessonTemplateDB]:
+        """Erhoeht den Usage-Counter einer Vorlage."""
+        db_template = self.get_by_id(template_id)
+        if not db_template:
+            return None
+
+        db_template.usage_count += 1
+        self.db.commit()
+        self.db.refresh(db_template)
+        return db_template
+
+    # ==================== DELETE ====================
+
+    def delete(self, template_id: str) -> bool:
+        """Loescht eine Vorlage."""
+        db_template = self.get_by_id(template_id)
+        if not db_template:
+            return False
+
+        self.db.delete(db_template)
+        self.db.commit()
+        return True
+
+    # ==================== CONVERSION ====================
+
+    def to_dataclass(self, db_template: LessonTemplateDB) -> LessonTemplate:
+        """Konvertiert DB-Model zu Dataclass."""
+        return LessonTemplate(
+            template_id=db_template.id,
+            teacher_id=db_template.teacher_id,
+            name=db_template.name,
+            description=db_template.description or "",
+            subject=db_template.subject or "",
+            grade_level=db_template.grade_level or "",
+            phase_durations=db_template.phase_durations or get_default_durations(),
+            default_topic=db_template.default_topic or "",
+            default_notes=db_template.default_notes or "",
+            is_public=db_template.is_public,
+            usage_count=db_template.usage_count,
+            created_at=db_template.created_at,
+            updated_at=db_template.updated_at,
+        )

klausur-service/backend/cv_cell_grid_build.py

@@ -0,0 +1,498 @@
+"""
+Cell-grid construction v2 (hybrid: broad columns via word lookup, narrow via cell-crop).
+Extracted from cv_cell_grid.py.
+Lizenz: Apache 2.0 — DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+from concurrent.futures import ThreadPoolExecutor, as_completed
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+
+from cv_vocab_types import PageRegion, RowGeometry
+from cv_ocr_engines import (
+    RAPIDOCR_AVAILABLE,
+    _assign_row_words_to_columns,
+    _clean_cell_text,
+    _clean_cell_text_lite,
+    _words_to_reading_order_text,
+    _words_to_spaced_text,
+    ocr_region_lighton,
+    ocr_region_rapid,
+    ocr_region_trocr,
+)
+from cv_cell_grid_helpers import (
+    _MIN_WORD_CONF,
+    _ensure_minimum_crop_size,
+    _heal_row_gaps,
+    _is_artifact_row,
+    _select_psm_for_column,
+)
+
+logger = logging.getLogger(__name__)
+
+try:
+    import cv2
+except ImportError:
+    cv2 = None  # type: ignore[assignment]
+
+
+# ---------------------------------------------------------------------------
+# _ocr_cell_crop — isolated cell-crop OCR for v2 hybrid mode
+# ---------------------------------------------------------------------------
+
+def _ocr_cell_crop(
+    row_idx: int,
+    col_idx: int,
+    row: RowGeometry,
+    col: PageRegion,
+    ocr_img: np.ndarray,
+    img_bgr: Optional[np.ndarray],
+    img_w: int,
+    img_h: int,
+    engine_name: str,
+    lang: str,
+    lang_map: Dict[str, str],
+) -> Dict[str, Any]:
+    """OCR a single cell by cropping the exact column x row intersection.
+
+    No padding beyond cell boundaries -> no neighbour bleeding.
+    """
+    # Display bbox: exact column x row intersection
+    disp_x = col.x
+    disp_y = row.y
+    disp_w = col.width
+    disp_h = row.height
+
+    # Crop boundaries: add small internal padding (3px each side) to avoid
+    # clipping characters near column/row edges (e.g. parentheses, descenders).
+    # Stays within image bounds but may extend slightly beyond strict cell.
+    # 3px is small enough to avoid neighbour content at typical scan DPI (200-300).
+    _PAD = 3
+    cx = max(0, disp_x - _PAD)
+    cy = max(0, disp_y - _PAD)
+    cx2 = min(img_w, disp_x + disp_w + _PAD)
+    cy2 = min(img_h, disp_y + disp_h + _PAD)
+    cw = cx2 - cx
+    ch = cy2 - cy
+
+    empty_cell = {
+        'cell_id': f"R{row_idx:02d}_C{col_idx}",
+        'row_index': row_idx,
+        'col_index': col_idx,
+        'col_type': col.type,
+        'text': '',
+        'confidence': 0.0,
+        'bbox_px': {'x': disp_x, 'y': disp_y, 'w': disp_w, 'h': disp_h},
+        'bbox_pct': {
+            'x': round(disp_x / img_w * 100, 2) if img_w else 0,
+            'y': round(disp_y / img_h * 100, 2) if img_h else 0,
+            'w': round(disp_w / img_w * 100, 2) if img_w else 0,
+            'h': round(disp_h / img_h * 100, 2) if img_h else 0,
+        },
+        'ocr_engine': 'cell_crop_v2',
+        'is_bold': False,
+    }
+
+    if cw <= 0 or ch <= 0:
+        logger.debug("_ocr_cell_crop R%02d_C%d: zero-size crop (%dx%d)", row_idx, col_idx, cw, ch)
+        return empty_cell
+
+    # --- Pixel-density check: skip truly empty cells ---
+    if ocr_img is not None:
+        crop = ocr_img[cy:cy + ch, cx:cx + cw]
+        if crop.size > 0:
+            dark_ratio = float(np.count_nonzero(crop < 180)) / crop.size
+            if dark_ratio < 0.005:
+                logger.debug("_ocr_cell_crop R%02d_C%d: skip empty (dark_ratio=%.4f, crop=%dx%d)",
+                            row_idx, col_idx, dark_ratio, cw, ch)
+                return empty_cell
+
+    # --- Prepare crop for OCR ---
+    cell_lang = lang_map.get(col.type, lang)
+    psm = _select_psm_for_column(col.type, col.width, row.height)
+    text = ''
+    avg_conf = 0.0
+    used_engine = 'cell_crop_v2'
+
+    if engine_name in ("trocr-printed", "trocr-handwritten") and img_bgr is not None:
+        cell_region = PageRegion(type=col.type, x=cx, y=cy, width=cw, height=ch)
+        words = ocr_region_trocr(img_bgr, cell_region,
+                                 handwritten=(engine_name == "trocr-handwritten"))
+    elif engine_name == "lighton" and img_bgr is not None:
+        cell_region = PageRegion(type=col.type, x=cx, y=cy, width=cw, height=ch)
+        words = ocr_region_lighton(img_bgr, cell_region)
+    elif engine_name == "rapid" and img_bgr is not None:
+        # Upscale small BGR crops for RapidOCR.
+        bgr_crop = img_bgr[cy:cy + ch, cx:cx + cw]
+        if bgr_crop.size == 0:
+            words = []
+        else:
+            crop_h, crop_w = bgr_crop.shape[:2]
+            if crop_h < 80:
+                # Force 3x upscale for short rows — small chars need more pixels
+                scale = 3.0
+                bgr_up = cv2.resize(bgr_crop, None, fx=scale, fy=scale,
+                                    interpolation=cv2.INTER_CUBIC)
+            else:
+                bgr_up = _ensure_minimum_crop_size(bgr_crop, min_dim=150, max_scale=3)
+            up_h, up_w = bgr_up.shape[:2]
+            scale_x = up_w / max(crop_w, 1)
+            scale_y = up_h / max(crop_h, 1)
+            was_scaled = (up_w != crop_w or up_h != crop_h)
+            logger.debug("_ocr_cell_crop R%02d_C%d: rapid %dx%d -> %dx%d (scale=%.1fx)",
+                        row_idx, col_idx, crop_w, crop_h, up_w, up_h, scale_y)
+            tmp_region = PageRegion(type=col.type, x=0, y=0, width=up_w, height=up_h)
+            words = ocr_region_rapid(bgr_up, tmp_region)
+            # Remap positions back to original image coords
+            if words and was_scaled:
+                for w in words:
+                    w['left'] = int(w['left'] / scale_x) + cx
+                    w['top'] = int(w['top'] / scale_y) + cy
+                    w['width'] = int(w['width'] / scale_x)
+                    w['height'] = int(w['height'] / scale_y)
+            elif words:
+                for w in words:
+                    w['left'] += cx
+                    w['top'] += cy
+    else:
+        # Tesseract: upscale tiny crops for better recognition
+        if ocr_img is not None:
+            crop_slice = ocr_img[cy:cy + ch, cx:cx + cw]
+            upscaled = _ensure_minimum_crop_size(crop_slice)
+            up_h, up_w = upscaled.shape[:2]
+            tmp_region = PageRegion(type=col.type, x=0, y=0, width=up_w, height=up_h)
+            words = ocr_region(upscaled, tmp_region, lang=cell_lang, psm=psm)
+            # Remap word positions back to original image coordinates
+            if words and (up_w != cw or up_h != ch):
+                sx = cw / max(up_w, 1)
+                sy = ch / max(up_h, 1)
+                for w in words:
+                    w['left'] = int(w['left'] * sx) + cx
+                    w['top'] = int(w['top'] * sy) + cy
+                    w['width'] = int(w['width'] * sx)
+                    w['height'] = int(w['height'] * sy)
+            elif words:
+                for w in words:
+                    w['left'] += cx
+                    w['top'] += cy
+        else:
+            words = []
+
+    # Filter low-confidence words
+    if words:
+        words = [w for w in words if w.get('conf', 0) >= _MIN_WORD_CONF]
+
+    if words:
+        y_tol = max(15, ch)
+        text = _words_to_reading_order_text(words, y_tolerance_px=y_tol)
+        avg_conf = round(sum(w['conf'] for w in words) / len(words), 1)
+        logger.debug("_ocr_cell_crop R%02d_C%d: OCR raw text=%r conf=%.1f nwords=%d crop=%dx%d psm=%s engine=%s",
+                    row_idx, col_idx, text, avg_conf, len(words), cw, ch, psm, engine_name)
+    else:
+        logger.debug("_ocr_cell_crop R%02d_C%d: OCR returned NO words (crop=%dx%d psm=%s engine=%s)",
+                    row_idx, col_idx, cw, ch, psm, engine_name)
+
+    # --- PSM 7 fallback for still-empty Tesseract cells ---
+    if not text.strip() and engine_name == "tesseract" and ocr_img is not None:
+        crop_slice = ocr_img[cy:cy + ch, cx:cx + cw]
+        upscaled = _ensure_minimum_crop_size(crop_slice)
+        up_h, up_w = upscaled.shape[:2]
+        tmp_region = PageRegion(type=col.type, x=0, y=0, width=up_w, height=up_h)
+        psm7_words = ocr_region(upscaled, tmp_region, lang=cell_lang, psm=7)
+        if psm7_words:
+            psm7_words = [w for w in psm7_words if w.get('conf', 0) >= _MIN_WORD_CONF]
+        if psm7_words:
+            p7_text = _words_to_reading_order_text(psm7_words, y_tolerance_px=10)
+            if p7_text.strip():
+                text = p7_text
+                avg_conf = round(
+                    sum(w['conf'] for w in psm7_words) / len(psm7_words), 1
+                )
+                used_engine = 'cell_crop_v2_psm7'
+                # Remap PSM7 word positions back to original image coords
+                if up_w != cw or up_h != ch:
+                    sx = cw / max(up_w, 1)
+                    sy = ch / max(up_h, 1)
+                    for w in psm7_words:
+                        w['left'] = int(w['left'] * sx) + cx
+                        w['top'] = int(w['top'] * sy) + cy
+                        w['width'] = int(w['width'] * sx)
+                        w['height'] = int(w['height'] * sy)
+                else:
+                    for w in psm7_words:
+                        w['left'] += cx
+                        w['top'] += cy
+                words = psm7_words
+
+    # --- Noise filter ---
+    if text.strip():
+        pre_filter = text
+        text = _clean_cell_text_lite(text)
+        if not text:
+            logger.debug("_ocr_cell_crop R%02d_C%d: _clean_cell_text_lite REMOVED %r",
+                        row_idx, col_idx, pre_filter)
+            avg_conf = 0.0
+
+    result = dict(empty_cell)
+    result['text'] = text
+    result['confidence'] = avg_conf
+    result['ocr_engine'] = used_engine
+
+    # Store individual word bounding boxes (absolute image coordinates)
+    # for pixel-accurate overlay positioning in the frontend.
+    if words and text.strip():
+        result['word_boxes'] = [
+            {
+                'text': w.get('text', ''),
+                'left': w['left'],
+                'top': w['top'],
+                'width': w['width'],
+                'height': w['height'],
+                'conf': w.get('conf', 0),
+            }
+            for w in words
+            if w.get('text', '').strip()
+        ]
+
+    return result
+
+
+# Threshold: columns narrower than this (% of image width) use single-cell
+# crop OCR instead of full-page word assignment.
+_NARROW_COL_THRESHOLD_PCT = 15.0
+
+
+# ---------------------------------------------------------------------------
+# build_cell_grid_v2 — hybrid grid builder (current default)
+# ---------------------------------------------------------------------------
+
+def build_cell_grid_v2(
+    ocr_img: np.ndarray,
+    column_regions: List[PageRegion],
+    row_geometries: List[RowGeometry],
+    img_w: int,
+    img_h: int,
+    lang: str = "eng+deu",
+    ocr_engine: str = "auto",
+    img_bgr: Optional[np.ndarray] = None,
+    skip_heal_gaps: bool = False,
+) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
+    """Hybrid Grid: full-page OCR for broad columns, cell-crop for narrow ones.
+
+    Drop-in replacement for build_cell_grid() -- same signature & return type.
+
+    Strategy:
+    - Broad columns (>15% image width): Use pre-assigned full-page Tesseract
+      words (from row.words). Handles IPA brackets, punctuation, sentence
+      continuity correctly.
+    - Narrow columns (<15% image width): Use isolated cell-crop OCR to prevent
+      neighbour bleeding from adjacent broad columns.
+    """
+    engine_name = "tesseract"
+    if ocr_engine in ("trocr-printed", "trocr-handwritten", "lighton"):
+        engine_name = ocr_engine
+    elif ocr_engine == "rapid" and RAPIDOCR_AVAILABLE:
+        engine_name = "rapid"
+
+    logger.info(f"build_cell_grid_v2: using OCR engine '{engine_name}' (hybrid mode)")
+
+    # Filter to content rows only
+    content_rows = [r for r in row_geometries if r.row_type == 'content']
+    if not content_rows:
+        logger.warning("build_cell_grid_v2: no content rows found")
+        return [], []
+
+    # Filter phantom rows (word_count=0) and artifact rows
+    before = len(content_rows)
+    content_rows = [r for r in content_rows if r.word_count > 0]
+    skipped = before - len(content_rows)
+    if skipped > 0:
+        logger.info(f"build_cell_grid_v2: skipped {skipped} phantom rows (word_count=0)")
+    if not content_rows:
+        logger.warning("build_cell_grid_v2: no content rows with words found")
+        return [], []
+
+    before_art = len(content_rows)
+    content_rows = [r for r in content_rows if not _is_artifact_row(r)]
+    artifact_skipped = before_art - len(content_rows)
+    if artifact_skipped > 0:
+        logger.info(f"build_cell_grid_v2: skipped {artifact_skipped} artifact rows")
+    if not content_rows:
+        logger.warning("build_cell_grid_v2: no content rows after artifact filtering")
+        return [], []
+
+    # Filter columns
+    _skip_types = {'column_ignore', 'header', 'footer', 'margin_top',
+                   'margin_bottom', 'margin_left', 'margin_right'}
+    relevant_cols = [c for c in column_regions if c.type not in _skip_types]
+    if not relevant_cols:
+        logger.warning("build_cell_grid_v2: no usable columns found")
+        return [], []
+
+    # Heal row gaps -- use header/footer boundaries
+    content_rows.sort(key=lambda r: r.y)
+    header_rows = [r for r in row_geometries if r.row_type == 'header']
+    footer_rows = [r for r in row_geometries if r.row_type == 'footer']
+    if header_rows:
+        top_bound = max(r.y + r.height for r in header_rows)
+    else:
+        top_bound = content_rows[0].y
+    if footer_rows:
+        bottom_bound = min(r.y for r in footer_rows)
+    else:
+        bottom_bound = content_rows[-1].y + content_rows[-1].height
+
+    # skip_heal_gaps: When True, keep cell positions at their exact row geometry
+    # positions without expanding to fill gaps from removed rows.
+    if not skip_heal_gaps:
+        _heal_row_gaps(content_rows, top_bound=top_bound, bottom_bound=bottom_bound)
+
+    relevant_cols.sort(key=lambda c: c.x)
+
+    columns_meta = [
+        {'index': ci, 'type': c.type, 'x': c.x, 'width': c.width}
+        for ci, c in enumerate(relevant_cols)
+    ]
+
+    lang_map = {
+        'column_en': 'eng',
+        'column_de': 'deu',
+        'column_example': 'eng+deu',
+    }
+
+    # --- Classify columns as broad vs narrow ---
+    narrow_col_indices = set()
+    for ci, col in enumerate(relevant_cols):
+        col_pct = (col.width / img_w * 100) if img_w > 0 else 0
+        if col_pct < _NARROW_COL_THRESHOLD_PCT:
+            narrow_col_indices.add(ci)
+
+    broad_col_count = len(relevant_cols) - len(narrow_col_indices)
+    logger.info(f"build_cell_grid_v2: {broad_col_count} broad columns (full-page), "
+                f"{len(narrow_col_indices)} narrow columns (cell-crop)")
+
+    # --- Phase 1: Broad columns via full-page word assignment ---
+    cells: List[Dict[str, Any]] = []
+
+    for row_idx, row in enumerate(content_rows):
+        # Assign full-page words to columns for this row
+        col_words = _assign_row_words_to_columns(row, relevant_cols)
+
+        for col_idx, col in enumerate(relevant_cols):
+            if col_idx not in narrow_col_indices:
+                # BROAD column: use pre-assigned full-page words
+                words = col_words.get(col_idx, [])
+                # Filter low-confidence words
+                words = [w for w in words if w.get('conf', 0) >= _MIN_WORD_CONF]
+
+                # Single full-width column (box sub-session): preserve spacing
+                is_single_full_column = (
+                    len(relevant_cols) == 1
+                    and img_w > 0
+                    and relevant_cols[0].width / img_w > 0.9
+                )
+
+                if words:
+                    y_tol = max(15, row.height)
+                    if is_single_full_column:
+                        text = _words_to_spaced_text(words, y_tolerance_px=y_tol)
+                        logger.info(f"R{row_idx:02d}: {len(words)} words, "
+                                    f"text={text!r:.100}")
+                    else:
+                        text = _words_to_reading_order_text(words, y_tolerance_px=y_tol)
+                    avg_conf = round(sum(w['conf'] for w in words) / len(words), 1)
+                else:
+                    text = ''
+                    avg_conf = 0.0
+                    if is_single_full_column:
+                        logger.info(f"R{row_idx:02d}: 0 words (row has "
+                                    f"{row.word_count} total, y={row.y}..{row.y+row.height})")
+
+                # Apply noise filter -- but NOT for single-column sub-sessions
+                if not is_single_full_column:
+                    text = _clean_cell_text(text)
+
+                cell = {
+                    'cell_id': f"R{row_idx:02d}_C{col_idx}",
+                    'row_index': row_idx,
+                    'col_index': col_idx,
+                    'col_type': col.type,
+                    'text': text,
+                    'confidence': avg_conf,
+                    'bbox_px': {
+                        'x': col.x, 'y': row.y,
+                        'w': col.width, 'h': row.height,
+                    },
+                    'bbox_pct': {
+                        'x': round(col.x / img_w * 100, 2) if img_w else 0,
+                        'y': round(row.y / img_h * 100, 2) if img_h else 0,
+                        'w': round(col.width / img_w * 100, 2) if img_w else 0,
+                        'h': round(row.height / img_h * 100, 2) if img_h else 0,
+                    },
+                    'ocr_engine': 'word_lookup',
+                    'is_bold': False,
+                }
+                # Store word bounding boxes for pixel-accurate overlay
+                if words and text.strip():
+                    cell['word_boxes'] = [
+                        {
+                            'text': w.get('text', ''),
+                            'left': w['left'],
+                            'top': w['top'],
+                            'width': w['width'],
+                            'height': w['height'],
+                            'conf': w.get('conf', 0),
+                        }
+                        for w in words
+                        if w.get('text', '').strip()
+                    ]
+                cells.append(cell)
+
+    # --- Phase 2: Narrow columns via cell-crop OCR (parallel) ---
+    narrow_tasks = []
+    for row_idx, row in enumerate(content_rows):
+        for col_idx, col in enumerate(relevant_cols):
+            if col_idx in narrow_col_indices:
+                narrow_tasks.append((row_idx, col_idx, row, col))
+
+    if narrow_tasks:
+        max_workers = 4 if engine_name == "tesseract" else 2
+        with ThreadPoolExecutor(max_workers=max_workers) as pool:
+            futures = {
+                pool.submit(
+                    _ocr_cell_crop,
+                    ri, ci, row, col,
+                    ocr_img, img_bgr, img_w, img_h,
+                    engine_name, lang, lang_map,
+                ): (ri, ci)
+                for ri, ci, row, col in narrow_tasks
+            }
+            for future in as_completed(futures):
+                try:
+                    cell = future.result()
+                    cells.append(cell)
+                except Exception as e:
+                    ri, ci = futures[future]
+                    logger.error(f"build_cell_grid_v2: narrow cell R{ri:02d}_C{ci} failed: {e}")
+
+    # Sort cells by (row_index, col_index)
+    cells.sort(key=lambda c: (c['row_index'], c['col_index']))
+
+    # Remove all-empty rows
+    rows_with_text: set = set()
+    for cell in cells:
+        if cell['text'].strip():
+            rows_with_text.add(cell['row_index'])
+    before_filter = len(cells)
+    cells = [c for c in cells if c['row_index'] in rows_with_text]
+    empty_rows_removed = (before_filter - len(cells)) // max(len(relevant_cols), 1)
+    if empty_rows_removed > 0:
+        logger.info(f"build_cell_grid_v2: removed {empty_rows_removed} all-empty rows")
+
+    logger.info(f"build_cell_grid_v2: {len(cells)} cells from "
+                f"{len(content_rows)} rows x {len(relevant_cols)} columns, "
+                f"engine={engine_name} (hybrid)")
+
+    return cells, columns_meta

klausur-service/backend/cv_cell_grid_helpers.py

@@ -0,0 +1,136 @@
+"""
+Shared helpers for cell-grid construction (v2 + legacy).
+
+Extracted from cv_cell_grid.py — used by both cv_cell_grid_build and
+cv_cell_grid_legacy.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+from typing import List
+
+import numpy as np
+
+from cv_vocab_types import RowGeometry
+
+logger = logging.getLogger(__name__)
+
+try:
+    import cv2
+except ImportError:
+    cv2 = None  # type: ignore[assignment]
+
+# Minimum OCR word confidence to keep (used across multiple functions)
+_MIN_WORD_CONF = 30
+
+
+def _compute_cell_padding(col_width: int, img_w: int) -> int:
+    """Adaptive padding for OCR crops based on column width.
+
+    Narrow columns (page_ref, marker) need more surrounding context so
+    Tesseract can segment characters correctly.  Wide columns keep the
+    minimal 4 px padding to avoid pulling in neighbours.
+    """
+    col_pct = col_width / img_w * 100 if img_w > 0 else 100
+    if col_pct < 5:
+        return max(20, col_width // 2)
+    if col_pct < 10:
+        return max(12, col_width // 4)
+    if col_pct < 15:
+        return 8
+    return 4
+
+
+def _ensure_minimum_crop_size(crop: np.ndarray, min_dim: int = 150,
+                               max_scale: int = 3) -> np.ndarray:
+    """Upscale tiny crops so Tesseract gets enough pixel data.
+
+    If either dimension is below *min_dim*, the crop is bicubic-upscaled
+    so the smallest dimension reaches *min_dim* (capped at *max_scale* x).
+    """
+    h, w = crop.shape[:2]
+    if h >= min_dim and w >= min_dim:
+        return crop
+    scale = min(max_scale, max(min_dim / max(h, 1), min_dim / max(w, 1)))
+    if scale <= 1.0:
+        return crop
+    new_w = int(w * scale)
+    new_h = int(h * scale)
+    return cv2.resize(crop, (new_w, new_h), interpolation=cv2.INTER_CUBIC)
+
+
+def _select_psm_for_column(col_type: str, col_width: int,
+                            row_height: int) -> int:
+    """Choose the best Tesseract PSM for a given column geometry.
+
+    - page_ref columns are almost always single short tokens -> PSM 8
+    - Very narrow or short cells -> PSM 7 (single text line)
+    - Everything else -> PSM 6 (uniform block)
+    """
+    if col_type in ('page_ref', 'marker'):
+        return 8  # single word
+    if col_width < 100 or row_height < 30:
+        return 7  # single line
+    return 6  # uniform block
+
+
+def _is_artifact_row(row: RowGeometry) -> bool:
+    """Return True if this row contains only scan artifacts, not real text.
+
+    Artifact rows (scanner shadows, noise) typically produce only single-character
+    detections. A real content row always has at least one token with 2+ characters.
+    """
+    if row.word_count == 0:
+        return True
+    texts = [w.get('text', '').strip() for w in row.words]
+    return all(len(t) <= 1 for t in texts)
+
+
+def _heal_row_gaps(
+    rows: List[RowGeometry],
+    top_bound: int,
+    bottom_bound: int,
+) -> None:
+    """Expand row y/height to fill vertical gaps caused by removed adjacent rows.
+
+    After filtering out empty or artifact rows, remaining content rows may have
+    gaps between them where the removed rows used to be. This function mutates
+    each row to extend upward/downward to the midpoint of such gaps so that
+    OCR crops cover the full available content area.
+
+    The first row always extends to top_bound; the last row to bottom_bound.
+    """
+    if not rows:
+        return
+    rows.sort(key=lambda r: r.y)
+    n = len(rows)
+    orig = [(r.y, r.y + r.height) for r in rows]  # snapshot before mutation
+
+    for i, row in enumerate(rows):
+        # New top: midpoint between previous row's bottom and this row's top
+        if i == 0:
+            new_top = top_bound
+        else:
+            prev_bot = orig[i - 1][1]
+            my_top = orig[i][0]
+            gap = my_top - prev_bot
+            new_top = prev_bot + gap // 2 if gap > 1 else my_top
+
+        # New bottom: midpoint between this row's bottom and next row's top
+        if i == n - 1:
+            new_bottom = bottom_bound
+        else:
+            my_bot = orig[i][1]
+            next_top = orig[i + 1][0]
+            gap = next_top - my_bot
+            new_bottom = my_bot + gap // 2 if gap > 1 else my_bot
+
+        row.y = new_top
+        row.height = max(5, new_bottom - new_top)
+
+    logger.debug(
+        f"_heal_row_gaps: {n} rows -> y range [{rows[0].y}..{rows[-1].y + rows[-1].height}] "
+        f"(bounds: top={top_bound}, bottom={bottom_bound})"
+    )

klausur-service/backend/cv_cell_grid_legacy.py

@@ -0,0 +1,436 @@
+"""
+Legacy cell-grid construction (v1) -- DEPRECATED, kept for backward compat.
+
+Extracted from cv_cell_grid.py. Prefer build_cell_grid_v2 for new code.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+from typing import Any, Dict, List, Optional, Tuple
+
+import numpy as np
+
+from cv_vocab_types import PageRegion, RowGeometry
+from cv_ocr_engines import (
+    RAPIDOCR_AVAILABLE,
+    _assign_row_words_to_columns,
+    _clean_cell_text,
+    _words_to_reading_order_text,
+    ocr_region_lighton,
+    ocr_region_rapid,
+    ocr_region_trocr,
+)
+from cv_cell_grid_helpers import (
+    _MIN_WORD_CONF,
+    _compute_cell_padding,
+    _ensure_minimum_crop_size,
+    _heal_row_gaps,
+    _is_artifact_row,
+    _select_psm_for_column,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# _ocr_single_cell — legacy per-cell OCR with multi-level fallback
+# ---------------------------------------------------------------------------
+
+def _ocr_single_cell(
+    row_idx: int,
+    col_idx: int,
+    row: RowGeometry,
+    col: PageRegion,
+    ocr_img: np.ndarray,
+    img_bgr: Optional[np.ndarray],
+    img_w: int,
+    img_h: int,
+    use_rapid: bool,
+    engine_name: str,
+    lang: str,
+    lang_map: Dict[str, str],
+    preassigned_words: Optional[List[Dict]] = None,
+) -> Dict[str, Any]:
+    """Populate a single cell (column x row intersection) via word lookup."""
+    # Display bbox: exact column x row intersection (no padding)
+    disp_x = col.x
+    disp_y = row.y
+    disp_w = col.width
+    disp_h = row.height
+
+    # OCR crop: adaptive padding -- narrow columns get more context
+    pad = _compute_cell_padding(col.width, img_w)
+    cell_x = max(0, col.x - pad)
+    cell_y = max(0, row.y - pad)
+    cell_w = min(col.width + 2 * pad, img_w - cell_x)
+    cell_h = min(row.height + 2 * pad, img_h - cell_y)
+    is_narrow = (col.width / img_w * 100) < 15 if img_w > 0 else False
+
+    if disp_w <= 0 or disp_h <= 0:
+        return {
+            'cell_id': f"R{row_idx:02d}_C{col_idx}",
+            'row_index': row_idx,
+            'col_index': col_idx,
+            'col_type': col.type,
+            'text': '',
+            'confidence': 0.0,
+            'bbox_px': {'x': col.x, 'y': row.y, 'w': col.width, 'h': row.height},
+            'bbox_pct': {
+                'x': round(col.x / img_w * 100, 2),
+                'y': round(row.y / img_h * 100, 2),
+                'w': round(col.width / img_w * 100, 2),
+                'h': round(row.height / img_h * 100, 2),
+            },
+            'ocr_engine': 'word_lookup',
+        }
+
+    # --- PRIMARY: Word-lookup from full-page Tesseract ---
+    words = preassigned_words if preassigned_words is not None else []
+    used_engine = 'word_lookup'
+
+    # Filter low-confidence words
+    if words:
+        words = [w for w in words if w.get('conf', 0) >= _MIN_WORD_CONF]
+
+    if words:
+        y_tol = max(15, row.height)
+        text = _words_to_reading_order_text(words, y_tolerance_px=y_tol)
+        avg_conf = round(sum(w['conf'] for w in words) / len(words), 1)
+    else:
+        text = ''
+        avg_conf = 0.0
+
+    # --- FALLBACK: Cell-OCR for empty cells ---
+    _run_fallback = False
+    if not text.strip() and cell_w > 0 and cell_h > 0:
+        if ocr_img is not None:
+            crop = ocr_img[cell_y:cell_y + cell_h, cell_x:cell_x + cell_w]
+            if crop.size > 0:
+                dark_ratio = float(np.count_nonzero(crop < 180)) / crop.size
+                _run_fallback = dark_ratio > 0.005
+    if _run_fallback:
+        # For narrow columns, upscale the crop before OCR
+        if is_narrow and ocr_img is not None:
+            _crop_slice = ocr_img[cell_y:cell_y + cell_h, cell_x:cell_x + cell_w]
+            _upscaled = _ensure_minimum_crop_size(_crop_slice)
+            if _upscaled is not _crop_slice:
+                _up_h, _up_w = _upscaled.shape[:2]
+                _tmp_region = PageRegion(
+                    type=col.type, x=0, y=0, width=_up_w, height=_up_h,
+                )
+                _cell_psm = _select_psm_for_column(col.type, col.width, row.height)
+                cell_lang = lang_map.get(col.type, lang)
+                fallback_words = ocr_region(_upscaled, _tmp_region,
+                                            lang=cell_lang, psm=_cell_psm)
+                # Remap word positions back to original image coordinates
+                _sx = cell_w / max(_up_w, 1)
+                _sy = cell_h / max(_up_h, 1)
+                for _fw in (fallback_words or []):
+                    _fw['left'] = int(_fw['left'] * _sx) + cell_x
+                    _fw['top'] = int(_fw['top'] * _sy) + cell_y
+                    _fw['width'] = int(_fw['width'] * _sx)
+                    _fw['height'] = int(_fw['height'] * _sy)
+            else:
+                cell_region = PageRegion(
+                    type=col.type, x=cell_x, y=cell_y,
+                    width=cell_w, height=cell_h,
+                )
+                _cell_psm = _select_psm_for_column(col.type, col.width, row.height)
+                cell_lang = lang_map.get(col.type, lang)
+                fallback_words = ocr_region(ocr_img, cell_region,
+                                            lang=cell_lang, psm=_cell_psm)
+        else:
+            cell_region = PageRegion(
+                type=col.type,
+                x=cell_x, y=cell_y,
+                width=cell_w, height=cell_h,
+            )
+            if engine_name in ("trocr-printed", "trocr-handwritten") and img_bgr is not None:
+                fallback_words = ocr_region_trocr(img_bgr, cell_region, handwritten=(engine_name == "trocr-handwritten"))
+            elif engine_name == "lighton" and img_bgr is not None:
+                fallback_words = ocr_region_lighton(img_bgr, cell_region)
+            elif use_rapid and img_bgr is not None:
+                fallback_words = ocr_region_rapid(img_bgr, cell_region)
+            else:
+                _cell_psm = _select_psm_for_column(col.type, col.width, row.height)
+                cell_lang = lang_map.get(col.type, lang)
+                fallback_words = ocr_region(ocr_img, cell_region,
+                                            lang=cell_lang, psm=_cell_psm)
+
+        if fallback_words:
+            fallback_words = [w for w in fallback_words if w.get('conf', 0) >= _MIN_WORD_CONF]
+        if fallback_words:
+            fb_avg_h = sum(w['height'] for w in fallback_words) / len(fallback_words)
+            fb_y_tol = max(10, int(fb_avg_h * 0.5))
+            fb_text = _words_to_reading_order_text(fallback_words, y_tolerance_px=fb_y_tol)
+            if fb_text.strip():
+                text = fb_text
+                avg_conf = round(
+                    sum(w['conf'] for w in fallback_words) / len(fallback_words), 1
+                )
+                used_engine = 'cell_ocr_fallback'
+
+        # --- SECONDARY FALLBACK: PSM=7 (single line) for still-empty cells ---
+        if not text.strip() and _run_fallback and not use_rapid:
+            _fb_region = PageRegion(
+                type=col.type, x=cell_x, y=cell_y,
+                width=cell_w, height=cell_h,
+            )
+            cell_lang = lang_map.get(col.type, lang)
+            psm7_words = ocr_region(ocr_img, _fb_region, lang=cell_lang, psm=7)
+            if psm7_words:
+                psm7_words = [w for w in psm7_words if w.get('conf', 0) >= _MIN_WORD_CONF]
+            if psm7_words:
+                p7_text = _words_to_reading_order_text(psm7_words, y_tolerance_px=10)
+                if p7_text.strip():
+                    text = p7_text
+                    avg_conf = round(
+                        sum(w['conf'] for w in psm7_words) / len(psm7_words), 1
+                    )
+                    used_engine = 'cell_ocr_psm7'
+
+        # --- TERTIARY FALLBACK: Row-strip re-OCR for narrow columns ---
+        if not text.strip() and is_narrow and img_bgr is not None:
+            row_region = PageRegion(
+                type='_row_strip', x=0, y=row.y,
+                width=img_w, height=row.height,
+            )
+            strip_words = ocr_region_rapid(img_bgr, row_region)
+            if strip_words:
+                col_left = col.x
+                col_right = col.x + col.width
+                col_words = []
+                for sw in strip_words:
+                    sw_left = sw.get('left', 0)
+                    sw_right = sw_left + sw.get('width', 0)
+                    overlap = max(0, min(sw_right, col_right) - max(sw_left, col_left))
+                    if overlap > sw.get('width', 1) * 0.3:
+                        col_words.append(sw)
+                if col_words:
+                    col_words = [w for w in col_words if w.get('conf', 0) >= _MIN_WORD_CONF]
+                if col_words:
+                    rs_text = _words_to_reading_order_text(col_words, y_tolerance_px=row.height)
+                    if rs_text.strip():
+                        text = rs_text
+                        avg_conf = round(
+                            sum(w['conf'] for w in col_words) / len(col_words), 1
+                        )
+                        used_engine = 'row_strip_rapid'
+
+    # --- NOISE FILTER: clear cells that contain only OCR artifacts ---
+    if text.strip():
+        text = _clean_cell_text(text)
+        if not text:
+            avg_conf = 0.0
+
+    return {
+        'cell_id': f"R{row_idx:02d}_C{col_idx}",
+        'row_index': row_idx,
+        'col_index': col_idx,
+        'col_type': col.type,
+        'text': text,
+        'confidence': avg_conf,
+        'bbox_px': {'x': disp_x, 'y': disp_y, 'w': disp_w, 'h': disp_h},
+        'bbox_pct': {
+            'x': round(disp_x / img_w * 100, 2),
+            'y': round(disp_y / img_h * 100, 2),
+            'w': round(disp_w / img_w * 100, 2),
+            'h': round(disp_h / img_h * 100, 2),
+        },
+        'ocr_engine': used_engine,
+    }
+
+
+# ---------------------------------------------------------------------------
+# build_cell_grid — legacy grid builder (DEPRECATED)
+# ---------------------------------------------------------------------------
+
+def build_cell_grid(
+    ocr_img: np.ndarray,
+    column_regions: List[PageRegion],
+    row_geometries: List[RowGeometry],
+    img_w: int,
+    img_h: int,
+    lang: str = "eng+deu",
+    ocr_engine: str = "auto",
+    img_bgr: Optional[np.ndarray] = None,
+) -> Tuple[List[Dict[str, Any]], List[Dict[str, Any]]]:
+    """Generic Cell-Grid: Columns x Rows -> cells with OCR text.
+
+    DEPRECATED: Use build_cell_grid_v2 instead.
+    """
+    # Resolve engine choice
+    use_rapid = False
+    if ocr_engine in ("trocr-printed", "trocr-handwritten", "lighton"):
+        engine_name = ocr_engine
+    elif ocr_engine == "auto":
+        use_rapid = RAPIDOCR_AVAILABLE and img_bgr is not None
+        engine_name = "rapid" if use_rapid else "tesseract"
+    elif ocr_engine == "rapid":
+        if not RAPIDOCR_AVAILABLE:
+            logger.warning("RapidOCR requested but not available, falling back to Tesseract")
+        else:
+            use_rapid = True
+        engine_name = "rapid" if use_rapid else "tesseract"
+    else:
+        engine_name = "tesseract"
+
+    logger.info(f"build_cell_grid: using OCR engine '{engine_name}'")
+
+    # Filter to content rows only (skip header/footer)
+    content_rows = [r for r in row_geometries if r.row_type == 'content']
+    if not content_rows:
+        logger.warning("build_cell_grid: no content rows found")
+        return [], []
+
+    before = len(content_rows)
+    content_rows = [r for r in content_rows if r.word_count > 0]
+    skipped = before - len(content_rows)
+    if skipped > 0:
+        logger.info(f"build_cell_grid: skipped {skipped} phantom rows (word_count=0)")
+    if not content_rows:
+        logger.warning("build_cell_grid: no content rows with words found")
+        return [], []
+
+    _skip_types = {'column_ignore', 'header', 'footer', 'margin_top', 'margin_bottom', 'margin_left', 'margin_right'}
+    relevant_cols = [c for c in column_regions if c.type not in _skip_types]
+    if not relevant_cols:
+        logger.warning("build_cell_grid: no usable columns found")
+        return [], []
+
+    before_art = len(content_rows)
+    content_rows = [r for r in content_rows if not _is_artifact_row(r)]
+    artifact_skipped = before_art - len(content_rows)
+    if artifact_skipped > 0:
+        logger.info(f"build_cell_grid: skipped {artifact_skipped} artifact rows (all single-char words)")
+    if not content_rows:
+        logger.warning("build_cell_grid: no content rows after artifact filtering")
+        return [], []
+
+    _heal_row_gaps(
+        content_rows,
+        top_bound=min(c.y for c in relevant_cols),
+        bottom_bound=max(c.y + c.height for c in relevant_cols),
+    )
+
+    relevant_cols.sort(key=lambda c: c.x)
+
+    columns_meta = [
+        {
+            'index': col_idx,
+            'type': col.type,
+            'x': col.x,
+            'width': col.width,
+        }
+        for col_idx, col in enumerate(relevant_cols)
+    ]
+
+    lang_map = {
+        'column_en': 'eng',
+        'column_de': 'deu',
+        'column_example': 'eng+deu',
+    }
+
+    cells: List[Dict[str, Any]] = []
+
+    for row_idx, row in enumerate(content_rows):
+        col_words = _assign_row_words_to_columns(row, relevant_cols)
+        for col_idx, col in enumerate(relevant_cols):
+            cell = _ocr_single_cell(
+                row_idx, col_idx, row, col,
+                ocr_img, img_bgr, img_w, img_h,
+                use_rapid, engine_name, lang, lang_map,
+                preassigned_words=col_words[col_idx],
+            )
+            cells.append(cell)
+
+    # --- BATCH FALLBACK: re-OCR empty cells by column strip ---
+    empty_by_col: Dict[int, List[int]] = {}
+    for ci, cell in enumerate(cells):
+        if not cell['text'].strip() and cell.get('ocr_engine') != 'cell_ocr_psm7':
+            bpx = cell['bbox_px']
+            x, y, w, h = bpx['x'], bpx['y'], bpx['w'], bpx['h']
+            if w > 0 and h > 0 and ocr_img is not None:
+                crop = ocr_img[y:y + h, x:x + w]
+                if crop.size > 0:
+                    dark_ratio = float(np.count_nonzero(crop < 180)) / crop.size
+                    if dark_ratio > 0.005:
+                        empty_by_col.setdefault(cell['col_index'], []).append(ci)
+
+    for col_idx, cell_indices in empty_by_col.items():
+        if len(cell_indices) < 3:
+            continue
+
+        min_y = min(cells[ci]['bbox_px']['y'] for ci in cell_indices)
+        max_y_h = max(cells[ci]['bbox_px']['y'] + cells[ci]['bbox_px']['h'] for ci in cell_indices)
+        col_x = cells[cell_indices[0]]['bbox_px']['x']
+        col_w = cells[cell_indices[0]]['bbox_px']['w']
+
+        strip_region = PageRegion(
+            type=relevant_cols[col_idx].type,
+            x=col_x, y=min_y,
+            width=col_w, height=max_y_h - min_y,
+        )
+        strip_lang = lang_map.get(relevant_cols[col_idx].type, lang)
+
+        if engine_name in ("trocr-printed", "trocr-handwritten") and img_bgr is not None:
+            strip_words = ocr_region_trocr(img_bgr, strip_region, handwritten=(engine_name == "trocr-handwritten"))
+        elif engine_name == "lighton" and img_bgr is not None:
+            strip_words = ocr_region_lighton(img_bgr, strip_region)
+        elif use_rapid and img_bgr is not None:
+            strip_words = ocr_region_rapid(img_bgr, strip_region)
+        else:
+            strip_words = ocr_region(ocr_img, strip_region, lang=strip_lang, psm=6)
+
+        if not strip_words:
+            continue
+
+        strip_words = [w for w in strip_words if w.get('conf', 0) >= 30]
+        if not strip_words:
+            continue
+
+        for ci in cell_indices:
+            cell_y = cells[ci]['bbox_px']['y']
+            cell_h = cells[ci]['bbox_px']['h']
+            cell_mid_y = cell_y + cell_h / 2
+
+            matched_words = [
+                w for w in strip_words
+                if abs((w['top'] + w['height'] / 2) - cell_mid_y) < cell_h * 0.8
+            ]
+            if matched_words:
+                matched_words.sort(key=lambda w: w['left'])
+                batch_text = ' '.join(w['text'] for w in matched_words)
+                batch_text = _clean_cell_text(batch_text)
+                if batch_text.strip():
+                    cells[ci]['text'] = batch_text
+                    cells[ci]['confidence'] = round(
+                        sum(w['conf'] for w in matched_words) / len(matched_words), 1
+                    )
+                    cells[ci]['ocr_engine'] = 'batch_column_ocr'
+
+        batch_filled = sum(1 for ci in cell_indices if cells[ci]['text'].strip())
+        if batch_filled > 0:
+            logger.info(
+                f"build_cell_grid: batch OCR filled {batch_filled}/{len(cell_indices)} "
+                f"empty cells in column {col_idx}"
+            )
+
+    # Remove all-empty rows
+    rows_with_text: set = set()
+    for cell in cells:
+        if cell['text'].strip():
+            rows_with_text.add(cell['row_index'])
+    before_filter = len(cells)
+    cells = [c for c in cells if c['row_index'] in rows_with_text]
+    empty_rows_removed = (before_filter - len(cells)) // max(len(relevant_cols), 1)
+    if empty_rows_removed > 0:
+        logger.info(f"build_cell_grid: removed {empty_rows_removed} all-empty rows after OCR")
+
+    logger.info(f"build_cell_grid: {len(cells)} cells from "
+                f"{len(content_rows)} rows x {len(relevant_cols)} columns, "
+                f"engine={engine_name}")
+
+    return cells, columns_meta

klausur-service/backend/cv_cell_grid_merge.py

@@ -0,0 +1,235 @@
+"""
+Row-merging logic for vocabulary entries (phonetic, wrapped, continuation rows).
+
+Extracted from cv_cell_grid.py.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List
+
+from cv_ocr_engines import _RE_ALPHA
+
+logger = logging.getLogger(__name__)
+
+# Regex: line starts with phonetic bracket content only (no real word before it)
+_PHONETIC_ONLY_RE = re.compile(
+    r'''^\s*[\[\('"]*[^\]]*[\])\s]*$'''
+)
+
+
+def _is_phonetic_only_text(text: str) -> bool:
+    """Check if text consists only of phonetic transcription.
+
+    Phonetic-only patterns:
+      ['mani serva]   ->  True
+      [dance]         ->  True
+      ["a:mand]       ->  True
+      almond ['a:mand] -> False (has real word before bracket)
+      Mandel           -> False
+    """
+    t = text.strip()
+    if not t:
+        return False
+    # Must contain at least one bracket
+    if '[' not in t and ']' not in t:
+        return False
+    # Remove all bracket content and surrounding punctuation/whitespace
+    without_brackets = re.sub(r"\[.*?\]", '', t)
+    without_brackets = re.sub(r"[\[\]'\"()\s]", '', without_brackets)
+    # If nothing meaningful remains, it's phonetic-only
+    alpha_remaining = ''.join(_RE_ALPHA.findall(without_brackets))
+    return len(alpha_remaining) < 2
+
+
+def _merge_phonetic_continuation_rows(
+    entries: List[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    """Merge rows that contain only phonetic transcription into previous entry.
+
+    In dictionary pages, phonetic transcription sometimes wraps to the next
+    row.  E.g.:
+      Row 28: EN="it's a money-saver"  DE="es spart Kosten"
+      Row 29: EN="['mani serva]"       DE=""
+
+    Row 29 is phonetic-only -> merge into row 28's EN field.
+    """
+    if len(entries) < 2:
+        return entries
+
+    merged: List[Dict[str, Any]] = []
+    for entry in entries:
+        en = (entry.get('english') or '').strip()
+        de = (entry.get('german') or '').strip()
+        ex = (entry.get('example') or '').strip()
+
+        # Check if this entry is phonetic-only (EN has only phonetics, DE empty)
+        if merged and _is_phonetic_only_text(en) and not de:
+            prev = merged[-1]
+            prev_en = (prev.get('english') or '').strip()
+            # Append phonetic to previous entry's EN
+            if prev_en:
+                prev['english'] = prev_en + ' ' + en
+            else:
+                prev['english'] = en
+            # If there was an example, append to previous too
+            if ex:
+                prev_ex = (prev.get('example') or '').strip()
+                prev['example'] = (prev_ex + ' ' + ex).strip() if prev_ex else ex
+            logger.debug(
+                f"Merged phonetic row {entry.get('row_index')} "
+                f"into previous entry: {prev['english']!r}"
+            )
+            continue
+
+        merged.append(entry)
+
+    return merged
+
+
+def _merge_wrapped_rows(
+    entries: List[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    """Merge rows where the primary column (EN) is empty -- cell wrap continuation.
+
+    In textbook vocabulary tables, columns are often narrow, so the author
+    wraps text within a cell. OCR treats each physical line as a separate row.
+    The key indicator: if the EN column is empty but DE/example have text,
+    this row is a continuation of the previous row's cells.
+
+    Example (original textbook has ONE row):
+      Row 2: EN="take part (in)"  DE="teilnehmen (an), mitmachen"  EX="More than 200 singers took"
+      Row 3: EN=""                DE="(bei)"                        EX="part in the concert."
+      -> Merged: EN="take part (in)" DE="teilnehmen (an), mitmachen (bei)" EX="..."
+
+    Also handles the reverse case: DE empty but EN has text (wrap in EN column).
+    """
+    if len(entries) < 2:
+        return entries
+
+    merged: List[Dict[str, Any]] = []
+    for entry in entries:
+        en = (entry.get('english') or '').strip()
+        de = (entry.get('german') or '').strip()
+        ex = (entry.get('example') or '').strip()
+
+        if not merged:
+            merged.append(entry)
+            continue
+
+        prev = merged[-1]
+        prev_en = (prev.get('english') or '').strip()
+        prev_de = (prev.get('german') or '').strip()
+        prev_ex = (prev.get('example') or '').strip()
+
+        # Case 1: EN is empty -> continuation of previous row
+        if not en and (de or ex) and prev_en:
+            if de:
+                if prev_de.endswith(','):
+                    sep = ' '
+                elif prev_de.endswith(('-', '(')):
+                    sep = ''
+                else:
+                    sep = ' '
+                prev['german'] = (prev_de + sep + de).strip()
+            if ex:
+                sep = ' ' if prev_ex else ''
+                prev['example'] = (prev_ex + sep + ex).strip()
+            logger.debug(
+                f"Merged wrapped row {entry.get('row_index')} into previous "
+                f"(empty EN): DE={prev['german']!r}, EX={prev.get('example', '')!r}"
+            )
+            continue
+
+        # Case 2: DE is empty, EN has text that looks like continuation
+        if en and not de and prev_de:
+            is_paren = en.startswith('(')
+            first_alpha = next((c for c in en if c.isalpha()), '')
+            starts_lower = first_alpha and first_alpha.islower()
+
+            if (is_paren or starts_lower) and len(en.split()) < 5:
+                sep = ' ' if prev_en and not prev_en.endswith((',', '-', '(')) else ''
+                prev['english'] = (prev_en + sep + en).strip()
+                if ex:
+                    sep2 = ' ' if prev_ex else ''
+                    prev['example'] = (prev_ex + sep2 + ex).strip()
+                logger.debug(
+                    f"Merged wrapped row {entry.get('row_index')} into previous "
+                    f"(empty DE): EN={prev['english']!r}"
+                )
+                continue
+
+        merged.append(entry)
+
+    if len(merged) < len(entries):
+        logger.info(
+            f"_merge_wrapped_rows: merged {len(entries) - len(merged)} "
+            f"continuation rows ({len(entries)} -> {len(merged)})"
+        )
+    return merged
+
+
+def _merge_continuation_rows(
+    entries: List[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    """Merge multi-line vocabulary entries where text wraps to the next row.
+
+    A row is a continuation of the previous entry when:
+    - EN has text, but DE is empty
+    - EN starts with a lowercase letter (not a new vocab entry)
+    - Previous entry's EN does NOT end with a sentence terminator (.!?)
+    - The continuation text has fewer than 4 words (not an example sentence)
+    - The row was not already merged as phonetic
+
+    Example:
+      Row 5: EN="to put up"       DE="aufstellen"
+      Row 6: EN="with sth."       DE=""
+      -> Merged: EN="to put up with sth."  DE="aufstellen"
+    """
+    if len(entries) < 2:
+        return entries
+
+    merged: List[Dict[str, Any]] = []
+    for entry in entries:
+        en = (entry.get('english') or '').strip()
+        de = (entry.get('german') or '').strip()
+
+        if merged and en and not de:
+            # Check: not phonetic (already handled)
+            if _is_phonetic_only_text(en):
+                merged.append(entry)
+                continue
+
+            # Check: starts with lowercase
+            first_alpha = next((c for c in en if c.isalpha()), '')
+            starts_lower = first_alpha and first_alpha.islower()
+
+            # Check: fewer than 4 words (not an example sentence)
+            word_count = len(en.split())
+            is_short = word_count < 4
+
+            # Check: previous entry doesn't end with sentence terminator
+            prev = merged[-1]
+            prev_en = (prev.get('english') or '').strip()
+            prev_ends_sentence = prev_en and prev_en[-1] in '.!?'
+
+            if starts_lower and is_short and not prev_ends_sentence:
+                # Merge into previous entry
+                prev['english'] = (prev_en + ' ' + en).strip()
+                # Merge example if present
+                ex = (entry.get('example') or '').strip()
+                if ex:
+                    prev_ex = (prev.get('example') or '').strip()
+                    prev['example'] = (prev_ex + ' ' + ex).strip() if prev_ex else ex
+                logger.debug(
+                    f"Merged continuation row {entry.get('row_index')} "
+                    f"into previous entry: {prev['english']!r}"
+                )
+                continue
+
+        merged.append(entry)
+
+    return merged

klausur-service/backend/cv_cell_grid_streaming.py

@@ -0,0 +1,217 @@
+"""
+Streaming variants of cell-grid builders (v2 + legacy).
+
+Extracted from cv_cell_grid.py. These yield cells one-by-one as OCR'd,
+useful for progress reporting.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+from typing import Any, Dict, Generator, List, Optional, Tuple
+
+import numpy as np
+
+from cv_vocab_types import PageRegion, RowGeometry
+from cv_ocr_engines import (
+    RAPIDOCR_AVAILABLE,
+    _assign_row_words_to_columns,
+)
+from cv_cell_grid_helpers import (
+    _heal_row_gaps,
+    _is_artifact_row,
+)
+from cv_cell_grid_build import _ocr_cell_crop
+from cv_cell_grid_legacy import _ocr_single_cell
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# build_cell_grid_v2_streaming
+# ---------------------------------------------------------------------------
+
+def build_cell_grid_v2_streaming(
+    ocr_img: np.ndarray,
+    column_regions: List[PageRegion],
+    row_geometries: List[RowGeometry],
+    img_w: int,
+    img_h: int,
+    lang: str = "eng+deu",
+    ocr_engine: str = "auto",
+    img_bgr: Optional[np.ndarray] = None,
+) -> Generator[Tuple[Dict[str, Any], List[Dict[str, Any]], int], None, None]:
+    """Streaming variant of build_cell_grid_v2 -- yields each cell as OCR'd.
+
+    Yields:
+        (cell_dict, columns_meta, total_cells)
+    """
+    use_rapid = False
+    if ocr_engine in ("trocr-printed", "trocr-handwritten", "lighton"):
+        engine_name = ocr_engine
+    elif ocr_engine == "auto":
+        engine_name = "tesseract"
+    elif ocr_engine == "rapid":
+        if not RAPIDOCR_AVAILABLE:
+            logger.warning("RapidOCR requested but not available, falling back to Tesseract")
+        else:
+            use_rapid = True
+        engine_name = "rapid" if use_rapid else "tesseract"
+    else:
+        engine_name = "tesseract"
+
+    content_rows = [r for r in row_geometries if r.row_type == 'content']
+    if not content_rows:
+        return
+
+    content_rows = [r for r in content_rows if r.word_count > 0]
+    if not content_rows:
+        return
+
+    _skip_types = {'column_ignore', 'header', 'footer', 'margin_top',
+                   'margin_bottom', 'margin_left', 'margin_right'}
+    relevant_cols = [c for c in column_regions if c.type not in _skip_types]
+    if not relevant_cols:
+        return
+
+    content_rows = [r for r in content_rows if not _is_artifact_row(r)]
+    if not content_rows:
+        return
+
+    # Use header/footer boundaries for heal_row_gaps
+    content_rows.sort(key=lambda r: r.y)
+    header_rows = [r for r in row_geometries if r.row_type == 'header']
+    footer_rows = [r for r in row_geometries if r.row_type == 'footer']
+    if header_rows:
+        top_bound = max(r.y + r.height for r in header_rows)
+    else:
+        top_bound = content_rows[0].y
+    if footer_rows:
+        bottom_bound = min(r.y for r in footer_rows)
+    else:
+        bottom_bound = content_rows[-1].y + content_rows[-1].height
+
+    _heal_row_gaps(content_rows, top_bound=top_bound, bottom_bound=bottom_bound)
+
+    relevant_cols.sort(key=lambda c: c.x)
+
+    columns_meta = [
+        {'index': ci, 'type': c.type, 'x': c.x, 'width': c.width}
+        for ci, c in enumerate(relevant_cols)
+    ]
+
+    lang_map = {
+        'column_en': 'eng',
+        'column_de': 'deu',
+        'column_example': 'eng+deu',
+    }
+
+    total_cells = len(content_rows) * len(relevant_cols)
+
+    for row_idx, row in enumerate(content_rows):
+        for col_idx, col in enumerate(relevant_cols):
+            cell = _ocr_cell_crop(
+                row_idx, col_idx, row, col,
+                ocr_img, img_bgr, img_w, img_h,
+                engine_name, lang, lang_map,
+            )
+            yield cell, columns_meta, total_cells
+
+
+# ---------------------------------------------------------------------------
+# build_cell_grid_streaming — legacy streaming variant
+# ---------------------------------------------------------------------------
+
+def build_cell_grid_streaming(
+    ocr_img: np.ndarray,
+    column_regions: List[PageRegion],
+    row_geometries: List[RowGeometry],
+    img_w: int,
+    img_h: int,
+    lang: str = "eng+deu",
+    ocr_engine: str = "auto",
+    img_bgr: Optional[np.ndarray] = None,
+) -> Generator[Tuple[Dict[str, Any], List[Dict[str, Any]], int], None, None]:
+    """Like build_cell_grid(), but yields each cell as it is OCR'd.
+
+    DEPRECATED: Use build_cell_grid_v2_streaming instead.
+
+    Yields:
+        (cell_dict, columns_meta, total_cells) for each cell.
+    """
+    use_rapid = False
+    if ocr_engine in ("trocr-printed", "trocr-handwritten", "lighton"):
+        engine_name = ocr_engine
+    elif ocr_engine == "auto":
+        use_rapid = RAPIDOCR_AVAILABLE and img_bgr is not None
+        engine_name = "rapid" if use_rapid else "tesseract"
+    elif ocr_engine == "rapid":
+        if not RAPIDOCR_AVAILABLE:
+            logger.warning("RapidOCR requested but not available, falling back to Tesseract")
+        else:
+            use_rapid = True
+        engine_name = "rapid" if use_rapid else "tesseract"
+    else:
+        engine_name = "tesseract"
+
+    content_rows = [r for r in row_geometries if r.row_type == 'content']
+    if not content_rows:
+        return
+
+    before = len(content_rows)
+    content_rows = [r for r in content_rows if r.word_count > 0]
+    skipped = before - len(content_rows)
+    if skipped > 0:
+        logger.info(f"build_cell_grid_streaming: skipped {skipped} phantom rows (word_count=0)")
+    if not content_rows:
+        return
+
+    _skip_types = {'column_ignore', 'header', 'footer', 'margin_top', 'margin_bottom', 'margin_left', 'margin_right'}
+    relevant_cols = [c for c in column_regions if c.type not in _skip_types]
+    if not relevant_cols:
+        return
+
+    before_art = len(content_rows)
+    content_rows = [r for r in content_rows if not _is_artifact_row(r)]
+    artifact_skipped = before_art - len(content_rows)
+    if artifact_skipped > 0:
+        logger.info(f"build_cell_grid_streaming: skipped {artifact_skipped} artifact rows")
+    if not content_rows:
+        return
+    _heal_row_gaps(
+        content_rows,
+        top_bound=min(c.y for c in relevant_cols),
+        bottom_bound=max(c.y + c.height for c in relevant_cols),
+    )
+
+    relevant_cols.sort(key=lambda c: c.x)
+
+    columns_meta = [
+        {
+            'index': col_idx,
+            'type': col.type,
+            'x': col.x,
+            'width': col.width,
+        }
+        for col_idx, col in enumerate(relevant_cols)
+    ]
+
+    lang_map = {
+        'column_en': 'eng',
+        'column_de': 'deu',
+        'column_example': 'eng+deu',
+    }
+
+    total_cells = len(content_rows) * len(relevant_cols)
+
+    for row_idx, row in enumerate(content_rows):
+        col_words = _assign_row_words_to_columns(row, relevant_cols)
+        for col_idx, col in enumerate(relevant_cols):
+            cell = _ocr_single_cell(
+                row_idx, col_idx, row, col,
+                ocr_img, img_bgr, img_w, img_h,
+                use_rapid, engine_name, lang, lang_map,
+                preassigned_words=col_words[col_idx],
+            )
+            yield cell, columns_meta, total_cells

klausur-service/backend/cv_cell_grid_vocab.py

@@ -0,0 +1,200 @@
+"""
+Vocabulary extraction: cells -> vocab entries, and build_word_grid wrapper.
+
+Extracted from cv_cell_grid.py.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+from typing import Any, Dict, List
+
+from cv_ocr_engines import (
+    _attach_example_sentences,
+    _fix_phonetic_brackets,
+    _split_comma_entries,
+)
+from cv_cell_grid_legacy import build_cell_grid
+from cv_cell_grid_merge import (
+    _merge_continuation_rows,
+    _merge_phonetic_continuation_rows,
+    _merge_wrapped_rows,
+)
+
+logger = logging.getLogger(__name__)
+
+
+def _cells_to_vocab_entries(
+    cells: List[Dict[str, Any]],
+    columns_meta: List[Dict[str, Any]],
+) -> List[Dict[str, Any]]:
+    """Map generic cells to vocab entries with english/german/example fields.
+
+    Groups cells by row_index, maps col_type -> field name, and produces
+    one entry per row (only rows with at least one non-empty field).
+    """
+    col_type_to_field = {
+        'column_en': 'english',
+        'column_de': 'german',
+        'column_example': 'example',
+        'page_ref': 'source_page',
+        'column_marker': 'marker',
+        'column_text': 'text',  # generic single-column (box sub-sessions)
+    }
+    bbox_key_map = {
+        'column_en': 'bbox_en',
+        'column_de': 'bbox_de',
+        'column_example': 'bbox_ex',
+        'page_ref': 'bbox_ref',
+        'column_marker': 'bbox_marker',
+        'column_text': 'bbox_text',
+    }
+
+    # Group cells by row_index
+    rows: Dict[int, List[Dict]] = {}
+    for cell in cells:
+        ri = cell['row_index']
+        rows.setdefault(ri, []).append(cell)
+
+    entries: List[Dict[str, Any]] = []
+    for row_idx in sorted(rows.keys()):
+        row_cells = rows[row_idx]
+        entry: Dict[str, Any] = {
+            'row_index': row_idx,
+            'english': '',
+            'german': '',
+            'example': '',
+            'text': '',  # generic single-column (box sub-sessions)
+            'source_page': '',
+            'marker': '',
+            'confidence': 0.0,
+            'bbox': None,
+            'bbox_en': None,
+            'bbox_de': None,
+            'bbox_ex': None,
+            'bbox_ref': None,
+            'bbox_marker': None,
+            'bbox_text': None,
+            'ocr_engine': row_cells[0].get('ocr_engine', '') if row_cells else '',
+        }
+
+        confidences = []
+        for cell in row_cells:
+            col_type = cell['col_type']
+            field = col_type_to_field.get(col_type)
+            if field:
+                entry[field] = cell['text']
+            bbox_field = bbox_key_map.get(col_type)
+            if bbox_field:
+                entry[bbox_field] = cell['bbox_pct']
+            if cell['confidence'] > 0:
+                confidences.append(cell['confidence'])
+
+        # Compute row-level bbox as union of all cell bboxes
+        all_bboxes = [c['bbox_pct'] for c in row_cells if c.get('bbox_pct')]
+        if all_bboxes:
+            min_x = min(b['x'] for b in all_bboxes)
+            min_y = min(b['y'] for b in all_bboxes)
+            max_x2 = max(b['x'] + b['w'] for b in all_bboxes)
+            max_y2 = max(b['y'] + b['h'] for b in all_bboxes)
+            entry['bbox'] = {
+                'x': round(min_x, 2),
+                'y': round(min_y, 2),
+                'w': round(max_x2 - min_x, 2),
+                'h': round(max_y2 - min_y, 2),
+            }
+
+        entry['confidence'] = round(
+            sum(confidences) / len(confidences), 1
+        ) if confidences else 0.0
+
+        # Only include if at least one mapped field has text
+        has_content = any(
+            entry.get(f)
+            for f in col_type_to_field.values()
+        )
+        if has_content:
+            entries.append(entry)
+
+    return entries
+
+
+def build_word_grid(
+    ocr_img,
+    column_regions,
+    row_geometries,
+    img_w: int,
+    img_h: int,
+    lang: str = "eng+deu",
+    ocr_engine: str = "auto",
+    img_bgr=None,
+    pronunciation: str = "british",
+) -> List[Dict[str, Any]]:
+    """Vocab-specific: Cell-Grid + Vocab-Mapping + Post-Processing.
+
+    Wrapper around build_cell_grid() that adds vocabulary-specific logic:
+    - Maps cells to english/german/example entries
+    - Applies character confusion fixes, IPA lookup, comma splitting, etc.
+    - Falls back to returning raw cells if no vocab columns detected.
+
+    Args:
+        ocr_img: Binarized full-page image (for Tesseract).
+        column_regions: Classified columns from Step 3.
+        row_geometries: Rows from Step 4.
+        img_w, img_h: Image dimensions.
+        lang: Default Tesseract language.
+        ocr_engine: 'tesseract', 'rapid', or 'auto'.
+        img_bgr: BGR color image (required for RapidOCR).
+        pronunciation: 'british' or 'american' for IPA lookup.
+
+    Returns:
+        List of entry dicts with english/german/example text and bbox info (percent).
+    """
+    cells, columns_meta = build_cell_grid(
+        ocr_img, column_regions, row_geometries, img_w, img_h,
+        lang=lang, ocr_engine=ocr_engine, img_bgr=img_bgr,
+    )
+
+    if not cells:
+        return []
+
+    # Check if vocab layout is present
+    col_types = {c['type'] for c in columns_meta}
+    if not (col_types & {'column_en', 'column_de'}):
+        logger.info("build_word_grid: no vocab columns -- returning raw cells")
+        return cells
+
+    # Vocab mapping: cells -> entries
+    entries = _cells_to_vocab_entries(cells, columns_meta)
+
+    # --- Post-processing pipeline (deterministic, no LLM) ---
+    n_raw = len(entries)
+
+    # 0. Merge cell-wrap continuation rows (empty primary column = text wrap)
+    entries = _merge_wrapped_rows(entries)
+
+    # 0a. Merge phonetic-only continuation rows into previous entry
+    entries = _merge_phonetic_continuation_rows(entries)
+
+    # 0b. Merge multi-line continuation rows (lowercase EN, empty DE)
+    entries = _merge_continuation_rows(entries)
+
+    # 1. Character confusion (| -> I, 1 -> I, 8 -> B) is now run in
+    #    llm_review_entries_streaming so changes are visible to the user in Step 6.
+
+    # 2. Replace OCR'd phonetics with dictionary IPA
+    entries = _fix_phonetic_brackets(entries, pronunciation=pronunciation)
+
+    # 3. Split comma-separated word forms (break, broke, broken -> 3 entries)
+    entries = _split_comma_entries(entries)
+
+    # 4. Attach example sentences (rows without DE -> examples for preceding entry)
+    entries = _attach_example_sentences(entries)
+
+    engine_name = cells[0].get('ocr_engine', 'unknown') if cells else 'unknown'
+    logger.info(f"build_word_grid: {len(entries)} entries from "
+                f"{n_raw} raw -> {len(entries)} after post-processing "
+                f"(engine={engine_name})")
+
+    return entries

klausur-service/backend/cv_preprocessing_deskew.py

@@ -0,0 +1,437 @@
+"""
+CV Preprocessing Deskew — Rotation correction via Hough lines, word alignment, and iterative projection.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+from collections import defaultdict
+from typing import Any, Dict, Tuple
+
+import numpy as np
+
+from cv_vocab_types import (
+    CV2_AVAILABLE,
+    TESSERACT_AVAILABLE,
+)
+
+logger = logging.getLogger(__name__)
+
+try:
+    import cv2
+except ImportError:
+    cv2 = None  # type: ignore[assignment]
+
+try:
+    import pytesseract
+    from PIL import Image
+except ImportError:
+    pytesseract = None  # type: ignore[assignment]
+    Image = None  # type: ignore[assignment,misc]
+
+
+# =============================================================================
+# Deskew via Hough Lines
+# =============================================================================
+
+def deskew_image(img: np.ndarray) -> Tuple[np.ndarray, float]:
+    """Correct rotation using Hough Line detection.
+
+    Args:
+        img: BGR image.
+
+    Returns:
+        Tuple of (corrected image, detected angle in degrees).
+    """
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+    lines = cv2.HoughLinesP(binary, 1, np.pi / 180, threshold=100,
+                            minLineLength=img.shape[1] // 4, maxLineGap=20)
+
+    if lines is None or len(lines) < 3:
+        return img, 0.0
+
+    angles = []
+    for line in lines:
+        x1, y1, x2, y2 = line[0]
+        angle = np.degrees(np.arctan2(y2 - y1, x2 - x1))
+        if abs(angle) < 15:
+            angles.append(angle)
+
+    if not angles:
+        return img, 0.0
+
+    median_angle = float(np.median(angles))
+
+    if abs(median_angle) > 5.0:
+        median_angle = 5.0 * np.sign(median_angle)
+
+    if abs(median_angle) < 0.1:
+        return img, 0.0
+
+    h, w = img.shape[:2]
+    center = (w // 2, h // 2)
+    M = cv2.getRotationMatrix2D(center, median_angle, 1.0)
+    corrected = cv2.warpAffine(img, M, (w, h),
+                               flags=cv2.INTER_LINEAR,
+                               borderMode=cv2.BORDER_REPLICATE)
+
+    logger.info(f"Deskew: corrected {median_angle:.2f}\u00b0 rotation")
+    return corrected, median_angle
+
+
+# =============================================================================
+# Deskew via Word Alignment
+# =============================================================================
+
+def deskew_image_by_word_alignment(
+    image_data: bytes,
+    lang: str = "eng+deu",
+    downscale_factor: float = 0.5,
+) -> Tuple[bytes, float]:
+    """Correct rotation by fitting a line through left-most word starts per text line.
+
+    More robust than Hough-based deskew for vocabulary worksheets where text lines
+    have consistent left-alignment.
+
+    Args:
+        image_data: Raw image bytes (PNG/JPEG).
+        lang: Tesseract language string for the quick pass.
+        downscale_factor: Shrink factor for the quick Tesseract pass (0.5 = 50%).
+
+    Returns:
+        Tuple of (rotated image as PNG bytes, detected angle in degrees).
+    """
+    if not CV2_AVAILABLE or not TESSERACT_AVAILABLE:
+        return image_data, 0.0
+
+    img_array = np.frombuffer(image_data, dtype=np.uint8)
+    img = cv2.imdecode(img_array, cv2.IMREAD_COLOR)
+    if img is None:
+        logger.warning("deskew_by_word_alignment: could not decode image")
+        return image_data, 0.0
+
+    orig_h, orig_w = img.shape[:2]
+
+    small_w = int(orig_w * downscale_factor)
+    small_h = int(orig_h * downscale_factor)
+    small = cv2.resize(img, (small_w, small_h), interpolation=cv2.INTER_AREA)
+
+    pil_small = Image.fromarray(cv2.cvtColor(small, cv2.COLOR_BGR2RGB))
+    try:
+        data = pytesseract.image_to_data(
+            pil_small, lang=lang, config="--psm 6 --oem 3",
+            output_type=pytesseract.Output.DICT,
+        )
+    except Exception as e:
+        logger.warning(f"deskew_by_word_alignment: Tesseract failed: {e}")
+        return image_data, 0.0
+
+    line_groups: Dict[tuple, list] = defaultdict(list)
+    for i in range(len(data["text"])):
+        text = (data["text"][i] or "").strip()
+        conf = int(data["conf"][i])
+        if not text or conf < 20:
+            continue
+        key = (data["block_num"][i], data["par_num"][i], data["line_num"][i])
+        line_groups[key].append(i)
+
+    if len(line_groups) < 5:
+        logger.info(f"deskew_by_word_alignment: only {len(line_groups)} lines, skipping")
+        return image_data, 0.0
+
+    scale = 1.0 / downscale_factor
+    points = []
+    for key, indices in line_groups.items():
+        best_idx = min(indices, key=lambda i: data["left"][i])
+        lx = data["left"][best_idx] * scale
+        top = data["top"][best_idx] * scale
+        h = data["height"][best_idx] * scale
+        cy = top + h / 2.0
+        points.append((lx, cy))
+
+    xs = np.array([p[0] for p in points])
+    ys = np.array([p[1] for p in points])
+    median_x = float(np.median(xs))
+    tolerance = orig_w * 0.03
+
+    mask = np.abs(xs - median_x) <= tolerance
+    filtered_xs = xs[mask]
+    filtered_ys = ys[mask]
+
+    if len(filtered_xs) < 5:
+        logger.info(f"deskew_by_word_alignment: only {len(filtered_xs)} aligned points after filter, skipping")
+        return image_data, 0.0
+
+    coeffs = np.polyfit(filtered_ys, filtered_xs, 1)
+    slope = coeffs[0]
+    angle_rad = np.arctan(slope)
+    angle_deg = float(np.degrees(angle_rad))
+
+    angle_deg = max(-5.0, min(5.0, angle_deg))
+
+    logger.info(f"deskew_by_word_alignment: detected {angle_deg:.2f}\u00b0 from {len(filtered_xs)} points "
+                f"(total lines: {len(line_groups)})")
+
+    if abs(angle_deg) < 0.05:
+        return image_data, 0.0
+
+    center = (orig_w // 2, orig_h // 2)
+    M = cv2.getRotationMatrix2D(center, angle_deg, 1.0)
+    rotated = cv2.warpAffine(img, M, (orig_w, orig_h),
+                              flags=cv2.INTER_LINEAR,
+                              borderMode=cv2.BORDER_REPLICATE)
+
+    success, png_buf = cv2.imencode(".png", rotated)
+    if not success:
+        logger.warning("deskew_by_word_alignment: PNG encoding failed")
+        return image_data, 0.0
+
+    return png_buf.tobytes(), angle_deg
+
+
+# =============================================================================
+# Projection Gradient Scoring
+# =============================================================================
+
+def _projection_gradient_score(profile: np.ndarray) -> float:
+    """Score a projection profile by the L2-norm of its first derivative."""
+    diff = np.diff(profile)
+    return float(np.sum(diff * diff))
+
+
+# =============================================================================
+# Iterative Deskew (Vertical-Edge Projection)
+# =============================================================================
+
+def deskew_image_iterative(
+    img: np.ndarray,
+    coarse_range: float = 5.0,
+    coarse_step: float = 0.1,
+    fine_range: float = 0.15,
+    fine_step: float = 0.02,
+) -> Tuple[np.ndarray, float, Dict[str, Any]]:
+    """Iterative deskew using vertical-edge projection optimisation.
+
+    Args:
+        img: BGR image (full resolution).
+        coarse_range: half-range in degrees for the coarse sweep.
+        coarse_step: step size in degrees for the coarse sweep.
+        fine_range: half-range around the coarse winner for the fine sweep.
+        fine_step: step size in degrees for the fine sweep.
+
+    Returns:
+        (rotated_bgr, angle_degrees, debug_dict)
+    """
+    h, w = img.shape[:2]
+    debug: Dict[str, Any] = {}
+
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    y_lo, y_hi = int(h * 0.15), int(h * 0.85)
+    x_lo, x_hi = int(w * 0.10), int(w * 0.90)
+    gray_crop = gray[y_lo:y_hi, x_lo:x_hi]
+
+    sobel_x = cv2.Sobel(gray_crop, cv2.CV_64F, 1, 0, ksize=3)
+    edges = np.abs(sobel_x)
+    edge_max = edges.max()
+    if edge_max > 0:
+        edges = (edges / edge_max * 255).astype(np.uint8)
+    else:
+        return img, 0.0, {"error": "no edges detected"}
+
+    crop_h, crop_w = edges.shape[:2]
+    crop_center = (crop_w // 2, crop_h // 2)
+
+    trim_y = max(4, int(crop_h * 0.03))
+    trim_x = max(4, int(crop_w * 0.03))
+
+    def _sweep_edges(angles: np.ndarray) -> list:
+        results = []
+        for angle in angles:
+            if abs(angle) < 1e-6:
+                rotated = edges
+            else:
+                M = cv2.getRotationMatrix2D(crop_center, angle, 1.0)
+                rotated = cv2.warpAffine(edges, M, (crop_w, crop_h),
+                                         flags=cv2.INTER_NEAREST,
+                                         borderMode=cv2.BORDER_REPLICATE)
+            trimmed = rotated[trim_y:-trim_y, trim_x:-trim_x]
+            v_profile = np.sum(trimmed, axis=0, dtype=np.float64)
+            score = _projection_gradient_score(v_profile)
+            results.append((float(angle), score))
+        return results
+
+    coarse_angles = np.arange(-coarse_range, coarse_range + coarse_step * 0.5, coarse_step)
+    coarse_results = _sweep_edges(coarse_angles)
+    best_coarse = max(coarse_results, key=lambda x: x[1])
+    best_coarse_angle, best_coarse_score = best_coarse
+
+    debug["coarse_best_angle"] = round(best_coarse_angle, 2)
+    debug["coarse_best_score"] = round(best_coarse_score, 1)
+    debug["coarse_scores"] = [(round(a, 2), round(s, 1)) for a, s in coarse_results]
+
+    fine_lo = best_coarse_angle - fine_range
+    fine_hi = best_coarse_angle + fine_range
+    fine_angles = np.arange(fine_lo, fine_hi + fine_step * 0.5, fine_step)
+    fine_results = _sweep_edges(fine_angles)
+    best_fine = max(fine_results, key=lambda x: x[1])
+    best_fine_angle, best_fine_score = best_fine
+
+    debug["fine_best_angle"] = round(best_fine_angle, 2)
+    debug["fine_best_score"] = round(best_fine_score, 1)
+    debug["fine_scores"] = [(round(a, 2), round(s, 1)) for a, s in fine_results]
+
+    final_angle = best_fine_angle
+    final_angle = max(-5.0, min(5.0, final_angle))
+
+    logger.info(f"deskew_iterative: coarse={best_coarse_angle:.2f}\u00b0 fine={best_fine_angle:.2f}\u00b0 -> {final_angle:.2f}\u00b0")
+
+    if abs(final_angle) < 0.05:
+        return img, 0.0, debug
+
+    center = (w // 2, h // 2)
+    M = cv2.getRotationMatrix2D(center, final_angle, 1.0)
+    rotated = cv2.warpAffine(img, M, (w, h),
+                              flags=cv2.INTER_LINEAR,
+                              borderMode=cv2.BORDER_REPLICATE)
+
+    return rotated, final_angle, debug
+
+
+# =============================================================================
+# Text-Line Slope Measurement
+# =============================================================================
+
+def _measure_textline_slope(img: np.ndarray) -> float:
+    """Measure residual text-line slope via Tesseract word-position regression."""
+    import math as _math
+
+    if not TESSERACT_AVAILABLE or not CV2_AVAILABLE:
+        return 0.0
+
+    h, w = img.shape[:2]
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+    data = pytesseract.image_to_data(
+        Image.fromarray(gray),
+        output_type=pytesseract.Output.DICT,
+        config="--psm 6",
+    )
+
+    lines: Dict[tuple, list] = {}
+    for i in range(len(data["text"])):
+        txt = (data["text"][i] or "").strip()
+        if len(txt) < 2 or int(data["conf"][i]) < 30:
+            continue
+        key = (data["block_num"][i], data["par_num"][i], data["line_num"][i])
+        cx = data["left"][i] + data["width"][i] / 2.0
+        cy = data["top"][i] + data["height"][i] / 2.0
+        lines.setdefault(key, []).append((cx, cy))
+
+    slopes: list = []
+    for pts in lines.values():
+        if len(pts) < 3:
+            continue
+        pts.sort(key=lambda p: p[0])
+        xs = np.array([p[0] for p in pts], dtype=np.float64)
+        ys = np.array([p[1] for p in pts], dtype=np.float64)
+        if xs[-1] - xs[0] < w * 0.15:
+            continue
+        A = np.vstack([xs, np.ones_like(xs)]).T
+        result = np.linalg.lstsq(A, ys, rcond=None)
+        slope = result[0][0]
+        slopes.append(_math.degrees(_math.atan(slope)))
+
+    if len(slopes) < 3:
+        return 0.0
+
+    slopes.sort()
+    trim = max(1, len(slopes) // 10)
+    trimmed = slopes[trim:-trim] if len(slopes) > 2 * trim else slopes
+    if not trimmed:
+        return 0.0
+
+    return sum(trimmed) / len(trimmed)
+
+
+# =============================================================================
+# Two-Pass Deskew
+# =============================================================================
+
+def deskew_two_pass(
+    img: np.ndarray,
+    coarse_range: float = 5.0,
+) -> Tuple[np.ndarray, float, Dict[str, Any]]:
+    """Two-pass deskew: iterative projection + word-alignment residual check.
+
+    Returns:
+        (corrected_bgr, total_angle_degrees, debug_dict)
+    """
+    debug: Dict[str, Any] = {}
+
+    # --- Pass 1: iterative projection ---
+    corrected, angle1, dbg1 = deskew_image_iterative(
+        img.copy(), coarse_range=coarse_range,
+    )
+    debug["pass1_angle"] = round(angle1, 3)
+    debug["pass1_method"] = "iterative"
+    debug["pass1_debug"] = dbg1
+
+    # --- Pass 2: word-alignment residual check ---
+    angle2 = 0.0
+    try:
+        ok, buf = cv2.imencode(".png", corrected)
+        if ok:
+            corrected_bytes, angle2 = deskew_image_by_word_alignment(buf.tobytes())
+            if abs(angle2) >= 0.3:
+                arr2 = np.frombuffer(corrected_bytes, dtype=np.uint8)
+                corrected2 = cv2.imdecode(arr2, cv2.IMREAD_COLOR)
+                if corrected2 is not None:
+                    corrected = corrected2
+                    logger.info(f"deskew_two_pass: pass2 residual={angle2:.2f}\u00b0 applied "
+                                f"(total={angle1 + angle2:.2f}\u00b0)")
+                else:
+                    angle2 = 0.0
+            else:
+                logger.info(f"deskew_two_pass: pass2 residual={angle2:.2f}\u00b0 < 0.3\u00b0 -- skipped")
+                angle2 = 0.0
+    except Exception as e:
+        logger.warning(f"deskew_two_pass: pass2 word-alignment failed: {e}")
+        angle2 = 0.0
+
+    # --- Pass 3: Tesseract text-line regression residual check ---
+    angle3 = 0.0
+    try:
+        residual = _measure_textline_slope(corrected)
+        debug["pass3_raw"] = round(residual, 3)
+        if abs(residual) >= 0.3:
+            h3, w3 = corrected.shape[:2]
+            center3 = (w3 // 2, h3 // 2)
+            M3 = cv2.getRotationMatrix2D(center3, residual, 1.0)
+            corrected = cv2.warpAffine(
+                corrected, M3, (w3, h3),
+                flags=cv2.INTER_LINEAR,
+                borderMode=cv2.BORDER_REPLICATE,
+            )
+            angle3 = residual
+            logger.info("deskew_two_pass: pass3 text-line residual=%.2f\u00b0 applied", residual)
+        else:
+            logger.info("deskew_two_pass: pass3 text-line residual=%.2f\u00b0 < 0.3\u00b0 -- skipped", residual)
+    except Exception as e:
+        logger.warning("deskew_two_pass: pass3 text-line check failed: %s", e)
+
+    total_angle = angle1 + angle2 + angle3
+    debug["pass2_angle"] = round(angle2, 3)
+    debug["pass2_method"] = "word_alignment"
+    debug["pass3_angle"] = round(angle3, 3)
+    debug["pass3_method"] = "textline_regression"
+    debug["total_angle"] = round(total_angle, 3)
+
+    logger.info(
+        "deskew_two_pass: pass1=%.2f\u00b0 + pass2=%.2f\u00b0 + pass3=%.2f\u00b0 = %.2f\u00b0",
+        angle1, angle2, angle3, total_angle,
+    )
+
+    return corrected, total_angle, debug

klausur-service/backend/cv_preprocessing_dewarp.py

@@ -0,0 +1,474 @@
+"""
+CV Preprocessing Dewarp — Vertical shear detection and correction.
+
+Provides four shear detection methods (vertical edge, projection variance,
+Hough lines, text-line drift), ensemble combination, quality gating,
+and the main dewarp_image() function.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import math
+import time
+from typing import Any, Dict, List, Tuple
+
+import numpy as np
+
+from cv_vocab_types import (
+    CV2_AVAILABLE,
+    TESSERACT_AVAILABLE,
+)
+
+logger = logging.getLogger(__name__)
+
+try:
+    import cv2
+except ImportError:
+    cv2 = None  # type: ignore[assignment]
+
+try:
+    import pytesseract
+    from PIL import Image
+except ImportError:
+    pytesseract = None  # type: ignore[assignment]
+    Image = None  # type: ignore[assignment,misc]
+
+
+# =============================================================================
+# Shear Detection Methods
+# =============================================================================
+
+def _detect_shear_angle(img: np.ndarray) -> Dict[str, Any]:
+    """Detect vertical shear angle via strongest vertical edge tracking (Method A)."""
+    h, w = img.shape[:2]
+    result = {"method": "vertical_edge", "shear_degrees": 0.0, "confidence": 0.0}
+
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    sobel_x = cv2.Sobel(gray, cv2.CV_64F, 1, 0, ksize=3)
+    abs_sobel = np.abs(sobel_x).astype(np.uint8)
+
+    _, binary = cv2.threshold(abs_sobel, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU)
+
+    num_strips = 20
+    strip_h = h // num_strips
+    edge_positions = []
+
+    for i in range(num_strips):
+        y_start = i * strip_h
+        y_end = min((i + 1) * strip_h, h)
+        strip = binary[y_start:y_end, :]
+
+        projection = np.sum(strip, axis=0).astype(np.float64)
+        if projection.max() == 0:
+            continue
+
+        search_w = int(w * 0.4)
+        left_proj = projection[:search_w]
+        if left_proj.max() == 0:
+            continue
+
+        kernel_size = max(3, w // 100)
+        if kernel_size % 2 == 0:
+            kernel_size += 1
+        smoothed = cv2.GaussianBlur(left_proj.reshape(1, -1), (kernel_size, 1), 0).flatten()
+        x_pos = float(np.argmax(smoothed))
+        y_center = (y_start + y_end) / 2.0
+        edge_positions.append((y_center, x_pos))
+
+    if len(edge_positions) < 8:
+        return result
+
+    ys = np.array([p[0] for p in edge_positions])
+    xs = np.array([p[1] for p in edge_positions])
+
+    median_x = np.median(xs)
+    std_x = max(np.std(xs), 1.0)
+    mask = np.abs(xs - median_x) < 2 * std_x
+    ys = ys[mask]
+    xs = xs[mask]
+
+    if len(ys) < 6:
+        return result
+
+    straight_coeffs = np.polyfit(ys, xs, 1)
+    slope = straight_coeffs[0]
+    fitted = np.polyval(straight_coeffs, ys)
+    residuals = xs - fitted
+    rmse = float(np.sqrt(np.mean(residuals ** 2)))
+
+    shear_degrees = math.degrees(math.atan(slope))
+
+    confidence = min(1.0, len(ys) / 15.0) * max(0.5, 1.0 - rmse / 5.0)
+
+    result["shear_degrees"] = round(shear_degrees, 3)
+    result["confidence"] = round(float(confidence), 2)
+
+    return result
+
+
+def _detect_shear_by_projection(img: np.ndarray) -> Dict[str, Any]:
+    """Detect shear angle by maximising variance of horizontal text-line projections (Method B)."""
+    result = {"method": "projection", "shear_degrees": 0.0, "confidence": 0.0}
+
+    h, w = img.shape[:2]
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    _, binary = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+
+    small = cv2.resize(binary, (w // 2, h // 2), interpolation=cv2.INTER_AREA)
+    sh, sw = small.shape
+
+    def _sweep_variance(angles_list):
+        results = []
+        for angle_deg in angles_list:
+            if abs(angle_deg) < 0.001:
+                rotated = small
+            else:
+                shear_tan = math.tan(math.radians(angle_deg))
+                M = np.float32([[1, shear_tan, -sh / 2.0 * shear_tan], [0, 1, 0]])
+                rotated = cv2.warpAffine(small, M, (sw, sh),
+                                         flags=cv2.INTER_NEAREST,
+                                         borderMode=cv2.BORDER_CONSTANT)
+            profile = np.sum(rotated, axis=1).astype(float)
+            results.append((angle_deg, float(np.var(profile))))
+        return results
+
+    coarse_angles = [a * 0.5 for a in range(-6, 7)]
+    coarse_results = _sweep_variance(coarse_angles)
+    coarse_best = max(coarse_results, key=lambda x: x[1])
+
+    fine_center = coarse_best[0]
+    fine_angles = [fine_center + a * 0.05 for a in range(-10, 11)]
+    fine_results = _sweep_variance(fine_angles)
+    fine_best = max(fine_results, key=lambda x: x[1])
+
+    best_angle = fine_best[0]
+    best_variance = fine_best[1]
+    variances = coarse_results + fine_results
+
+    all_mean = sum(v for _, v in variances) / len(variances)
+    if all_mean > 0 and best_variance > all_mean:
+        confidence = min(1.0, (best_variance - all_mean) / (all_mean + 1.0) * 0.6)
+    else:
+        confidence = 0.0
+
+    result["shear_degrees"] = round(best_angle, 3)
+    result["confidence"] = round(max(0.0, min(1.0, confidence)), 2)
+    return result
+
+
+def _detect_shear_by_hough(img: np.ndarray) -> Dict[str, Any]:
+    """Detect shear using Hough transform on printed table / ruled lines (Method C)."""
+    result = {"method": "hough_lines", "shear_degrees": 0.0, "confidence": 0.0}
+
+    h, w = img.shape[:2]
+    gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+
+    edges = cv2.Canny(gray, 50, 150, apertureSize=3)
+
+    min_len = int(w * 0.15)
+    lines = cv2.HoughLinesP(
+        edges, rho=1, theta=np.pi / 360,
+        threshold=int(w * 0.08),
+        minLineLength=min_len,
+        maxLineGap=20,
+    )
+
+    if lines is None or len(lines) < 3:
+        return result
+
+    horizontal_angles: List[Tuple[float, float]] = []
+    for line in lines:
+        x1, y1, x2, y2 = line[0]
+        if x1 == x2:
+            continue
+        angle = float(np.degrees(np.arctan2(y2 - y1, x2 - x1)))
+        if abs(angle) <= 5.0:
+            length = float(np.sqrt((x2 - x1) ** 2 + (y2 - y1) ** 2))
+            horizontal_angles.append((angle, length))
+
+    if len(horizontal_angles) < 3:
+        return result
+
+    angles_arr = np.array([a for a, _ in horizontal_angles])
+    weights_arr = np.array([l for _, l in horizontal_angles])
+    sorted_idx = np.argsort(angles_arr)
+    s_angles = angles_arr[sorted_idx]
+    s_weights = weights_arr[sorted_idx]
+    cum = np.cumsum(s_weights)
+    mid_idx = int(np.searchsorted(cum, cum[-1] / 2.0))
+    median_angle = float(s_angles[min(mid_idx, len(s_angles) - 1)])
+
+    agree = sum(1 for a, _ in horizontal_angles if abs(a - median_angle) < 1.0)
+    confidence = min(1.0, agree / max(len(horizontal_angles), 1)) * 0.85
+
+    shear_degrees = -median_angle
+
+    result["shear_degrees"] = round(shear_degrees, 3)
+    result["confidence"] = round(max(0.0, min(1.0, confidence)), 2)
+    return result
+
+
+def _detect_shear_by_text_lines(img: np.ndarray) -> Dict[str, Any]:
+    """Detect shear by measuring text-line straightness (Method D)."""
+    result = {"method": "text_lines", "shear_degrees": 0.0, "confidence": 0.0}
+
+    h, w = img.shape[:2]
+    scale = 0.5
+    small = cv2.resize(img, (int(w * scale), int(h * scale)),
+                       interpolation=cv2.INTER_AREA)
+    gray = cv2.cvtColor(small, cv2.COLOR_BGR2GRAY)
+    pil_img = Image.fromarray(gray)
+
+    try:
+        data = pytesseract.image_to_data(
+            pil_img, lang='eng+deu', config='--psm 11 --oem 3',
+            output_type=pytesseract.Output.DICT,
+        )
+    except Exception:
+        return result
+
+    words = []
+    for i in range(len(data['text'])):
+        text = data['text'][i].strip()
+        conf = int(data['conf'][i])
+        if not text or conf < 20 or len(text) < 2:
+            continue
+        left_x = float(data['left'][i])
+        cy = data['top'][i] + data['height'][i] / 2.0
+        word_w = float(data['width'][i])
+        words.append((left_x, cy, word_w))
+
+    if len(words) < 15:
+        return result
+
+    avg_w = sum(ww for _, _, ww in words) / len(words)
+    x_tol = max(avg_w * 0.4, 8)
+
+    words_by_x = sorted(words, key=lambda w: w[0])
+    columns: List[List[Tuple[float, float]]] = []
+    cur_col: List[Tuple[float, float]] = [(words_by_x[0][0], words_by_x[0][1])]
+    cur_x = words_by_x[0][0]
+
+    for lx, cy, _ in words_by_x[1:]:
+        if abs(lx - cur_x) <= x_tol:
+            cur_col.append((lx, cy))
+            cur_x = cur_x * 0.8 + lx * 0.2
+        else:
+            if len(cur_col) >= 5:
+                columns.append(cur_col)
+            cur_col = [(lx, cy)]
+            cur_x = lx
+    if len(cur_col) >= 5:
+        columns.append(cur_col)
+
+    if len(columns) < 2:
+        return result
+
+    drifts = []
+    for col in columns:
+        ys = np.array([p[1] for p in col])
+        xs = np.array([p[0] for p in col])
+        y_range = ys.max() - ys.min()
+        if y_range < h * scale * 0.3:
+            continue
+        coeffs = np.polyfit(ys, xs, 1)
+        drifts.append(coeffs[0])
+
+    if len(drifts) < 2:
+        return result
+
+    median_drift = float(np.median(drifts))
+    shear_degrees = math.degrees(math.atan(median_drift))
+
+    drift_std = float(np.std(drifts))
+    consistency = max(0.0, 1.0 - drift_std * 50)
+    count_factor = min(1.0, len(drifts) / 4.0)
+    confidence = count_factor * 0.5 + consistency * 0.5
+
+    result["shear_degrees"] = round(shear_degrees, 3)
+    result["confidence"] = round(max(0.0, min(1.0, confidence)), 2)
+    logger.info("text_lines(v2): %d columns, %d drifts, median=%.4f, "
+                "shear=%.3f\u00b0, conf=%.2f",
+                len(columns), len(drifts), median_drift,
+                shear_degrees, confidence)
+    return result
+
+
+# =============================================================================
+# Quality Check and Shear Application
+# =============================================================================
+
+def _dewarp_quality_check(original: np.ndarray, corrected: np.ndarray) -> bool:
+    """Check whether the dewarp correction actually improved alignment."""
+    def _h_proj_variance(img: np.ndarray) -> float:
+        gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
+        _, binary = cv2.threshold(gray, 0, 255,
+                                  cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)
+        small = cv2.resize(binary, (binary.shape[1] // 2, binary.shape[0] // 2),
+                           interpolation=cv2.INTER_AREA)
+        profile = np.sum(small, axis=1).astype(float)
+        return float(np.var(profile))
+
+    var_before = _h_proj_variance(original)
+    var_after = _h_proj_variance(corrected)
+
+    return var_after > var_before
+
+
+def _apply_shear(img: np.ndarray, shear_degrees: float) -> np.ndarray:
+    """Apply a vertical shear correction to an image."""
+    h, w = img.shape[:2]
+    shear_tan = math.tan(math.radians(shear_degrees))
+
+    M = np.float32([
+        [1, shear_tan, -h / 2.0 * shear_tan],
+        [0, 1, 0],
+    ])
+
+    corrected = cv2.warpAffine(img, M, (w, h),
+                                flags=cv2.INTER_LINEAR,
+                                borderMode=cv2.BORDER_REPLICATE)
+    return corrected
+
+
+# =============================================================================
+# Ensemble Shear Combination
+# =============================================================================
+
+def _ensemble_shear(detections: List[Dict[str, Any]]) -> Tuple[float, float, str]:
+    """Combine multiple shear detections into a single weighted estimate (v2)."""
+    _MIN_CONF = 0.35
+    _METHOD_WEIGHT_BOOST = {"text_lines": 1.5}
+
+    accepted = []
+    for d in detections:
+        if d["confidence"] < _MIN_CONF:
+            continue
+        boost = _METHOD_WEIGHT_BOOST.get(d["method"], 1.0)
+        effective_conf = d["confidence"] * boost
+        accepted.append((d["shear_degrees"], effective_conf, d["method"]))
+
+    if not accepted:
+        return 0.0, 0.0, "none"
+
+    if len(accepted) == 1:
+        deg, conf, method = accepted[0]
+        return deg, min(conf, 1.0), method
+
+    total_w = sum(c for _, c, _ in accepted)
+    w_mean = sum(d * c for d, c, _ in accepted) / total_w
+
+    filtered = [(d, c, m) for d, c, m in accepted if abs(d - w_mean) <= 1.0]
+    if not filtered:
+        filtered = accepted
+
+    total_w2 = sum(c for _, c, _ in filtered)
+    final_deg = sum(d * c for d, c, _ in filtered) / total_w2
+
+    avg_conf = total_w2 / len(filtered)
+    spread = max(d for d, _, _ in filtered) - min(d for d, _, _ in filtered)
+    agreement_bonus = 0.15 if spread < 0.5 else 0.0
+    ensemble_conf = min(1.0, avg_conf + agreement_bonus)
+
+    methods_str = "+".join(m for _, _, m in filtered)
+    return round(final_deg, 3), round(min(ensemble_conf, 1.0), 2), methods_str
+
+
+# =============================================================================
+# Main Dewarp Function
+# =============================================================================
+
+def dewarp_image(img: np.ndarray, use_ensemble: bool = True) -> Tuple[np.ndarray, Dict[str, Any]]:
+    """Correct vertical shear after deskew (v2 with quality gate).
+
+    Methods (all run in ~150ms total):
+        A. _detect_shear_angle()           -- vertical edge profile (~50ms)
+        B. _detect_shear_by_projection()   -- horizontal text-line variance (~30ms)
+        C. _detect_shear_by_hough()        -- Hough lines on table borders (~20ms)
+        D. _detect_shear_by_text_lines()   -- text-line straightness (~50ms)
+
+    Args:
+        img: BGR image (already deskewed).
+        use_ensemble: If False, fall back to single-method behaviour (method A only).
+
+    Returns:
+        Tuple of (corrected_image, dewarp_info).
+    """
+    no_correction = {
+        "method": "none",
+        "shear_degrees": 0.0,
+        "confidence": 0.0,
+        "detections": [],
+    }
+
+    if not CV2_AVAILABLE:
+        return img, no_correction
+
+    t0 = time.time()
+
+    if use_ensemble:
+        det_a = _detect_shear_angle(img)
+        det_b = _detect_shear_by_projection(img)
+        det_c = _detect_shear_by_hough(img)
+        det_d = _detect_shear_by_text_lines(img)
+        detections = [det_a, det_b, det_c, det_d]
+        shear_deg, confidence, method = _ensemble_shear(detections)
+    else:
+        det_a = _detect_shear_angle(img)
+        detections = [det_a]
+        shear_deg = det_a["shear_degrees"]
+        confidence = det_a["confidence"]
+        method = det_a["method"]
+
+    duration = time.time() - t0
+
+    logger.info(
+        "dewarp: ensemble shear=%.3f\u00b0 conf=%.2f method=%s (%.2fs) | "
+        "A=%.3f/%.2f B=%.3f/%.2f C=%.3f/%.2f D=%.3f/%.2f",
+        shear_deg, confidence, method, duration,
+        detections[0]["shear_degrees"], detections[0]["confidence"],
+        detections[1]["shear_degrees"] if len(detections) > 1 else 0.0,
+        detections[1]["confidence"] if len(detections) > 1 else 0.0,
+        detections[2]["shear_degrees"] if len(detections) > 2 else 0.0,
+        detections[2]["confidence"] if len(detections) > 2 else 0.0,
+        detections[3]["shear_degrees"] if len(detections) > 3 else 0.0,
+        detections[3]["confidence"] if len(detections) > 3 else 0.0,
+    )
+
+    _all_detections = [
+        {"method": d["method"], "shear_degrees": d["shear_degrees"],
+         "confidence": d["confidence"]}
+        for d in detections
+    ]
+
+    if abs(shear_deg) < 0.08 or confidence < 0.4:
+        no_correction["detections"] = _all_detections
+        return img, no_correction
+
+    corrected = _apply_shear(img, -shear_deg)
+
+    if abs(shear_deg) >= 0.5 and not _dewarp_quality_check(img, corrected):
+        logger.info("dewarp: quality gate REJECTED correction (%.3f\u00b0) -- "
+                     "projection variance did not improve", shear_deg)
+        no_correction["detections"] = _all_detections
+        return img, no_correction
+
+    info = {
+        "method": method,
+        "shear_degrees": shear_deg,
+        "confidence": confidence,
+        "detections": _all_detections,
+    }
+
+    return corrected, info
+
+
+def dewarp_image_manual(img: np.ndarray, shear_degrees: float) -> np.ndarray:
+    """Apply shear correction with a manual angle."""
+    if abs(shear_degrees) < 0.001:
+        return img
+    return _apply_shear(img, -shear_degrees)

klausur-service/backend/cv_review_llm.py

@@ -0,0 +1,388 @@
+"""
+CV Review LLM — LLM-based OCR correction: prompt building, change detection, streaming.
+
+Handles the LLM review path (REVIEW_ENGINE=llm) and shared utilities like
+_entry_needs_review, _is_spurious_change, _diff_batch, and JSON parsing.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import json
+import logging
+import os
+import re
+import time
+from typing import Dict, List, Tuple
+
+import httpx
+
+logger = logging.getLogger(__name__)
+
+_OLLAMA_URL = os.getenv("OLLAMA_BASE_URL", "http://host.docker.internal:11434")
+OLLAMA_REVIEW_MODEL = os.getenv("OLLAMA_REVIEW_MODEL", "qwen3:0.6b")
+_REVIEW_BATCH_SIZE = int(os.getenv("OLLAMA_REVIEW_BATCH_SIZE", "20"))
+logger.info("LLM review model: %s (batch=%d)", OLLAMA_REVIEW_MODEL, _REVIEW_BATCH_SIZE)
+
+REVIEW_ENGINE = os.getenv("REVIEW_ENGINE", "spell")   # "spell" (default) | "llm"
+
+# Regex: entry contains IPA phonetic brackets like "dance [da:ns]"
+_HAS_PHONETIC_RE = re.compile(r'\[.*?[\u02c8\u02cc\u02d0\u0283\u0292\u03b8\u00f0\u014b\u0251\u0252\u0254\u0259\u025c\u026a\u028a\u028c\u00e6].*?\]')
+
+# Regex: digit adjacent to a letter -- OCR digit<->letter confusion
+_OCR_DIGIT_IN_WORD_RE = re.compile(r'(?<=[A-Za-z\u00c4\u00d6\u00dc\u00e4\u00f6\u00fc\u00df])[01568]|[01568](?=[A-Za-z\u00c4\u00d6\u00dc\u00e4\u00f6\u00fc\u00df])')
+
+
+def _entry_needs_review(entry: Dict) -> bool:
+    """Check if an entry should be sent for review.
+
+    Sends all non-empty entries that don't have IPA phonetic transcriptions.
+    """
+    en = entry.get("english", "") or ""
+    de = entry.get("german", "") or ""
+
+    if not en.strip() and not de.strip():
+        return False
+    if _HAS_PHONETIC_RE.search(en) or _HAS_PHONETIC_RE.search(de):
+        return False
+    return True
+
+
+def _build_llm_prompt(table_lines: List[Dict]) -> str:
+    """Build the LLM correction prompt for a batch of entries."""
+    return f"""Du bist ein OCR-Zeichenkorrektur-Werkzeug fuer Vokabeltabellen (Englisch-Deutsch).
+
+DEINE EINZIGE AUFGABE: Einzelne Zeichen korrigieren, die vom OCR-Scanner als Ziffer statt als Buchstabe erkannt wurden.
+
+NUR diese Korrekturen sind erlaubt:
+- Ziffer 8 statt B: "8en" -> "Ben", "8uch" -> "Buch", "8all" -> "Ball"
+- Ziffer 0 statt O oder o: "L0ndon" -> "London", "0ld" -> "Old"
+- Ziffer 1 statt l oder I: "1ong" -> "long", "Ber1in" -> "Berlin"
+- Ziffer 5 statt S oder s: "5tadt" -> "Stadt", "5ee" -> "See"
+- Ziffer 6 statt G oder g: "6eld" -> "Geld"
+- Senkrechter Strich | statt I oder l: "| want" -> "I want", "|ong" -> "long", "he| p" -> "help"
+
+ABSOLUT VERBOTEN -- aendere NIEMALS:
+- Woerter die korrekt geschrieben sind -- auch wenn du eine andere Schreibweise kennst
+- Uebersetzungen -- du uebersetzt NICHTS, weder EN->DE noch DE->EN
+- Korrekte englische Woerter (en-Spalte) -- auch wenn du eine Bedeutung kennst
+- Korrekte deutsche Woerter (de-Spalte) -- auch wenn du sie anders sagen wuerdest
+- Eigennamen: Ben, London, China, Africa, Shakespeare usw.
+- Abkuerzungen: sth., sb., etc., e.g., i.e., v.t., smb. usw.
+- Lautschrift in eckigen Klammern [...] -- diese NIEMALS beruehren
+- Beispielsaetze in der ex-Spalte -- NIEMALS aendern
+
+Wenn ein Wort keinen Ziffer-Buchstaben-Fehler enthaelt: gib es UNVERAENDERT zurueck und setze "corrected": false.
+
+Antworte NUR mit dem JSON-Array. Kein Text davor oder danach.
+Behalte die exakte Struktur (gleiche Anzahl Eintraege, gleiche Reihenfolge).
+
+/no_think
+
+Eingabe:
+{json.dumps(table_lines, ensure_ascii=False, indent=2)}"""
+
+
+def _is_spurious_change(old_val: str, new_val: str) -> bool:
+    """Detect LLM changes that are likely wrong and should be discarded.
+
+    Only digit<->letter substitutions (0->O, 1->l, 5->S, 6->G, 8->B) are
+    legitimate OCR corrections. Everything else is rejected.
+    """
+    if not old_val or not new_val:
+        return False
+
+    if old_val.lower() == new_val.lower():
+        return True
+
+    old_words = old_val.split()
+    new_words = new_val.split()
+    if abs(len(old_words) - len(new_words)) > 1:
+        return True
+
+    _OCR_CHAR_MAP = {
+        '0': set('oOgG'),
+        '1': set('lLiI'),
+        '5': set('sS'),
+        '6': set('gG'),
+        '8': set('bB'),
+        '|': set('lLiI1'),
+        'l': set('iI|1'),
+    }
+    has_valid_fix = False
+    if len(old_val) == len(new_val):
+        for oc, nc in zip(old_val, new_val):
+            if oc != nc:
+                if oc in _OCR_CHAR_MAP and nc in _OCR_CHAR_MAP[oc]:
+                    has_valid_fix = True
+                elif nc in _OCR_CHAR_MAP and oc in _OCR_CHAR_MAP[nc]:
+                    has_valid_fix = True
+    else:
+        _OCR_SUSPICIOUS_RE = re.compile(r'[|01568]')
+        if abs(len(old_val) - len(new_val)) <= 1 and _OCR_SUSPICIOUS_RE.search(old_val):
+            has_valid_fix = True
+
+    if not has_valid_fix:
+        return True
+
+    return False
+
+
+def _diff_batch(originals: List[Dict], corrected: List[Dict]) -> Tuple[List[Dict], List[Dict]]:
+    """Compare original entries with LLM-corrected ones, return (changes, corrected_entries)."""
+    changes = []
+    entries_out = []
+    for i, orig in enumerate(originals):
+        if i < len(corrected):
+            c = corrected[i]
+            entry = dict(orig)
+            for field_name, key in [("english", "en"), ("german", "de"), ("example", "ex")]:
+                new_val = c.get(key, "").strip()
+                old_val = (orig.get(field_name, "") or "").strip()
+                if new_val and new_val != old_val:
+                    if _is_spurious_change(old_val, new_val):
+                        continue
+                    changes.append({
+                        "row_index": orig.get("row_index", i),
+                        "field": field_name,
+                        "old": old_val,
+                        "new": new_val,
+                    })
+                    entry[field_name] = new_val
+                    entry["llm_corrected"] = True
+            entries_out.append(entry)
+        else:
+            entries_out.append(dict(orig))
+    return changes, entries_out
+
+
+def _sanitize_for_json(text: str) -> str:
+    """Remove or escape control characters that break JSON parsing."""
+    return re.sub(r'[\x00-\x08\x0b\x0c\x0e-\x1f\x7f]', ' ', text)
+
+
+def _parse_llm_json_array(text: str) -> List[Dict]:
+    """Extract JSON array from LLM response (handles markdown fences and qwen3 think-tags)."""
+    text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
+    text = re.sub(r'```json\s*', '', text)
+    text = re.sub(r'```\s*', '', text)
+    text = _sanitize_for_json(text)
+    match = re.search(r'\[.*\]', text, re.DOTALL)
+    if match:
+        try:
+            return json.loads(match.group())
+        except (ValueError, json.JSONDecodeError) as e:
+            logger.warning("LLM review: JSON parse failed: %s | raw snippet: %.200s", e, match.group()[:200])
+    else:
+        logger.warning("LLM review: no JSON array found in response (%.200s)", text[:200])
+    return []
+
+
+async def llm_review_entries(
+    entries: List[Dict],
+    model: str = None,
+) -> Dict:
+    """OCR error correction. Uses spell-checker (REVIEW_ENGINE=spell) or LLM (REVIEW_ENGINE=llm)."""
+    from cv_review_spell import spell_review_entries_sync, _SPELL_AVAILABLE
+
+    if REVIEW_ENGINE == "spell" and _SPELL_AVAILABLE:
+        return spell_review_entries_sync(entries)
+    if REVIEW_ENGINE == "spell" and not _SPELL_AVAILABLE:
+        logger.warning("REVIEW_ENGINE=spell but pyspellchecker not installed, using LLM")
+
+    model = model or OLLAMA_REVIEW_MODEL
+
+    reviewable = [(i, e) for i, e in enumerate(entries) if _entry_needs_review(e)]
+
+    if not reviewable:
+        return {
+            "entries_original": entries,
+            "entries_corrected": [dict(e) for e in entries],
+            "changes": [],
+            "skipped_count": len(entries),
+            "model_used": model,
+            "duration_ms": 0,
+        }
+
+    review_entries = [e for _, e in reviewable]
+    table_lines = [
+        {"row": e.get("row_index", 0), "en": e.get("english", ""), "de": e.get("german", ""), "ex": e.get("example", "")}
+        for e in review_entries
+    ]
+
+    logger.info("LLM review: sending %d/%d entries to %s (skipped %d without digit-pattern)",
+                len(review_entries), len(entries), model, len(entries) - len(reviewable))
+
+    prompt = _build_llm_prompt(table_lines)
+
+    t0 = time.time()
+    async with httpx.AsyncClient(timeout=300.0) as client:
+        resp = await client.post(
+            f"{_OLLAMA_URL}/api/chat",
+            json={
+                "model": model,
+                "messages": [{"role": "user", "content": prompt}],
+                "stream": False,
+                "think": False,
+                "options": {"temperature": 0.1, "num_predict": 8192},
+            },
+        )
+        resp.raise_for_status()
+        content = resp.json().get("message", {}).get("content", "")
+    duration_ms = int((time.time() - t0) * 1000)
+
+    logger.info("LLM review: response in %dms, raw length=%d chars", duration_ms, len(content))
+
+    corrected = _parse_llm_json_array(content)
+    changes, corrected_entries = _diff_batch(review_entries, corrected)
+
+    all_corrected = [dict(e) for e in entries]
+    for batch_idx, (orig_idx, _) in enumerate(reviewable):
+        if batch_idx < len(corrected_entries):
+            all_corrected[orig_idx] = corrected_entries[batch_idx]
+
+    return {
+        "entries_original": entries,
+        "entries_corrected": all_corrected,
+        "changes": changes,
+        "skipped_count": len(entries) - len(reviewable),
+        "model_used": model,
+        "duration_ms": duration_ms,
+    }
+
+
+async def llm_review_entries_streaming(
+    entries: List[Dict],
+    model: str = None,
+    batch_size: int = _REVIEW_BATCH_SIZE,
+):
+    """Async generator: yield SSE events. Uses spell-checker or LLM depending on REVIEW_ENGINE.
+
+    Phase 0 (always): Run _fix_character_confusion and emit any changes.
+    """
+    from cv_ocr_engines import _fix_character_confusion
+    from cv_review_spell import spell_review_entries_streaming, _SPELL_AVAILABLE
+
+    _CONF_FIELDS = ('english', 'german', 'example')
+    originals = [{f: e.get(f, '') for f in _CONF_FIELDS} for e in entries]
+    _fix_character_confusion(entries)
+    char_changes = [
+        {'row_index': i, 'field': f, 'old': originals[i][f], 'new': entries[i].get(f, '')}
+        for i in range(len(entries))
+        for f in _CONF_FIELDS
+        if originals[i][f] != entries[i].get(f, '')
+    ]
+
+    if REVIEW_ENGINE == "spell" and _SPELL_AVAILABLE:
+        _meta_sent = False
+        async for event in spell_review_entries_streaming(entries, batch_size):
+            yield event
+            if not _meta_sent and event.get('type') == 'meta' and char_changes:
+                _meta_sent = True
+                yield {
+                    'type': 'batch',
+                    'changes': char_changes,
+                    'entries_reviewed': sorted({c['row_index'] for c in char_changes}),
+                    'progress': {'current': 0, 'total': len(entries)},
+                }
+        return
+
+    if REVIEW_ENGINE == "spell" and not _SPELL_AVAILABLE:
+        logger.warning("REVIEW_ENGINE=spell but pyspellchecker not installed, using LLM")
+
+    # LLM path
+    if char_changes:
+        yield {
+            'type': 'batch',
+            'changes': char_changes,
+            'entries_reviewed': sorted({c['row_index'] for c in char_changes}),
+            'progress': {'current': 0, 'total': len(entries)},
+        }
+
+    model = model or OLLAMA_REVIEW_MODEL
+
+    reviewable = []
+    skipped_indices = []
+    for i, e in enumerate(entries):
+        if _entry_needs_review(e):
+            reviewable.append((i, e))
+        else:
+            skipped_indices.append(i)
+
+    total_to_review = len(reviewable)
+
+    yield {
+        "type": "meta",
+        "total_entries": len(entries),
+        "to_review": total_to_review,
+        "skipped": len(skipped_indices),
+        "model": model,
+        "batch_size": batch_size,
+    }
+
+    all_changes = []
+    all_corrected = [dict(e) for e in entries]
+    total_duration_ms = 0
+    reviewed_count = 0
+
+    for batch_start in range(0, total_to_review, batch_size):
+        batch_items = reviewable[batch_start:batch_start + batch_size]
+        batch_entries = [e for _, e in batch_items]
+
+        table_lines = [
+            {"row": e.get("row_index", 0), "en": e.get("english", ""), "de": e.get("german", ""), "ex": e.get("example", "")}
+            for e in batch_entries
+        ]
+
+        prompt = _build_llm_prompt(table_lines)
+
+        logger.info("LLM review streaming: batch %d -- sending %d entries to %s",
+                    batch_start // batch_size, len(batch_entries), model)
+
+        t0 = time.time()
+        async with httpx.AsyncClient(timeout=300.0) as client:
+            resp = await client.post(
+                f"{_OLLAMA_URL}/api/chat",
+                json={
+                    "model": model,
+                    "messages": [{"role": "user", "content": prompt}],
+                    "stream": False,
+                    "think": False,
+                    "options": {"temperature": 0.1, "num_predict": 8192},
+                },
+            )
+            resp.raise_for_status()
+            content = resp.json().get("message", {}).get("content", "")
+        batch_ms = int((time.time() - t0) * 1000)
+        total_duration_ms += batch_ms
+
+        corrected = _parse_llm_json_array(content)
+        batch_changes, batch_corrected = _diff_batch(batch_entries, corrected)
+
+        for batch_idx, (orig_idx, _) in enumerate(batch_items):
+            if batch_idx < len(batch_corrected):
+                all_corrected[orig_idx] = batch_corrected[batch_idx]
+
+        all_changes.extend(batch_changes)
+        reviewed_count += len(batch_items)
+
+        yield {
+            "type": "batch",
+            "batch_index": batch_start // batch_size,
+            "entries_reviewed": [e.get("row_index", 0) for _, e in batch_items],
+            "changes": batch_changes,
+            "duration_ms": batch_ms,
+            "progress": {"current": reviewed_count, "total": total_to_review},
+        }
+
+    yield {
+        "type": "complete",
+        "changes": all_changes,
+        "model_used": model,
+        "duration_ms": total_duration_ms,
+        "total_entries": len(entries),
+        "reviewed": total_to_review,
+        "skipped": len(skipped_indices),
+        "corrections_found": len(all_changes),
+        "entries_corrected": all_corrected,
+    }

klausur-service/backend/cv_review_pipeline.py

@@ -0,0 +1,430 @@
+"""
+CV Review Pipeline — Multi-pass OCR, line alignment, LLM post-correction, and orchestration.
+
+Stages 6-8 of the CV vocabulary pipeline plus the main orchestrator.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import time
+from typing import Any, Dict, List, Optional
+
+import numpy as np
+
+from cv_vocab_types import (
+    CV_PIPELINE_AVAILABLE,
+    PageRegion,
+    PipelineResult,
+    VocabRow,
+)
+from cv_preprocessing import (
+    deskew_image,
+    dewarp_image,
+    render_image_high_res,
+    render_pdf_high_res,
+)
+from cv_layout import (
+    analyze_layout,
+    create_layout_image,
+    create_ocr_image,
+)
+from cv_ocr_engines import (
+    _group_words_into_lines,
+)
+
+logger = logging.getLogger(__name__)
+
+try:
+    import cv2
+except ImportError:
+    cv2 = None  # type: ignore[assignment]
+
+try:
+    import pytesseract
+    from PIL import Image
+except ImportError:
+    pytesseract = None  # type: ignore[assignment]
+    Image = None  # type: ignore[assignment,misc]
+
+
+# =============================================================================
+# Stage 6: Multi-Pass OCR
+# =============================================================================
+
+def ocr_region(ocr_img: np.ndarray, region: PageRegion, lang: str,
+               psm: int, fallback_psm: Optional[int] = None,
+               min_confidence: float = 40.0) -> List[Dict[str, Any]]:
+    """Run Tesseract OCR on a specific region with given PSM.
+
+    Args:
+        ocr_img: Binarized full-page image.
+        region: Region to crop and OCR.
+        lang: Tesseract language string.
+        psm: Page Segmentation Mode.
+        fallback_psm: If confidence too low, retry with this PSM per line.
+        min_confidence: Minimum average confidence before fallback.
+
+    Returns:
+        List of word dicts with text, position, confidence.
+    """
+    crop = ocr_img[region.y:region.y + region.height,
+                   region.x:region.x + region.width]
+
+    if crop.size == 0:
+        return []
+
+    pil_img = Image.fromarray(crop)
+
+    config = f'--psm {psm} --oem 3'
+    try:
+        data = pytesseract.image_to_data(pil_img, lang=lang, config=config,
+                                         output_type=pytesseract.Output.DICT)
+    except Exception as e:
+        logger.warning(f"Tesseract failed for region {region.type}: {e}")
+        return []
+
+    words = []
+    for i in range(len(data['text'])):
+        text = data['text'][i].strip()
+        conf = int(data['conf'][i])
+        if not text or conf < 10:
+            continue
+        words.append({
+            'text': text,
+            'left': data['left'][i] + region.x,
+            'top': data['top'][i] + region.y,
+            'width': data['width'][i],
+            'height': data['height'][i],
+            'conf': conf,
+            'region_type': region.type,
+        })
+
+    if words and fallback_psm is not None:
+        avg_conf = sum(w['conf'] for w in words) / len(words)
+        if avg_conf < min_confidence:
+            logger.info(f"Region {region.type}: avg confidence {avg_conf:.0f}% < {min_confidence}%, "
+                        f"trying fallback PSM {fallback_psm}")
+            words = _ocr_region_line_by_line(ocr_img, region, lang, fallback_psm)
+
+    return words
+
+
+def _ocr_region_line_by_line(ocr_img: np.ndarray, region: PageRegion,
+                              lang: str, psm: int) -> List[Dict[str, Any]]:
+    """OCR a region line by line (fallback for low-confidence regions)."""
+    crop = ocr_img[region.y:region.y + region.height,
+                   region.x:region.x + region.width]
+
+    if crop.size == 0:
+        return []
+
+    inv = cv2.bitwise_not(crop)
+    h_proj = np.sum(inv, axis=1)
+    threshold = np.max(h_proj) * 0.05 if np.max(h_proj) > 0 else 0
+
+    lines = []
+    in_text = False
+    line_start = 0
+    for y in range(len(h_proj)):
+        if h_proj[y] > threshold and not in_text:
+            line_start = y
+            in_text = True
+        elif h_proj[y] <= threshold and in_text:
+            if y - line_start > 5:
+                lines.append((line_start, y))
+            in_text = False
+    if in_text and len(h_proj) - line_start > 5:
+        lines.append((line_start, len(h_proj)))
+
+    all_words = []
+    config = f'--psm {psm} --oem 3'
+
+    for line_y_start, line_y_end in lines:
+        pad = 3
+        y1 = max(0, line_y_start - pad)
+        y2 = min(crop.shape[0], line_y_end + pad)
+        line_crop = crop[y1:y2, :]
+
+        if line_crop.size == 0:
+            continue
+
+        pil_img = Image.fromarray(line_crop)
+        try:
+            data = pytesseract.image_to_data(pil_img, lang=lang, config=config,
+                                             output_type=pytesseract.Output.DICT)
+        except Exception:
+            continue
+
+        for i in range(len(data['text'])):
+            text = data['text'][i].strip()
+            conf = int(data['conf'][i])
+            if not text or conf < 10:
+                continue
+            all_words.append({
+                'text': text,
+                'left': data['left'][i] + region.x,
+                'top': data['top'][i] + region.y + y1,
+                'width': data['width'][i],
+                'height': data['height'][i],
+                'conf': conf,
+                'region_type': region.type,
+            })
+
+    return all_words
+
+
+def run_multi_pass_ocr(ocr_img: np.ndarray,
+                       regions: List[PageRegion],
+                       lang: str = "eng+deu") -> Dict[str, List[Dict]]:
+    """Run OCR on each detected region with optimized settings."""
+    results: Dict[str, List[Dict]] = {}
+
+    _ocr_skip = {'header', 'footer', 'margin_top', 'margin_bottom', 'margin_left', 'margin_right'}
+    for region in regions:
+        if region.type in _ocr_skip:
+            continue
+
+        if region.type == 'column_en':
+            words = ocr_region(ocr_img, region, lang='eng', psm=4)
+        elif region.type == 'column_de':
+            words = ocr_region(ocr_img, region, lang='deu', psm=4)
+        elif region.type == 'column_example':
+            words = ocr_region(ocr_img, region, lang=lang, psm=6,
+                              fallback_psm=7, min_confidence=40.0)
+        else:
+            words = ocr_region(ocr_img, region, lang=lang, psm=6)
+
+        results[region.type] = words
+        logger.info(f"OCR {region.type}: {len(words)} words")
+
+    return results
+
+
+# =============================================================================
+# Stage 7: Line Alignment -> Vocabulary Entries
+# =============================================================================
+
+def match_lines_to_vocab(ocr_results: Dict[str, List[Dict]],
+                          regions: List[PageRegion],
+                          y_tolerance_px: int = 25) -> List[VocabRow]:
+    """Align OCR results from different columns into vocabulary rows."""
+    if 'column_en' not in ocr_results and 'column_de' not in ocr_results:
+        logger.info("match_lines_to_vocab: no column_en/column_de in OCR results, returning empty")
+        return []
+
+    en_lines = _group_words_into_lines(ocr_results.get('column_en', []), y_tolerance_px)
+    de_lines = _group_words_into_lines(ocr_results.get('column_de', []), y_tolerance_px)
+    ex_lines = _group_words_into_lines(ocr_results.get('column_example', []), y_tolerance_px)
+
+    def line_y_center(line: List[Dict]) -> float:
+        return sum(w['top'] + w['height'] / 2 for w in line) / len(line)
+
+    def line_text(line: List[Dict]) -> str:
+        return ' '.join(w['text'] for w in line)
+
+    def line_confidence(line: List[Dict]) -> float:
+        return sum(w['conf'] for w in line) / len(line) if line else 0
+
+    vocab_rows: List[VocabRow] = []
+
+    for en_line in en_lines:
+        en_y = line_y_center(en_line)
+        en_text = line_text(en_line)
+        en_conf = line_confidence(en_line)
+
+        if len(en_text.strip()) < 2:
+            continue
+
+        de_text = ""
+        de_conf = 0.0
+        best_de_dist = float('inf')
+        best_de_idx = -1
+        for idx, de_line in enumerate(de_lines):
+            dist = abs(line_y_center(de_line) - en_y)
+            if dist < y_tolerance_px and dist < best_de_dist:
+                best_de_dist = dist
+                best_de_idx = idx
+
+        if best_de_idx >= 0:
+            de_text = line_text(de_lines[best_de_idx])
+            de_conf = line_confidence(de_lines[best_de_idx])
+
+        ex_text = ""
+        ex_conf = 0.0
+        best_ex_dist = float('inf')
+        best_ex_idx = -1
+        for idx, ex_line in enumerate(ex_lines):
+            dist = abs(line_y_center(ex_line) - en_y)
+            if dist < y_tolerance_px and dist < best_ex_dist:
+                best_ex_dist = dist
+                best_ex_idx = idx
+
+        if best_ex_idx >= 0:
+            ex_text = line_text(ex_lines[best_ex_idx])
+            ex_conf = line_confidence(ex_lines[best_ex_idx])
+
+        avg_conf = en_conf
+        conf_count = 1
+        if de_conf > 0:
+            avg_conf += de_conf
+            conf_count += 1
+        if ex_conf > 0:
+            avg_conf += ex_conf
+            conf_count += 1
+
+        vocab_rows.append(VocabRow(
+            english=en_text.strip(),
+            german=de_text.strip(),
+            example=ex_text.strip(),
+            confidence=avg_conf / conf_count,
+            y_position=int(en_y),
+        ))
+
+    # Handle multi-line wrapping in example column
+    matched_ex_ys = set()
+    for row in vocab_rows:
+        if row.example:
+            matched_ex_ys.add(row.y_position)
+
+    for ex_line in ex_lines:
+        ex_y = line_y_center(ex_line)
+        already_matched = any(abs(ex_y - y) < y_tolerance_px for y in matched_ex_ys)
+        if already_matched:
+            continue
+
+        best_row = None
+        best_dist = float('inf')
+        for row in vocab_rows:
+            dist = ex_y - row.y_position
+            if 0 < dist < y_tolerance_px * 3 and dist < best_dist:
+                best_dist = dist
+                best_row = row
+
+        if best_row:
+            continuation = line_text(ex_line).strip()
+            if continuation:
+                best_row.example = (best_row.example + " " + continuation).strip()
+
+    vocab_rows.sort(key=lambda r: r.y_position)
+
+    return vocab_rows
+
+
+# =============================================================================
+# Stage 8: Optional LLM Post-Correction
+# =============================================================================
+
+async def llm_post_correct(img: np.ndarray, vocab_rows: List[VocabRow],
+                           confidence_threshold: float = 50.0,
+                           enabled: bool = False) -> List[VocabRow]:
+    """Optionally send low-confidence regions to Qwen-VL for correction."""
+    if not enabled:
+        return vocab_rows
+
+    logger.info(f"LLM post-correction skipped (not yet implemented)")
+    return vocab_rows
+
+
+# =============================================================================
+# Orchestrator
+# =============================================================================
+
+async def run_cv_pipeline(
+    pdf_data: Optional[bytes] = None,
+    image_data: Optional[bytes] = None,
+    page_number: int = 0,
+    zoom: float = 3.0,
+    enable_dewarp: bool = True,
+    enable_llm_correction: bool = False,
+    lang: str = "eng+deu",
+) -> PipelineResult:
+    """Run the complete CV document reconstruction pipeline."""
+    if not CV_PIPELINE_AVAILABLE:
+        return PipelineResult(error="CV pipeline not available (OpenCV or Tesseract missing)")
+
+    result = PipelineResult()
+    total_start = time.time()
+
+    try:
+        # Stage 1: Render
+        t = time.time()
+        if pdf_data:
+            img = render_pdf_high_res(pdf_data, page_number, zoom)
+        elif image_data:
+            img = render_image_high_res(image_data)
+        else:
+            return PipelineResult(error="No input data (pdf_data or image_data required)")
+        result.stages['render'] = round(time.time() - t, 2)
+        result.image_width = img.shape[1]
+        result.image_height = img.shape[0]
+        logger.info(f"Stage 1 (render): {img.shape[1]}x{img.shape[0]} in {result.stages['render']}s")
+
+        # Stage 2: Deskew
+        t = time.time()
+        img, angle = deskew_image(img)
+        result.stages['deskew'] = round(time.time() - t, 2)
+        logger.info(f"Stage 2 (deskew): {angle:.2f}\u00b0 in {result.stages['deskew']}s")
+
+        # Stage 3: Dewarp
+        if enable_dewarp:
+            t = time.time()
+            img, _dewarp_info = dewarp_image(img)
+            result.stages['dewarp'] = round(time.time() - t, 2)
+
+        # Stage 4: Dual image preparation
+        t = time.time()
+        ocr_img = create_ocr_image(img)
+        layout_img = create_layout_image(img)
+        result.stages['image_prep'] = round(time.time() - t, 2)
+
+        # Stage 5: Layout analysis
+        t = time.time()
+        regions = analyze_layout(layout_img, ocr_img)
+        result.stages['layout'] = round(time.time() - t, 2)
+        result.columns_detected = len([r for r in regions if r.type.startswith('column')])
+        logger.info(f"Stage 5 (layout): {result.columns_detected} columns in {result.stages['layout']}s")
+
+        # Stage 6: Multi-pass OCR
+        t = time.time()
+        ocr_results = run_multi_pass_ocr(ocr_img, regions, lang)
+        result.stages['ocr'] = round(time.time() - t, 2)
+        total_words = sum(len(w) for w in ocr_results.values())
+        result.word_count = total_words
+        logger.info(f"Stage 6 (OCR): {total_words} words in {result.stages['ocr']}s")
+
+        # Stage 7: Line alignment
+        t = time.time()
+        vocab_rows = match_lines_to_vocab(ocr_results, regions)
+        result.stages['alignment'] = round(time.time() - t, 2)
+
+        # Stage 8: Optional LLM correction
+        if enable_llm_correction:
+            t = time.time()
+            vocab_rows = await llm_post_correct(img, vocab_rows)
+            result.stages['llm_correction'] = round(time.time() - t, 2)
+
+        # Convert to output format
+        result.vocabulary = [
+            {
+                "english": row.english,
+                "german": row.german,
+                "example": row.example,
+                "confidence": round(row.confidence, 1),
+            }
+            for row in vocab_rows
+            if row.english or row.german
+        ]
+
+        result.duration_seconds = round(time.time() - total_start, 2)
+        logger.info(f"CV Pipeline complete: {len(result.vocabulary)} entries in {result.duration_seconds}s")
+
+    except Exception as e:
+        logger.error(f"CV Pipeline error: {e}")
+        import traceback
+        logger.debug(traceback.format_exc())
+        result.error = str(e)
+        result.duration_seconds = round(time.time() - total_start, 2)
+
+    return result

klausur-service/backend/cv_review_spell.py

@@ -0,0 +1,315 @@
+"""
+CV Review Spell — Rule-based OCR spell correction (no LLM).
+
+Provides dictionary-backed digit-to-letter substitution, umlaut correction,
+general spell correction, merged-word splitting, and page-ref normalization.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import re
+import time
+from typing import Dict, List, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+try:
+    from spellchecker import SpellChecker as _SpellChecker
+    _en_spell = _SpellChecker(language='en', distance=1)
+    _de_spell = _SpellChecker(language='de', distance=1)
+    _SPELL_AVAILABLE = True
+    logger.info("pyspellchecker loaded (EN+DE)")
+except ImportError:
+    _SPELL_AVAILABLE = False
+    _en_spell = None  # type: ignore[assignment]
+    _de_spell = None  # type: ignore[assignment]
+    logger.warning("pyspellchecker not installed")
+
+
+# ---- Page-Ref Normalization ----
+# Normalizes OCR variants like "p-60", "p 61", "p60" -> "p.60"
+_PAGE_REF_RE = re.compile(r'\bp[\s\-]?(\d+)', re.IGNORECASE)
+
+
+def _normalize_page_ref(text: str) -> str:
+    """Normalize page references: 'p-60' / 'p 61' / 'p60' -> 'p.60'."""
+    if not text:
+        return text
+    return _PAGE_REF_RE.sub(lambda m: f"p.{m.group(1)}", text)
+
+
+# Suspicious OCR chars -> ordered list of most-likely correct replacements
+_SPELL_SUBS: Dict[str, List[str]] = {
+    '0': ['O', 'o'],
+    '1': ['l', 'I'],
+    '5': ['S', 's'],
+    '6': ['G', 'g'],
+    '8': ['B', 'b'],
+    '|': ['I', 'l', '1'],
+}
+_SPELL_SUSPICIOUS = frozenset(_SPELL_SUBS.keys())
+
+# Tokenizer: word tokens (letters + pipe) alternating with separators
+_SPELL_TOKEN_RE = re.compile(r'([A-Za-z\u00c4\u00d6\u00dc\u00e4\u00f6\u00fc\u00df|]+)([^A-Za-z\u00c4\u00d6\u00dc\u00e4\u00f6\u00fc\u00df|]*)')
+
+
+def _spell_dict_knows(word: str) -> bool:
+    """True if word is known in EN or DE dictionary."""
+    if not _SPELL_AVAILABLE:
+        return False
+    w = word.lower()
+    return bool(_en_spell.known([w])) or bool(_de_spell.known([w]))
+
+
+def _try_split_merged_word(token: str) -> Optional[str]:
+    """Try to split a merged word like 'atmyschool' into 'at my school'.
+
+    Uses dynamic programming to find the shortest sequence of dictionary
+    words that covers the entire token. Only returns a result when the
+    split produces at least 2 words and ALL parts are known dictionary words.
+
+    Preserves original capitalisation by mapping back to the input string.
+    """
+    if not _SPELL_AVAILABLE or len(token) < 4:
+        return None
+
+    lower = token.lower()
+    n = len(lower)
+
+    # dp[i] = (word_lengths_list, score) for best split of lower[:i], or None
+    dp: list = [None] * (n + 1)
+    dp[0] = ([], 0)
+
+    for i in range(1, n + 1):
+        for j in range(max(0, i - 20), i):
+            if dp[j] is None:
+                continue
+            candidate = lower[j:i]
+            word_len = i - j
+            if word_len == 1 and candidate not in ('a', 'i'):
+                continue
+            if _spell_dict_knows(candidate):
+                prev_words, prev_sq = dp[j]
+                new_words = prev_words + [word_len]
+                new_sq = prev_sq + word_len * word_len
+                new_key = (-len(new_words), new_sq)
+                if dp[i] is None:
+                    dp[i] = (new_words, new_sq)
+                else:
+                    old_key = (-len(dp[i][0]), dp[i][1])
+                    if new_key >= old_key:
+                        dp[i] = (new_words, new_sq)
+
+    if dp[n] is None or len(dp[n][0]) < 2:
+        return None
+
+    result = []
+    pos = 0
+    for wlen in dp[n][0]:
+        result.append(token[pos:pos + wlen])
+        pos += wlen
+
+    logger.debug("Split merged word: %r -> %r", token, " ".join(result))
+    return " ".join(result)
+
+
+def _spell_fix_token(token: str, field: str = "") -> Optional[str]:
+    """Return corrected form of token, or None if no fix needed/possible.
+
+    *field* is 'english' or 'german' -- used to pick the right dictionary.
+    """
+    has_suspicious = any(ch in _SPELL_SUSPICIOUS for ch in token)
+
+    # 1. Already known word -> no fix needed
+    if _spell_dict_knows(token):
+        return None
+
+    # 2. Digit/pipe substitution
+    if has_suspicious:
+        if token == '|':
+            return 'I'
+        for i, ch in enumerate(token):
+            if ch not in _SPELL_SUBS:
+                continue
+            for replacement in _SPELL_SUBS[ch]:
+                candidate = token[:i] + replacement + token[i + 1:]
+                if _spell_dict_knows(candidate):
+                    return candidate
+        first = token[0]
+        if first in _SPELL_SUBS and len(token) >= 2:
+            rest = token[1:]
+            if rest.isalpha() and rest.islower():
+                candidate = _SPELL_SUBS[first][0] + rest
+                if not candidate[0].isdigit():
+                    return candidate
+
+    # 3. OCR umlaut confusion
+    if len(token) >= 3 and token.isalpha() and field == "german":
+        _UMLAUT_SUBS = {'a': '\u00e4', 'o': '\u00f6', 'u': '\u00fc', 'i': '\u00fc',
+                         'A': '\u00c4', 'O': '\u00d6', 'U': '\u00dc', 'I': '\u00dc'}
+        for i, ch in enumerate(token):
+            if ch in _UMLAUT_SUBS:
+                candidate = token[:i] + _UMLAUT_SUBS[ch] + token[i + 1:]
+                if _spell_dict_knows(candidate):
+                    return candidate
+
+    # 4. General spell correction for unknown words (no digits/pipes)
+    if not has_suspicious and len(token) >= 3 and token.isalpha():
+        spell = _en_spell if field == "english" else _de_spell if field == "german" else None
+        if spell is not None:
+            correction = spell.correction(token.lower())
+            if correction and correction != token.lower():
+                if token[0].isupper():
+                    correction = correction[0].upper() + correction[1:]
+                if _spell_dict_knows(correction):
+                    return correction
+
+    # 5. Merged-word split
+    if len(token) >= 4 and token.isalpha():
+        split = _try_split_merged_word(token)
+        if split:
+            return split
+
+    return None
+
+
+def _spell_fix_field(text: str, field: str = "") -> Tuple[str, bool]:
+    """Apply OCR corrections to a text field. Returns (fixed_text, was_changed)."""
+    if not text:
+        return text, False
+    has_suspicious = any(ch in text for ch in _SPELL_SUSPICIOUS)
+    if not has_suspicious and not any(c.isalpha() for c in text):
+        return text, False
+    # Pattern: | immediately before . or , -> numbered list prefix
+    fixed = re.sub(r'(?<!\w)\|(?=[.,])', '1', text) if has_suspicious else text
+    changed = fixed != text
+    # Tokenize and fix word by word
+    parts: List[str] = []
+    pos = 0
+    for m in _SPELL_TOKEN_RE.finditer(fixed):
+        token, sep = m.group(1), m.group(2)
+        correction = _spell_fix_token(token, field=field)
+        if correction:
+            parts.append(correction)
+            changed = True
+        else:
+            parts.append(token)
+        parts.append(sep)
+        pos = m.end()
+    if pos < len(fixed):
+        parts.append(fixed[pos:])
+    return ''.join(parts), changed
+
+
+def spell_review_entries_sync(entries: List[Dict]) -> Dict:
+    """Rule-based OCR correction: spell-checker + structural heuristics.
+
+    Deterministic -- never translates, never touches IPA, never hallucinates.
+    Uses SmartSpellChecker for language-aware corrections with context-based
+    disambiguation (a/I), multi-digit substitution, and cross-language guard.
+    """
+    from cv_review_llm import _entry_needs_review
+
+    t0 = time.time()
+    changes: List[Dict] = []
+    all_corrected: List[Dict] = []
+
+    # Use SmartSpellChecker if available
+    _smart = None
+    try:
+        from smart_spell import SmartSpellChecker
+        _smart = SmartSpellChecker()
+        logger.debug("spell_review: using SmartSpellChecker")
+    except Exception:
+        logger.debug("spell_review: SmartSpellChecker not available, using legacy")
+
+    _LANG_MAP = {"english": "en", "german": "de", "example": "auto"}
+
+    for i, entry in enumerate(entries):
+        e = dict(entry)
+        # Page-ref normalization
+        old_ref = (e.get("source_page") or "").strip()
+        if old_ref:
+            new_ref = _normalize_page_ref(old_ref)
+            if new_ref != old_ref:
+                changes.append({
+                    "row_index": e.get("row_index", i),
+                    "field": "source_page",
+                    "old": old_ref,
+                    "new": new_ref,
+                })
+                e["source_page"] = new_ref
+                e["llm_corrected"] = True
+        if not _entry_needs_review(e):
+            all_corrected.append(e)
+            continue
+        for field_name in ("english", "german", "example"):
+            old_val = (e.get(field_name) or "").strip()
+            if not old_val:
+                continue
+
+            if _smart:
+                lang_code = _LANG_MAP.get(field_name, "en")
+                result = _smart.correct_text(old_val, lang=lang_code)
+                new_val = result.corrected
+                was_changed = result.changed
+            else:
+                lang = "german" if field_name in ("german", "example") else "english"
+                new_val, was_changed = _spell_fix_field(old_val, field=lang)
+
+            if was_changed and new_val != old_val:
+                changes.append({
+                    "row_index": e.get("row_index", i),
+                    "field": field_name,
+                    "old": old_val,
+                    "new": new_val,
+                })
+                e[field_name] = new_val
+                e["llm_corrected"] = True
+        all_corrected.append(e)
+    duration_ms = int((time.time() - t0) * 1000)
+    model_name = "smart-spell-checker" if _smart else "spell-checker"
+    return {
+        "entries_original": entries,
+        "entries_corrected": all_corrected,
+        "changes": changes,
+        "skipped_count": 0,
+        "model_used": model_name,
+        "duration_ms": duration_ms,
+    }
+
+
+async def spell_review_entries_streaming(entries: List[Dict], batch_size: int = 50):
+    """Async generator yielding SSE-compatible events for spell-checker review."""
+    total = len(entries)
+    yield {
+        "type": "meta",
+        "total_entries": total,
+        "to_review": total,
+        "skipped": 0,
+        "model": "spell-checker",
+        "batch_size": batch_size,
+    }
+    result = spell_review_entries_sync(entries)
+    changes = result["changes"]
+    yield {
+        "type": "batch",
+        "batch_index": 0,
+        "entries_reviewed": [e.get("row_index", i) for i, e in enumerate(entries)],
+        "changes": changes,
+        "duration_ms": result["duration_ms"],
+        "progress": {"current": total, "total": total},
+    }
+    yield {
+        "type": "complete",
+        "changes": changes,
+        "model_used": "spell-checker",
+        "duration_ms": result["duration_ms"],
+        "total_entries": total,
+        "reviewed": total,
+        "skipped": 0,
+        "corrections_found": len(changes),
+        "entries_corrected": result["entries_corrected"],
+    }

klausur-service/backend/grid_editor_columns.py

@@ -0,0 +1,492 @@
+"""
+Grid Editor — column detection, cross-column splitting, marker merging.
+
+Split from grid_editor_helpers.py for maintainability.
+All functions are pure computation — no HTTP, DB, or session side effects.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List, Optional
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Cross-column word splitting
+# ---------------------------------------------------------------------------
+
+_spell_cache: Optional[Any] = None
+_spell_loaded = False
+
+
+def _is_recognized_word(text: str) -> bool:
+    """Check if *text* is a recognized German or English word.
+
+    Uses the spellchecker library (same as cv_syllable_detect.py).
+    Returns True for real words like "oder", "Kabel", "Zeitung".
+    Returns False for OCR merge artifacts like "sichzie", "dasZimmer".
+    """
+    global _spell_cache, _spell_loaded
+    if not text or len(text) < 2:
+        return False
+
+    if not _spell_loaded:
+        _spell_loaded = True
+        try:
+            from spellchecker import SpellChecker
+            _spell_cache = SpellChecker(language="de")
+        except Exception:
+            pass
+
+    if _spell_cache is None:
+        return False
+
+    return text.lower() in _spell_cache
+
+
+def _split_cross_column_words(
+    words: List[Dict],
+    columns: List[Dict],
+) -> List[Dict]:
+    """Split word boxes that span across column boundaries.
+
+    When OCR merges adjacent words from different columns (e.g. "sichzie"
+    spanning Col 1 and Col 2, or "dasZimmer" crossing the boundary),
+    split the word box at the column boundary so each piece is assigned
+    to the correct column.
+
+    Only splits when:
+    - The word has significant overlap (>15% of its width) on both sides
+    - AND the word is not a recognized real word (OCR merge artifact), OR
+      the word contains a case transition (lowercase->uppercase) near the
+      boundary indicating two merged words like "dasZimmer".
+    """
+    if len(columns) < 2:
+        return words
+
+    # Column boundaries = midpoints between adjacent column edges
+    boundaries = []
+    for i in range(len(columns) - 1):
+        boundary = (columns[i]["x_max"] + columns[i + 1]["x_min"]) / 2
+        boundaries.append(boundary)
+
+    new_words: List[Dict] = []
+    split_count = 0
+    for w in words:
+        w_left = w["left"]
+        w_width = w["width"]
+        w_right = w_left + w_width
+        text = (w.get("text") or "").strip()
+
+        if not text or len(text) < 4 or w_width < 10:
+            new_words.append(w)
+            continue
+
+        # Find the first boundary this word straddles significantly
+        split_boundary = None
+        for b in boundaries:
+            if w_left < b < w_right:
+                left_part = b - w_left
+                right_part = w_right - b
+                # Both sides must have at least 15% of the word width
+                if left_part > w_width * 0.15 and right_part > w_width * 0.15:
+                    split_boundary = b
+                    break
+
+        if split_boundary is None:
+            new_words.append(w)
+            continue
+
+        # Compute approximate split position in the text.
+        left_width = split_boundary - w_left
+        split_ratio = left_width / w_width
+        approx_pos = len(text) * split_ratio
+
+        # Strategy 1: look for a case transition (lowercase->uppercase) near
+        # the approximate split point — e.g. "dasZimmer" splits at 'Z'.
+        split_char = None
+        search_lo = max(1, int(approx_pos) - 3)
+        search_hi = min(len(text), int(approx_pos) + 2)
+        for i in range(search_lo, search_hi):
+            if text[i - 1].islower() and text[i].isupper():
+                split_char = i
+                break
+
+        # Strategy 2: if no case transition, only split if the whole word
+        # is NOT a real word (i.e. it's an OCR merge artifact like "sichzie").
+        # Real words like "oder", "Kabel", "Zeitung" must not be split.
+        if split_char is None:
+            clean = re.sub(r"[,;:.!?]+$", "", text)  # strip trailing punct
+            if _is_recognized_word(clean):
+                new_words.append(w)
+                continue
+            # Not a real word — use floor of proportional position
+            split_char = max(1, min(len(text) - 1, int(approx_pos)))
+
+        left_text = text[:split_char].rstrip()
+        right_text = text[split_char:].lstrip()
+
+        if len(left_text) < 2 or len(right_text) < 2:
+            new_words.append(w)
+            continue
+
+        right_width = w_width - round(left_width)
+        new_words.append({
+            **w,
+            "text": left_text,
+            "width": round(left_width),
+        })
+        new_words.append({
+            **w,
+            "text": right_text,
+            "left": round(split_boundary),
+            "width": right_width,
+        })
+        split_count += 1
+        logger.info(
+            "split cross-column word %r -> %r + %r at boundary %.0f",
+            text, left_text, right_text, split_boundary,
+        )
+
+    if split_count:
+        logger.info("split %d cross-column word(s)", split_count)
+    return new_words
+
+
+def _cluster_columns_by_alignment(
+    words: List[Dict],
+    zone_w: int,
+    rows: List[Dict],
+) -> List[Dict[str, Any]]:
+    """Detect columns by clustering left-edge alignment across rows.
+
+    Hybrid approach:
+      1. Group words by row, find "group start" positions within each row
+         (words preceded by a large gap or first word in row)
+      2. Cluster group-start left-edges by X-proximity across rows
+      3. Filter by row coverage (how many rows have a group start here)
+      4. Merge nearby clusters
+      5. Build column boundaries
+
+    This filters out mid-phrase word positions (e.g. IPA transcriptions,
+    second words in multi-word entries) by only considering positions
+    where a new word group begins within a row.
+    """
+    if not words or not rows:
+        return []
+
+    total_rows = len(rows)
+    if total_rows == 0:
+        return []
+
+    # --- Group words by row ---
+    row_words: Dict[int, List[Dict]] = {}
+    for w in words:
+        y_center = w["top"] + w["height"] / 2
+        best = min(rows, key=lambda r: abs(r["y_center"] - y_center))
+        row_words.setdefault(best["index"], []).append(w)
+
+    # --- Compute adaptive gap threshold for group-start detection ---
+    all_gaps: List[float] = []
+    for ri, rw_list in row_words.items():
+        sorted_rw = sorted(rw_list, key=lambda w: w["left"])
+        for i in range(len(sorted_rw) - 1):
+            right = sorted_rw[i]["left"] + sorted_rw[i]["width"]
+            gap = sorted_rw[i + 1]["left"] - right
+            if gap > 0:
+                all_gaps.append(gap)
+
+    if all_gaps:
+        sorted_gaps = sorted(all_gaps)
+        median_gap = sorted_gaps[len(sorted_gaps) // 2]
+        heights = [w["height"] for w in words if w.get("height", 0) > 0]
+        median_h = sorted(heights)[len(heights) // 2] if heights else 25
+
+        # For small word counts (boxes, sub-zones): PaddleOCR returns
+        # multi-word blocks, so ALL inter-word gaps are potential column
+        # boundaries.  Use a low threshold based on word height — any gap
+        # wider than ~1x median word height is a column separator.
+        if len(words) <= 60:
+            gap_threshold = max(median_h * 1.0, 25)
+            logger.info(
+                "alignment columns (small zone): gap_threshold=%.0f "
+                "(median_h=%.0f, %d words, %d gaps: %s)",
+                gap_threshold, median_h, len(words), len(sorted_gaps),
+                [int(g) for g in sorted_gaps[:10]],
+            )
+        else:
+            # Standard approach for large zones (full pages)
+            gap_threshold = max(median_gap * 3, median_h * 1.5, 30)
+            # Cap at 25% of zone width
+            max_gap = zone_w * 0.25
+            if gap_threshold > max_gap > 30:
+                logger.info("alignment columns: capping gap_threshold %.0f -> %.0f (25%% of zone_w=%d)", gap_threshold, max_gap, zone_w)
+                gap_threshold = max_gap
+    else:
+        gap_threshold = 50
+
+    # --- Find group-start positions (left-edges that begin a new column) ---
+    start_positions: List[tuple] = []  # (left_edge, row_index)
+    for ri, rw_list in row_words.items():
+        sorted_rw = sorted(rw_list, key=lambda w: w["left"])
+        # First word in row is always a group start
+        start_positions.append((sorted_rw[0]["left"], ri))
+        for i in range(1, len(sorted_rw)):
+            right_prev = sorted_rw[i - 1]["left"] + sorted_rw[i - 1]["width"]
+            gap = sorted_rw[i]["left"] - right_prev
+            if gap >= gap_threshold:
+                start_positions.append((sorted_rw[i]["left"], ri))
+
+    start_positions.sort(key=lambda x: x[0])
+
+    logger.info(
+        "alignment columns: %d group-start positions from %d words "
+        "(gap_threshold=%.0f, %d rows)",
+        len(start_positions), len(words), gap_threshold, total_rows,
+    )
+
+    if not start_positions:
+        x_min = min(w["left"] for w in words)
+        x_max = max(w["left"] + w["width"] for w in words)
+        return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
+
+    # --- Cluster group-start positions by X-proximity ---
+    tolerance = max(10, int(zone_w * 0.01))
+    clusters: List[Dict[str, Any]] = []
+    cur_edges = [start_positions[0][0]]
+    cur_rows = {start_positions[0][1]}
+
+    for left, row_idx in start_positions[1:]:
+        if left - cur_edges[-1] <= tolerance:
+            cur_edges.append(left)
+            cur_rows.add(row_idx)
+        else:
+            clusters.append({
+                "mean_x": int(sum(cur_edges) / len(cur_edges)),
+                "min_edge": min(cur_edges),
+                "max_edge": max(cur_edges),
+                "count": len(cur_edges),
+                "distinct_rows": len(cur_rows),
+                "row_coverage": len(cur_rows) / total_rows,
+            })
+            cur_edges = [left]
+            cur_rows = {row_idx}
+    clusters.append({
+        "mean_x": int(sum(cur_edges) / len(cur_edges)),
+        "min_edge": min(cur_edges),
+        "max_edge": max(cur_edges),
+        "count": len(cur_edges),
+        "distinct_rows": len(cur_rows),
+        "row_coverage": len(cur_rows) / total_rows,
+    })
+
+    # --- Filter by row coverage ---
+    # These thresholds must be high enough to avoid false columns in flowing
+    # text (random inter-word gaps) while still detecting real columns in
+    # vocabulary worksheets (which typically have >80% row coverage).
+    MIN_COVERAGE_PRIMARY = 0.35
+    MIN_COVERAGE_SECONDARY = 0.12
+    MIN_WORDS_SECONDARY = 4
+    MIN_DISTINCT_ROWS = 3
+
+    # Content boundary for left-margin detection
+    content_x_min = min(w["left"] for w in words)
+    content_x_max = max(w["left"] + w["width"] for w in words)
+    content_span = content_x_max - content_x_min
+
+    primary = [
+        c for c in clusters
+        if c["row_coverage"] >= MIN_COVERAGE_PRIMARY
+        and c["distinct_rows"] >= MIN_DISTINCT_ROWS
+    ]
+    primary_ids = {id(c) for c in primary}
+    secondary = [
+        c for c in clusters
+        if id(c) not in primary_ids
+        and c["row_coverage"] >= MIN_COVERAGE_SECONDARY
+        and c["count"] >= MIN_WORDS_SECONDARY
+        and c["distinct_rows"] >= MIN_DISTINCT_ROWS
+    ]
+
+    # Tertiary: narrow left-margin columns (page refs, markers) that have
+    # too few rows for secondary but are clearly left-aligned and separated
+    # from the main content.  These appear at the far left or far right and
+    # have a large gap to the nearest significant cluster.
+    used_ids = {id(c) for c in primary} | {id(c) for c in secondary}
+    sig_xs = [c["mean_x"] for c in primary + secondary]
+
+    # Tertiary: clusters that are clearly to the LEFT of the first
+    # significant column (or RIGHT of the last).  If words consistently
+    # start at a position left of the established first column boundary,
+    # they MUST be a separate column — regardless of how few rows they
+    # cover.  The only requirement is a clear spatial gap.
+    MIN_COVERAGE_TERTIARY = 0.02  # at least 1 row effectively
+    tertiary = []
+    for c in clusters:
+        if id(c) in used_ids:
+            continue
+        if c["distinct_rows"] < 1:
+            continue
+        if c["row_coverage"] < MIN_COVERAGE_TERTIARY:
+            continue
+        # Must be near left or right content margin (within 15%)
+        rel_pos = (c["mean_x"] - content_x_min) / content_span if content_span else 0.5
+        if not (rel_pos < 0.15 or rel_pos > 0.85):
+            continue
+        # Must have significant gap to nearest significant cluster
+        if sig_xs:
+            min_dist = min(abs(c["mean_x"] - sx) for sx in sig_xs)
+            if min_dist < max(30, content_span * 0.02):
+                continue
+        tertiary.append(c)
+
+    if tertiary:
+        for c in tertiary:
+            logger.info(
+                "  tertiary (margin) cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
+                c["mean_x"], c["min_edge"], c["max_edge"],
+                c["count"], c["distinct_rows"], c["row_coverage"] * 100,
+            )
+
+    significant = sorted(primary + secondary + tertiary, key=lambda c: c["mean_x"])
+
+    for c in significant:
+        logger.info(
+            "  significant cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
+            c["mean_x"], c["min_edge"], c["max_edge"],
+            c["count"], c["distinct_rows"], c["row_coverage"] * 100,
+        )
+    logger.info(
+        "alignment columns: %d clusters, %d primary, %d secondary -> %d significant",
+        len(clusters), len(primary), len(secondary), len(significant),
+    )
+
+    if not significant:
+        # Fallback: single column covering all content
+        x_min = min(w["left"] for w in words)
+        x_max = max(w["left"] + w["width"] for w in words)
+        return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
+
+    # --- Merge nearby clusters ---
+    merge_distance = max(25, int(zone_w * 0.03))
+    merged = [significant[0].copy()]
+    for s in significant[1:]:
+        if s["mean_x"] - merged[-1]["mean_x"] < merge_distance:
+            prev = merged[-1]
+            total = prev["count"] + s["count"]
+            prev["mean_x"] = (
+                prev["mean_x"] * prev["count"] + s["mean_x"] * s["count"]
+            ) // total
+            prev["count"] = total
+            prev["min_edge"] = min(prev["min_edge"], s["min_edge"])
+            prev["max_edge"] = max(prev["max_edge"], s["max_edge"])
+            prev["distinct_rows"] = max(prev["distinct_rows"], s["distinct_rows"])
+        else:
+            merged.append(s.copy())
+
+    logger.info(
+        "alignment columns: %d after merge (distance=%d)",
+        len(merged), merge_distance,
+    )
+
+    # --- Build column boundaries ---
+    margin = max(5, int(zone_w * 0.005))
+    content_x_min = min(w["left"] for w in words)
+    content_x_max = max(w["left"] + w["width"] for w in words)
+
+    columns: List[Dict[str, Any]] = []
+    for i, cluster in enumerate(merged):
+        x_min = max(content_x_min, cluster["min_edge"] - margin)
+        if i + 1 < len(merged):
+            x_max = merged[i + 1]["min_edge"] - margin
+        else:
+            x_max = content_x_max
+
+        columns.append({
+            "index": i,
+            "type": f"column_{i + 1}" if len(merged) > 1 else "column_text",
+            "x_min": x_min,
+            "x_max": x_max,
+        })
+
+    return columns
+
+
+_MARKER_CHARS = set("*-+#>")
+
+
+def _merge_inline_marker_columns(
+    columns: List[Dict],
+    words: List[Dict],
+) -> List[Dict]:
+    """Merge narrow marker columns (bullets, numbering) into adjacent text.
+
+    Bullet points (*, -) and numbering (1., 2.) create narrow columns
+    at the left edge of a zone.  These are inline markers that indent text,
+    not real separate columns.  Merge them with their right neighbour.
+
+    Does NOT merge columns containing alphabetic words like "to", "in",
+    "der", "die", "das" — those are legitimate content columns.
+    """
+    if len(columns) < 2:
+        return columns
+
+    merged: List[Dict] = []
+    skip: set = set()
+
+    for i, col in enumerate(columns):
+        if i in skip:
+            continue
+
+        # Find words in this column
+        col_words = [
+            w for w in words
+            if col["x_min"] <= w["left"] + w["width"] / 2 < col["x_max"]
+        ]
+        col_width = col["x_max"] - col["x_min"]
+
+        # Narrow column with mostly short words -> MIGHT be inline markers
+        if col_words and col_width < 80:
+            avg_len = sum(len(w.get("text", "")) for w in col_words) / len(col_words)
+            if avg_len <= 2 and i + 1 < len(columns):
+                # Check if words are actual markers (symbols/numbers) vs
+                # real alphabetic words like "to", "in", "der", "die"
+                texts = [(w.get("text") or "").strip() for w in col_words]
+                alpha_count = sum(
+                    1 for t in texts
+                    if t and t[0].isalpha() and t not in _MARKER_CHARS
+                )
+                alpha_ratio = alpha_count / len(texts) if texts else 0
+
+                # If >=50% of words are alphabetic, this is a real column
+                if alpha_ratio >= 0.5:
+                    logger.info(
+                        "  kept narrow column %d (w=%d, avg_len=%.1f, "
+                        "alpha=%.0f%%) -- contains real words",
+                        i, col_width, avg_len, alpha_ratio * 100,
+                    )
+                else:
+                    # Merge into next column
+                    next_col = columns[i + 1].copy()
+                    next_col["x_min"] = col["x_min"]
+                    merged.append(next_col)
+                    skip.add(i + 1)
+                    logger.info(
+                        "  merged inline marker column %d (w=%d, avg_len=%.1f) "
+                        "into column %d",
+                        i, col_width, avg_len, i + 1,
+                    )
+                    continue
+
+        merged.append(col)
+
+    # Re-index
+    for i, col in enumerate(merged):
+        col["index"] = i
+        col["type"] = f"column_{i + 1}" if len(merged) > 1 else "column_text"
+
+    return merged

klausur-service/backend/grid_editor_filters.py

@@ -0,0 +1,402 @@
+"""
+Grid Editor — word/zone filtering, border ghosts, decorative margins, footers.
+
+Split from grid_editor_helpers.py for maintainability.
+All functions are pure computation — no HTTP, DB, or session side effects.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+from typing import Any, Dict, List, Optional, Tuple
+
+logger = logging.getLogger(__name__)
+
+
+def _filter_border_strip_words(words: List[Dict]) -> Tuple[List[Dict], int]:
+    """Remove page-border decoration strip words BEFORE column detection.
+
+    Scans from each page edge inward to find the first significant x-gap
+    (>30 px).  If the edge cluster contains <15 % of total words, those
+    words are removed as border-strip artifacts (alphabet letters,
+    illustration fragments).
+
+    Must run BEFORE ``_build_zone_grid`` so that column detection only
+    sees real content words and doesn't produce inflated row counts.
+    """
+    if len(words) < 10:
+        return words, 0
+
+    sorted_words = sorted(words, key=lambda w: w.get("left", 0))
+    total = len(sorted_words)
+
+    # -- Left-edge scan (running max right-edge) --
+    left_count = 0
+    running_right = 0
+    for gi in range(total - 1):
+        running_right = max(
+            running_right,
+            sorted_words[gi].get("left", 0) + sorted_words[gi].get("width", 0),
+        )
+        if sorted_words[gi + 1].get("left", 0) - running_right > 30:
+            left_count = gi + 1
+            break
+
+    # -- Right-edge scan (running min left) --
+    right_count = 0
+    running_left = sorted_words[-1].get("left", 0)
+    for gi in range(total - 1, 0, -1):
+        running_left = min(running_left, sorted_words[gi].get("left", 0))
+        prev_right = (
+            sorted_words[gi - 1].get("left", 0)
+            + sorted_words[gi - 1].get("width", 0)
+        )
+        if running_left - prev_right > 30:
+            right_count = total - gi
+            break
+
+    # Validate candidate strip: real border decorations are mostly short
+    # words (alphabet letters like "A", "Bb", stray marks).  Multi-word
+    # content like "der Ranzen" or "die Schals" (continuation of German
+    # translations) must NOT be removed.
+    def _is_decorative_strip(candidates: List[Dict]) -> bool:
+        if not candidates:
+            return False
+        short = sum(1 for w in candidates if len((w.get("text") or "").strip()) <= 2)
+        return short / len(candidates) >= 0.45
+
+    strip_ids: set = set()
+    if left_count > 0 and left_count / total < 0.20:
+        candidates = sorted_words[:left_count]
+        if _is_decorative_strip(candidates):
+            strip_ids = {id(w) for w in candidates}
+    elif right_count > 0 and right_count / total < 0.20:
+        candidates = sorted_words[total - right_count:]
+        if _is_decorative_strip(candidates):
+            strip_ids = {id(w) for w in candidates}
+
+    if not strip_ids:
+        return words, 0
+
+    return [w for w in words if id(w) not in strip_ids], len(strip_ids)
+
+
+# Characters that are typically OCR artefacts from box border lines.
+# Intentionally excludes ! (red markers) and . , ; (real punctuation).
+_GRID_GHOST_CHARS = set("|1lI[](){}/\\-\u2014\u2013_~=+")
+
+
+def _filter_border_ghosts(
+    words: List[Dict],
+    boxes: List,
+) -> tuple:
+    """Remove words sitting on box borders that are OCR artefacts.
+
+    Returns (filtered_words, removed_count).
+    """
+    if not boxes or not words:
+        return words, 0
+
+    # Build border bands from detected boxes
+    x_bands: List[tuple] = []
+    y_bands: List[tuple] = []
+    for b in boxes:
+        bt = (
+            b.border_thickness
+            if hasattr(b, "border_thickness")
+            else b.get("border_thickness", 3)
+        )
+        # Skip borderless boxes (images/graphics) -- no border line to produce ghosts
+        if bt == 0:
+            continue
+        bx = b.x if hasattr(b, "x") else b.get("x", 0)
+        by = b.y if hasattr(b, "y") else b.get("y", 0)
+        bw = b.width if hasattr(b, "width") else b.get("w", b.get("width", 0))
+        bh = b.height if hasattr(b, "height") else b.get("h", b.get("height", 0))
+        margin = max(bt * 2, 10) + 6
+        x_bands.append((bx - margin, bx + margin))
+        x_bands.append((bx + bw - margin, bx + bw + margin))
+        y_bands.append((by - margin, by + margin))
+        y_bands.append((by + bh - margin, by + bh + margin))
+
+    def _is_ghost(w: Dict) -> bool:
+        text = (w.get("text") or "").strip()
+        if not text:
+            return False
+        # Check if any word edge (not just center) touches a border band
+        w_left = w["left"]
+        w_right = w["left"] + w["width"]
+        w_top = w["top"]
+        w_bottom = w["top"] + w["height"]
+        on_border = (
+            any(lo <= w_left <= hi or lo <= w_right <= hi for lo, hi in x_bands)
+            or any(lo <= w_top <= hi or lo <= w_bottom <= hi for lo, hi in y_bands)
+        )
+        if not on_border:
+            return False
+        if len(text) == 1 and text in _GRID_GHOST_CHARS:
+            return True
+        return False
+
+    filtered = [w for w in words if not _is_ghost(w)]
+    return filtered, len(words) - len(filtered)
+
+
+def _flatten_word_boxes(cells: List[Dict]) -> List[Dict]:
+    """Extract all word_boxes from cells into a flat list of word dicts."""
+    words: List[Dict] = []
+    for cell in cells:
+        for wb in cell.get("word_boxes") or []:
+            if wb.get("text", "").strip():
+                words.append({
+                    "text": wb["text"],
+                    "left": wb["left"],
+                    "top": wb["top"],
+                    "width": wb["width"],
+                    "height": wb["height"],
+                    "conf": wb.get("conf", 0),
+                })
+    return words
+
+
+def _words_in_zone(
+    words: List[Dict],
+    zone_y: int,
+    zone_h: int,
+    zone_x: int,
+    zone_w: int,
+) -> List[Dict]:
+    """Filter words whose Y-center falls within a zone's bounds."""
+    zone_y_end = zone_y + zone_h
+    zone_x_end = zone_x + zone_w
+    result = []
+    for w in words:
+        cy = w["top"] + w["height"] / 2
+        cx = w["left"] + w["width"] / 2
+        if zone_y <= cy <= zone_y_end and zone_x <= cx <= zone_x_end:
+            result.append(w)
+    return result
+
+
+def _get_content_bounds(words: List[Dict]) -> tuple:
+    """Get content bounds from word positions."""
+    if not words:
+        return 0, 0, 0, 0
+    x_min = min(w["left"] for w in words)
+    y_min = min(w["top"] for w in words)
+    x_max = max(w["left"] + w["width"] for w in words)
+    y_max = max(w["top"] + w["height"] for w in words)
+    return x_min, y_min, x_max - x_min, y_max - y_min
+
+
+def _filter_decorative_margin(
+    words: List[Dict],
+    img_w: int,
+    log: Any,
+    session_id: str,
+) -> Dict[str, Any]:
+    """Remove words that belong to a decorative alphabet strip on a margin.
+
+    Some vocabulary worksheets have a vertical A-Z alphabet graphic along
+    the left or right edge.  OCR reads each letter as an isolated single-
+    character word.  These decorative elements are not content and confuse
+    column/row detection.
+
+    Detection criteria (phase 1 -- find the strip using single-char words):
+      - Words are in the outer 30% of the page (left or right)
+      - Nearly all words are single characters (letters or digits)
+      - At least 8 such words form a vertical strip (>=8 unique Y positions)
+      - Average horizontal spread of the strip is small (< 80px)
+
+    Phase 2 -- once a strip is confirmed, also remove any short word (<=3
+    chars) in the same narrow x-range.  This catches multi-char OCR
+    artifacts like "Vv" that belong to the same decorative element.
+
+    Modifies *words* in place.
+
+    Returns:
+        Dict with 'found' (bool), 'side' (str), 'letters_detected' (int).
+    """
+    no_strip: Dict[str, Any] = {"found": False, "side": "", "letters_detected": 0}
+    if not words or img_w <= 0:
+        return no_strip
+
+    margin_cutoff = img_w * 0.30
+    # Phase 1: find candidate strips using short words (1-2 chars).
+    # OCR often reads alphabet sidebar letters as pairs ("Aa", "Bb")
+    # rather than singles, so accept <=2-char words as strip candidates.
+    left_strip = [
+        w for w in words
+        if len((w.get("text") or "").strip()) <= 2
+        and w["left"] + w.get("width", 0) / 2 < margin_cutoff
+    ]
+    right_strip = [
+        w for w in words
+        if len((w.get("text") or "").strip()) <= 2
+        and w["left"] + w.get("width", 0) / 2 > img_w - margin_cutoff
+    ]
+
+    for strip, side in [(left_strip, "left"), (right_strip, "right")]:
+        if len(strip) < 6:
+            continue
+        # Check vertical distribution: should have many distinct Y positions
+        y_centers = sorted(set(
+            int(w["top"] + w.get("height", 0) / 2) // 20 * 20  # bucket
+            for w in strip
+        ))
+        if len(y_centers) < 6:
+            continue
+        # Check horizontal compactness
+        x_positions = [w["left"] for w in strip]
+        x_min = min(x_positions)
+        x_max = max(x_positions)
+        x_spread = x_max - x_min
+        if x_spread > 80:
+            continue
+
+        # Phase 2: strip confirmed -- also collect short words in same x-range
+        # Expand x-range slightly to catch neighbors (e.g. "Vv" next to "U")
+        strip_x_lo = x_min - 20
+        strip_x_hi = x_max + 60  # word width + tolerance
+        all_strip_words = [
+            w for w in words
+            if len((w.get("text") or "").strip()) <= 3
+            and strip_x_lo <= w["left"] <= strip_x_hi
+            and (w["left"] + w.get("width", 0) / 2 < margin_cutoff
+                 if side == "left"
+                 else w["left"] + w.get("width", 0) / 2 > img_w - margin_cutoff)
+        ]
+
+        strip_set = set(id(w) for w in all_strip_words)
+        before = len(words)
+        words[:] = [w for w in words if id(w) not in strip_set]
+        removed = before - len(words)
+        if removed:
+            log.info(
+                "build-grid session %s: removed %d decorative %s-margin words "
+                "(strip x=%d-%d)",
+                session_id, removed, side, strip_x_lo, strip_x_hi,
+            )
+        return {"found": True, "side": side, "letters_detected": len(strip)}
+
+    return no_strip
+
+
+def _filter_footer_words(
+    words: List[Dict],
+    img_h: int,
+    log: Any,
+    session_id: str,
+) -> Optional[Dict]:
+    """Remove isolated words in the bottom 5% of the page (page numbers).
+
+    Modifies *words* in place and returns a page_number metadata dict
+    if a page number was extracted, or None.
+    """
+    if not words or img_h <= 0:
+        return None
+    footer_y = img_h * 0.95
+    footer_words = [
+        w for w in words
+        if w["top"] + w.get("height", 0) / 2 > footer_y
+    ]
+    if not footer_words:
+        return None
+    # Only remove if footer has very few words (<= 3) with short text
+    total_text = "".join((w.get("text") or "").strip() for w in footer_words)
+    if len(footer_words) <= 3 and len(total_text) <= 10:
+        # Extract page number metadata before removing
+        page_number_info = {
+            "text": total_text.strip(),
+            "y_pct": round(footer_words[0]["top"] / img_h * 100, 1),
+        }
+        # Try to parse as integer
+        digits = "".join(c for c in total_text if c.isdigit())
+        if digits:
+            page_number_info["number"] = int(digits)
+
+        footer_set = set(id(w) for w in footer_words)
+        words[:] = [w for w in words if id(w) not in footer_set]
+        log.info(
+            "build-grid session %s: extracted page number '%s' and removed %d footer words",
+            session_id, total_text, len(footer_words),
+        )
+        return page_number_info
+    return None
+
+
+def _filter_header_junk(
+    words: List[Dict],
+    img_h: int,
+    log: Any,
+    session_id: str,
+) -> None:
+    """Remove OCR junk from header illustrations above the real content.
+
+    Textbook pages often have decorative header graphics (illustrations,
+    icons) that OCR reads as low-confidence junk characters.  Real content
+    typically starts further down the page.
+
+    Algorithm:
+      1. Find the "content start" -- the first Y position where a dense
+         horizontal row of 3+ high-confidence words begins.
+      2. Above that line, remove words with conf < 75 and text <= 3 chars.
+         These are almost certainly OCR artifacts from illustrations.
+
+    Modifies *words* in place.
+    """
+    if not words or img_h <= 0:
+        return
+
+    # --- Find content start: first horizontal row with >=3 high-conf words ---
+    # Sort words by Y
+    sorted_by_y = sorted(words, key=lambda w: w["top"])
+    content_start_y = 0
+    _ROW_TOLERANCE = img_h * 0.02  # words within 2% of page height = same row
+    _MIN_ROW_WORDS = 3
+    _MIN_CONF = 80
+
+    i = 0
+    while i < len(sorted_by_y):
+        row_y = sorted_by_y[i]["top"]
+        # Collect words in this row band
+        row_words = []
+        j = i
+        while j < len(sorted_by_y) and sorted_by_y[j]["top"] - row_y < _ROW_TOLERANCE:
+            row_words.append(sorted_by_y[j])
+            j += 1
+        # Count high-confidence words with real text (> 1 char)
+        high_conf = [
+            w for w in row_words
+            if w.get("conf", 0) >= _MIN_CONF
+            and len((w.get("text") or "").strip()) > 1
+        ]
+        if len(high_conf) >= _MIN_ROW_WORDS:
+            content_start_y = row_y
+            break
+        i = j if j > i else i + 1
+
+    if content_start_y <= 0:
+        return  # no clear content start found
+
+    # --- Remove low-conf short junk above content start ---
+    junk = [
+        w for w in words
+        if w["top"] + w.get("height", 0) < content_start_y
+        and w.get("conf", 0) < 75
+        and len((w.get("text") or "").strip()) <= 3
+    ]
+    if not junk:
+        return
+
+    junk_set = set(id(w) for w in junk)
+    before = len(words)
+    words[:] = [w for w in words if id(w) not in junk_set]
+    removed = before - len(words)
+    if removed:
+        log.info(
+            "build-grid session %s: removed %d header junk words above y=%d "
+            "(content start)",
+            session_id, removed, content_start_y,
+        )

klausur-service/backend/grid_editor_headers.py

@@ -0,0 +1,499 @@
+"""
+Grid Editor — header/heading detection and colspan (merged cell) detection.
+Split from grid_editor_helpers.py.  Pure computation, no HTTP/DB side effects.
+Lizenz: Apache 2.0 | DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List, Optional
+
+from cv_ocr_engines import _text_has_garbled_ipa
+
+logger = logging.getLogger(__name__)
+
+
+def _detect_heading_rows_by_color(zones_data: List[Dict], img_w: int, img_h: int) -> int:
+    """Detect heading rows by color + height after color annotation.
+
+    A row is a heading if:
+    1. ALL word_boxes have color_name != 'black' (typically 'blue')
+    2. Mean word height > 1.2x median height of all words in the zone
+
+    Detected heading rows are merged into a single spanning cell.
+    Returns count of headings detected.
+    """
+    heading_count = 0
+
+    for z in zones_data:
+        cells = z.get("cells", [])
+        rows = z.get("rows", [])
+        columns = z.get("columns", [])
+        if not cells or not rows or len(columns) < 2:
+            continue
+
+        # Compute median word height across the zone
+        all_heights = []
+        for cell in cells:
+            for wb in cell.get("word_boxes") or []:
+                h = wb.get("height", 0)
+                if h > 0:
+                    all_heights.append(h)
+        if not all_heights:
+            continue
+        all_heights_sorted = sorted(all_heights)
+        median_h = all_heights_sorted[len(all_heights_sorted) // 2]
+
+        heading_row_indices = []
+        for row in rows:
+            if row.get("is_header"):
+                continue  # already detected as header
+            ri = row["index"]
+            row_cells = [c for c in cells if c.get("row_index") == ri]
+            row_wbs = [
+                wb for cell in row_cells
+                for wb in cell.get("word_boxes") or []
+            ]
+            if not row_wbs:
+                continue
+
+            # Condition 1: ALL words are non-black
+            all_colored = all(
+                wb.get("color_name", "black") != "black"
+                for wb in row_wbs
+            )
+            if not all_colored:
+                continue
+
+            # Condition 2: mean height > 1.2x median
+            mean_h = sum(wb.get("height", 0) for wb in row_wbs) / len(row_wbs)
+            if mean_h <= median_h * 1.2:
+                continue
+
+            heading_row_indices.append(ri)
+
+        # Merge heading cells into spanning cells
+        for hri in heading_row_indices:
+            header_cells = [c for c in cells if c.get("row_index") == hri]
+            if len(header_cells) <= 1:
+                # Single cell -- just mark it as heading
+                if header_cells:
+                    header_cells[0]["col_type"] = "heading"
+                    heading_count += 1
+                    # Mark row as header
+                    for row in rows:
+                        if row["index"] == hri:
+                            row["is_header"] = True
+                continue
+
+            # Collect all word_boxes and text from all columns
+            all_wb = []
+            all_text_parts = []
+            for hc in sorted(header_cells, key=lambda c: c["col_index"]):
+                all_wb.extend(hc.get("word_boxes", []))
+                if hc.get("text", "").strip():
+                    all_text_parts.append(hc["text"].strip())
+
+            # Remove all cells for this row, replace with one spanning cell
+            z["cells"] = [c for c in z["cells"] if c.get("row_index") != hri]
+
+            if all_wb:
+                x_min = min(wb["left"] for wb in all_wb)
+                y_min = min(wb["top"] for wb in all_wb)
+                x_max = max(wb["left"] + wb["width"] for wb in all_wb)
+                y_max = max(wb["top"] + wb["height"] for wb in all_wb)
+
+                # Use the actual starting col_index from the first cell
+                first_col = min(hc["col_index"] for hc in header_cells)
+                zone_idx = z.get("zone_index", 0)
+                z["cells"].append({
+                    "cell_id": f"Z{zone_idx}_R{hri:02d}_C{first_col}",
+                    "zone_index": zone_idx,
+                    "row_index": hri,
+                    "col_index": first_col,
+                    "col_type": "heading",
+                    "text": " ".join(all_text_parts),
+                    "confidence": 0.0,
+                    "bbox_px": {"x": x_min, "y": y_min,
+                                "w": x_max - x_min, "h": y_max - y_min},
+                    "bbox_pct": {
+                        "x": round(x_min / img_w * 100, 2) if img_w else 0,
+                        "y": round(y_min / img_h * 100, 2) if img_h else 0,
+                        "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
+                        "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
+                    },
+                    "word_boxes": all_wb,
+                    "ocr_engine": "words_first",
+                    "is_bold": True,
+                })
+
+            # Mark row as header
+            for row in rows:
+                if row["index"] == hri:
+                    row["is_header"] = True
+            heading_count += 1
+
+    return heading_count
+
+
+def _detect_heading_rows_by_single_cell(
+    zones_data: List[Dict], img_w: int, img_h: int,
+) -> int:
+    """Detect heading rows that have only a single content cell.
+
+    Black headings like "Theme" have normal color and height, so they are
+    missed by ``_detect_heading_rows_by_color``.  The distinguishing signal
+    is that they occupy only one column while normal vocabulary rows fill
+    at least 2-3 columns.
+
+    A row qualifies as a heading if:
+    1. It is not already marked as a header/heading.
+    2. It has exactly ONE cell whose col_type starts with ``column_``
+       (excluding column_1 / page_ref which only carries page numbers).
+    3. That single cell is NOT in the last column (continuation/example
+       lines like "2. Ver\u00e4nderung, Wechsel" often sit alone in column_4).
+    4. The text does not start with ``[`` (IPA continuation).
+    5. The zone has >=3 columns and >=5 rows (avoids false positives in
+       tiny zones).
+    6. The majority of rows in the zone have >=2 content cells (ensures
+       we are in a multi-column vocab layout).
+    """
+    heading_count = 0
+
+    for z in zones_data:
+        cells = z.get("cells", [])
+        rows = z.get("rows", [])
+        columns = z.get("columns", [])
+        if len(columns) < 3 or len(rows) < 5:
+            continue
+
+        # Determine the last col_index (example/sentence column)
+        col_indices = sorted(set(c.get("col_index", 0) for c in cells))
+        if not col_indices:
+            continue
+        last_col = col_indices[-1]
+
+        # Count content cells per row (column_* but not column_1/page_ref).
+        # Exception: column_1 cells that contain a dictionary article word
+        # (die/der/das etc.) ARE content -- they appear in dictionary layouts
+        # where the leftmost column holds grammatical articles.
+        _ARTICLE_WORDS = {
+            "die", "der", "das", "dem", "den", "des", "ein", "eine",
+            "the", "a", "an",
+        }
+        row_content_counts: Dict[int, int] = {}
+        for cell in cells:
+            ct = cell.get("col_type", "")
+            if not ct.startswith("column_"):
+                continue
+            if ct == "column_1":
+                ctext = (cell.get("text") or "").strip().lower()
+                if ctext not in _ARTICLE_WORDS:
+                    continue
+            ri = cell.get("row_index", -1)
+            row_content_counts[ri] = row_content_counts.get(ri, 0) + 1
+
+        # Majority of rows must have >=2 content cells
+        multi_col_rows = sum(1 for cnt in row_content_counts.values() if cnt >= 2)
+        if multi_col_rows < len(rows) * 0.4:
+            continue
+
+        # Exclude first and last non-header rows -- these are typically
+        # page numbers or footer text, not headings.
+        non_header_rows = [r for r in rows if not r.get("is_header")]
+        if len(non_header_rows) < 3:
+            continue
+        first_ri = non_header_rows[0]["index"]
+        last_ri = non_header_rows[-1]["index"]
+
+        heading_row_indices = []
+        for row in rows:
+            if row.get("is_header"):
+                continue
+            ri = row["index"]
+            if ri == first_ri or ri == last_ri:
+                continue
+            row_cells = [c for c in cells if c.get("row_index") == ri]
+            content_cells = [
+                c for c in row_cells
+                if c.get("col_type", "").startswith("column_")
+                and (c.get("col_type") != "column_1"
+                     or (c.get("text") or "").strip().lower() in _ARTICLE_WORDS)
+            ]
+            if len(content_cells) != 1:
+                continue
+            cell = content_cells[0]
+            # Not in the last column (continuation/example lines)
+            if cell.get("col_index") == last_col:
+                continue
+            text = (cell.get("text") or "").strip()
+            if not text or text.startswith("["):
+                continue
+            # Continuation lines start with "(" -- e.g. "(usw.)", "(TV-Serie)"
+            if text.startswith("("):
+                continue
+            # Single cell NOT in the first content column is likely a
+            # continuation/overflow line, not a heading.  Real headings
+            # ("Theme 1", "Unit 3: ...") appear in the first or second
+            # content column.
+            first_content_col = col_indices[0] if col_indices else 0
+            if cell.get("col_index", 0) > first_content_col + 1:
+                continue
+            # Skip garbled IPA without brackets (e.g. "ska:f -- ska:vz")
+            # but NOT text with real IPA symbols (e.g. "Theme [\u03b8\u02c8i\u02d0m]")
+            _REAL_IPA_CHARS = set("\u02c8\u02cc\u0259\u026a\u025b\u0252\u028a\u028c\u00e6\u0251\u0254\u0283\u0292\u03b8\u00f0\u014b")
+            if _text_has_garbled_ipa(text) and not any(c in _REAL_IPA_CHARS for c in text):
+                continue
+            # Guard: dictionary section headings are short (1-4 alpha chars
+            # like "A", "Ab", "Zi", "Sch").  Longer text that starts
+            # lowercase is a regular vocabulary word (e.g. "zentral") that
+            # happens to appear alone in its row.
+            alpha_only = re.sub(r'[^a-zA-Z\u00e4\u00f6\u00fc\u00c4\u00d6\u00dc\u00df\u1e9e]', '', text)
+            if len(alpha_only) > 4 and text[0].islower():
+                continue
+            heading_row_indices.append(ri)
+
+        # Guard: if >25% of eligible rows would become headings, the
+        # heuristic is misfiring (e.g. sparse single-column layout where
+        # most rows naturally have only 1 content cell).
+        eligible_rows = len(non_header_rows) - 2  # minus first/last excluded
+        if eligible_rows > 0 and len(heading_row_indices) > eligible_rows * 0.25:
+            logger.debug(
+                "Skipping single-cell heading detection for zone %s: "
+                "%d/%d rows would be headings (>25%%)",
+                z.get("zone_index"), len(heading_row_indices), eligible_rows,
+            )
+            continue
+
+        for hri in heading_row_indices:
+            header_cells = [c for c in cells if c.get("row_index") == hri]
+            if not header_cells:
+                continue
+
+            # Collect all word_boxes and text
+            all_wb = []
+            all_text_parts = []
+            for hc in sorted(header_cells, key=lambda c: c["col_index"]):
+                all_wb.extend(hc.get("word_boxes", []))
+                if hc.get("text", "").strip():
+                    all_text_parts.append(hc["text"].strip())
+
+            first_col_idx = min(hc["col_index"] for hc in header_cells)
+
+            # Remove old cells for this row, add spanning heading cell
+            z["cells"] = [c for c in z["cells"] if c.get("row_index") != hri]
+
+            if all_wb:
+                x_min = min(wb["left"] for wb in all_wb)
+                y_min = min(wb["top"] for wb in all_wb)
+                x_max = max(wb["left"] + wb["width"] for wb in all_wb)
+                y_max = max(wb["top"] + wb["height"] for wb in all_wb)
+            else:
+                # Fallback to first cell bbox
+                bp = header_cells[0].get("bbox_px", {})
+                x_min = bp.get("x", 0)
+                y_min = bp.get("y", 0)
+                x_max = x_min + bp.get("w", 0)
+                y_max = y_min + bp.get("h", 0)
+
+            zone_idx = z.get("zone_index", 0)
+            z["cells"].append({
+                "cell_id": f"Z{zone_idx}_R{hri:02d}_C{first_col_idx}",
+                "zone_index": zone_idx,
+                "row_index": hri,
+                "col_index": first_col_idx,
+                "col_type": "heading",
+                "text": " ".join(all_text_parts),
+                "confidence": 0.0,
+                "bbox_px": {"x": x_min, "y": y_min,
+                            "w": x_max - x_min, "h": y_max - y_min},
+                "bbox_pct": {
+                    "x": round(x_min / img_w * 100, 2) if img_w else 0,
+                    "y": round(y_min / img_h * 100, 2) if img_h else 0,
+                    "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
+                    "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
+                },
+                "word_boxes": all_wb,
+                "ocr_engine": "words_first",
+                "is_bold": False,
+            })
+
+            for row in rows:
+                if row["index"] == hri:
+                    row["is_header"] = True
+            heading_count += 1
+
+    return heading_count
+
+
+def _detect_header_rows(
+    rows: List[Dict],
+    zone_words: List[Dict],
+    zone_y: int,
+    columns: Optional[List[Dict]] = None,
+    skip_first_row_header: bool = False,
+) -> List[int]:
+    """Detect header rows: first-row heuristic + spanning header detection.
+
+    A "spanning header" is a row whose words stretch across multiple column
+    boundaries (e.g. "Unit4: Bonnie Scotland" centred across 4 columns).
+    """
+    if len(rows) < 2:
+        return []
+
+    headers = []
+
+    if not skip_first_row_header:
+        first_row = rows[0]
+        second_row = rows[1]
+
+        # Gap between first and second row > 0.5x average row height
+        avg_h = sum(r["y_max"] - r["y_min"] for r in rows) / len(rows)
+        gap = second_row["y_min"] - first_row["y_max"]
+        if gap > avg_h * 0.5:
+            headers.append(0)
+
+        # Also check if first row words are taller than average (bold/header text)
+        all_heights = [w["height"] for w in zone_words]
+        median_h = sorted(all_heights)[len(all_heights) // 2] if all_heights else 20
+        first_row_words = [
+            w for w in zone_words
+            if first_row["y_min"] <= w["top"] + w["height"] / 2 <= first_row["y_max"]
+        ]
+        if first_row_words:
+            first_h = max(w["height"] for w in first_row_words)
+            if first_h > median_h * 1.3:
+                if 0 not in headers:
+                    headers.append(0)
+
+    # Note: Spanning-header detection (rows spanning all columns) has been
+    # disabled because it produces too many false positives on vocabulary
+    # worksheets where IPA transcriptions or short entries naturally span
+    # multiple columns with few words.  The first-row heuristic above is
+    # sufficient for detecting real headers.
+
+    return headers
+
+
+def _detect_colspan_cells(
+    zone_words: List[Dict],
+    columns: List[Dict],
+    rows: List[Dict],
+    cells: List[Dict],
+    img_w: int,
+    img_h: int,
+) -> List[Dict]:
+    """Detect and merge cells that span multiple columns (colspan).
+
+    A word-block (PaddleOCR phrase) that extends significantly past a column
+    boundary into the next column indicates a merged cell.  This replaces
+    the incorrectly split cells with a single cell spanning multiple columns.
+
+    Works for both full-page scans and box zones.
+    """
+    if len(columns) < 2 or not zone_words or not rows:
+        return cells
+
+    from cv_words_first import _assign_word_to_row
+
+    # Column boundaries (midpoints between adjacent columns)
+    col_boundaries = []
+    for ci in range(len(columns) - 1):
+        col_boundaries.append((columns[ci]["x_max"] + columns[ci + 1]["x_min"]) / 2)
+
+    def _cols_covered(w_left: float, w_right: float) -> List[int]:
+        """Return list of column indices that a word-block covers."""
+        covered = []
+        for col in columns:
+            col_mid = (col["x_min"] + col["x_max"]) / 2
+            # Word covers a column if it extends past the column's midpoint
+            if w_left < col_mid < w_right:
+                covered.append(col["index"])
+            # Also include column if word starts within it
+            elif col["x_min"] <= w_left < col["x_max"]:
+                covered.append(col["index"])
+        return sorted(set(covered))
+
+    # Group original word-blocks by row
+    row_word_blocks: Dict[int, List[Dict]] = {}
+    for w in zone_words:
+        ri = _assign_word_to_row(w, rows)
+        row_word_blocks.setdefault(ri, []).append(w)
+
+    # For each row, check if any word-block spans multiple columns
+    rows_to_merge: Dict[int, List[Dict]] = {}  # row_index -> list of spanning word-blocks
+
+    for ri, wblocks in row_word_blocks.items():
+        spanning = []
+        for w in wblocks:
+            w_left = w["left"]
+            w_right = w_left + w["width"]
+            covered = _cols_covered(w_left, w_right)
+            if len(covered) >= 2:
+                spanning.append({"word": w, "cols": covered})
+        if spanning:
+            rows_to_merge[ri] = spanning
+
+    if not rows_to_merge:
+        return cells
+
+    # Merge cells for spanning rows
+    new_cells = []
+    for cell in cells:
+        ri = cell.get("row_index", -1)
+        if ri not in rows_to_merge:
+            new_cells.append(cell)
+            continue
+
+        # Check if this cell's column is part of a spanning block
+        ci = cell.get("col_index", -1)
+        is_part_of_span = False
+        for span in rows_to_merge[ri]:
+            if ci in span["cols"]:
+                is_part_of_span = True
+                # Only emit the merged cell for the FIRST column in the span
+                if ci == span["cols"][0]:
+                    # Use the ORIGINAL word-block text (not the split cell texts
+                    # which may have broken words like "euros a" + "nd cents")
+                    orig_word = span["word"]
+                    merged_text = orig_word.get("text", "").strip()
+                    all_wb = [orig_word]
+
+                    # Compute merged bbox
+                    if all_wb:
+                        x_min = min(wb["left"] for wb in all_wb)
+                        y_min = min(wb["top"] for wb in all_wb)
+                        x_max = max(wb["left"] + wb["width"] for wb in all_wb)
+                        y_max = max(wb["top"] + wb["height"] for wb in all_wb)
+                    else:
+                        x_min = y_min = x_max = y_max = 0
+
+                    new_cells.append({
+                        "cell_id": cell["cell_id"],
+                        "row_index": ri,
+                        "col_index": span["cols"][0],
+                        "col_type": "spanning_header",
+                        "colspan": len(span["cols"]),
+                        "text": merged_text,
+                        "confidence": cell.get("confidence", 0),
+                        "bbox_px": {"x": x_min, "y": y_min,
+                                    "w": x_max - x_min, "h": y_max - y_min},
+                        "bbox_pct": {
+                            "x": round(x_min / img_w * 100, 2) if img_w else 0,
+                            "y": round(y_min / img_h * 100, 2) if img_h else 0,
+                            "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
+                            "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
+                        },
+                        "word_boxes": all_wb,
+                        "ocr_engine": cell.get("ocr_engine", ""),
+                        "is_bold": cell.get("is_bold", False),
+                    })
+                    logger.info(
+                        "colspan detected: row %d, cols %s -> merged %d cells (%r)",
+                        ri, span["cols"], len(span["cols"]), merged_text[:50],
+                    )
+                break
+        if not is_part_of_span:
+            new_cells.append(cell)
+
+    return new_cells

klausur-service/backend/grid_editor_zones.py

@@ -0,0 +1,389 @@
+"""
+Grid Editor — vertical divider detection, zone splitting/merging, zone grid building.
+
+Split from grid_editor_helpers.py for maintainability.
+All functions are pure computation — no HTTP, DB, or session side effects.
+
+Lizenz: Apache 2.0 (kommerziell nutzbar)
+DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
+"""
+
+import logging
+import re
+from typing import Any, Dict, List, Optional
+
+from cv_vocab_types import PageZone
+from cv_words_first import _cluster_rows, _build_cells
+
+from grid_editor_columns import (
+    _cluster_columns_by_alignment,
+    _merge_inline_marker_columns,
+    _split_cross_column_words,
+)
+from grid_editor_headers import (
+    _detect_header_rows,
+    _detect_colspan_cells,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# ---------------------------------------------------------------------------
+# Vertical divider detection and zone splitting
+# ---------------------------------------------------------------------------
+
+_PIPE_RE_VSPLIT = re.compile(r"^\|+$")
+
+
+def _detect_vertical_dividers(
+    words: List[Dict],
+    zone_x: int,
+    zone_w: int,
+    zone_y: int,
+    zone_h: int,
+) -> List[float]:
+    """Detect vertical divider lines from pipe word_boxes at consistent x.
+
+    Returns list of divider x-positions (empty if no dividers found).
+    """
+    if not words or zone_w <= 0 or zone_h <= 0:
+        return []
+
+    # Collect pipe word_boxes
+    pipes = [
+        w for w in words
+        if _PIPE_RE_VSPLIT.match((w.get("text") or "").strip())
+    ]
+    if len(pipes) < 5:
+        return []
+
+    # Cluster pipe x-centers by proximity
+    tolerance = max(15, int(zone_w * 0.02))
+    pipe_xs = sorted(w["left"] + w["width"] / 2 for w in pipes)
+
+    clusters: List[List[float]] = [[pipe_xs[0]]]
+    for x in pipe_xs[1:]:
+        if x - clusters[-1][-1] <= tolerance:
+            clusters[-1].append(x)
+        else:
+            clusters.append([x])
+
+    dividers: List[float] = []
+    for cluster in clusters:
+        if len(cluster) < 5:
+            continue
+        mean_x = sum(cluster) / len(cluster)
+        # Must be between 15% and 85% of zone width
+        rel_pos = (mean_x - zone_x) / zone_w
+        if rel_pos < 0.15 or rel_pos > 0.85:
+            continue
+        # Check vertical coverage: pipes must span >= 50% of zone height
+        cluster_pipes = [
+            w for w in pipes
+            if abs(w["left"] + w["width"] / 2 - mean_x) <= tolerance
+        ]
+        ys = [w["top"] for w in cluster_pipes] + [w["top"] + w["height"] for w in cluster_pipes]
+        y_span = max(ys) - min(ys) if ys else 0
+        if y_span < zone_h * 0.5:
+            continue
+        dividers.append(mean_x)
+
+    return sorted(dividers)
+
+
+def _split_zone_at_vertical_dividers(
+    zone: "PageZone",
+    divider_xs: List[float],
+    vsplit_group_id: int,
+) -> List["PageZone"]:
+    """Split a PageZone at vertical divider positions into sub-zones."""
+    boundaries = [zone.x] + divider_xs + [zone.x + zone.width]
+    hints = []
+    for i in range(len(boundaries) - 1):
+        if i == 0:
+            hints.append("left_of_vsplit")
+        elif i == len(boundaries) - 2:
+            hints.append("right_of_vsplit")
+        else:
+            hints.append("middle_of_vsplit")
+
+    sub_zones = []
+    for i in range(len(boundaries) - 1):
+        x_start = int(boundaries[i])
+        x_end = int(boundaries[i + 1])
+        sub = PageZone(
+            index=0,  # re-indexed later
+            zone_type=zone.zone_type,
+            y=zone.y,
+            height=zone.height,
+            x=x_start,
+            width=x_end - x_start,
+            box=zone.box,
+            image_overlays=zone.image_overlays,
+            layout_hint=hints[i],
+            vsplit_group=vsplit_group_id,
+        )
+        sub_zones.append(sub)
+
+    return sub_zones
+
+
+def _merge_content_zones_across_boxes(
+    zones: List,
+    content_x: int,
+    content_w: int,
+) -> List:
+    """Merge content zones separated by box zones into single zones.
+
+    Box zones become image_overlays on the merged content zone.
+    Pattern: [content, box*, content] -> [merged_content with overlay]
+    Box zones NOT between two content zones stay as standalone zones.
+    """
+    if len(zones) < 3:
+        return zones
+
+    # Group consecutive runs of [content, box+, content]
+    result: List = []
+    i = 0
+    while i < len(zones):
+        z = zones[i]
+        if z.zone_type != "content":
+            result.append(z)
+            i += 1
+            continue
+
+        # Start of a potential merge group: content zone
+        group_contents = [z]
+        group_boxes = []
+        j = i + 1
+        # Absorb [box, content] pairs -- only absorb a box if it's
+        # confirmed to be followed by another content zone.
+        while j < len(zones):
+            if (zones[j].zone_type == "box"
+                    and j + 1 < len(zones)
+                    and zones[j + 1].zone_type == "content"):
+                group_boxes.append(zones[j])
+                group_contents.append(zones[j + 1])
+                j += 2
+            else:
+                break
+
+        if len(group_contents) >= 2 and group_boxes:
+            # Merge: create one large content zone spanning all
+            y_min = min(c.y for c in group_contents)
+            y_max = max(c.y + c.height for c in group_contents)
+            overlays = []
+            for bz in group_boxes:
+                overlay = {
+                    "y": bz.y,
+                    "height": bz.height,
+                    "x": bz.x,
+                    "width": bz.width,
+                }
+                if bz.box:
+                    overlay["box"] = {
+                        "x": bz.box.x,
+                        "y": bz.box.y,
+                        "width": bz.box.width,
+                        "height": bz.box.height,
+                        "confidence": bz.box.confidence,
+                        "border_thickness": bz.box.border_thickness,
+                    }
+                overlays.append(overlay)
+
+            merged = PageZone(
+                index=0,  # re-indexed below
+                zone_type="content",
+                y=y_min,
+                height=y_max - y_min,
+                x=content_x,
+                width=content_w,
+                image_overlays=overlays,
+            )
+            result.append(merged)
+            i = j
+        else:
+            # No merge possible -- emit just the content zone
+            result.append(z)
+            i += 1
+
+    # Re-index zones
+    for idx, z in enumerate(result):
+        z.index = idx
+
+    logger.info(
+        "zone-merge: %d zones -> %d zones after merging across boxes",
+        len(zones), len(result),
+    )
+    return result
+
+
+def _build_zone_grid(
+    zone_words: List[Dict],
+    zone_x: int,
+    zone_y: int,
+    zone_w: int,
+    zone_h: int,
+    zone_index: int,
+    img_w: int,
+    img_h: int,
+    global_columns: Optional[List[Dict]] = None,
+    skip_first_row_header: bool = False,
+) -> Dict[str, Any]:
+    """Build columns, rows, cells for a single zone from its words.
+
+    Args:
+        global_columns: If provided, use these pre-computed column boundaries
+            instead of detecting columns per zone.  Used for content zones so
+            that all content zones (above/between/below boxes) share the same
+            column structure.  Box zones always detect columns independently.
+    """
+    if not zone_words:
+        return {
+            "columns": [],
+            "rows": [],
+            "cells": [],
+            "header_rows": [],
+        }
+
+    # Cluster rows first (needed for column alignment analysis)
+    rows = _cluster_rows(zone_words)
+
+    # Diagnostic logging for small/medium zones (box zones typically have 40-60 words)
+    if len(zone_words) <= 60:
+        import statistics as _st
+        _heights = [w['height'] for w in zone_words if w.get('height', 0) > 0]
+        _med_h = _st.median(_heights) if _heights else 20
+        _y_tol = max(_med_h * 0.5, 5)
+        logger.info(
+            "zone %d row-clustering: %d words, median_h=%.0f, y_tol=%.1f -> %d rows",
+            zone_index, len(zone_words), _med_h, _y_tol, len(rows),
+        )
+        for w in sorted(zone_words, key=lambda ww: (ww['top'], ww['left'])):
+            logger.info(
+                "  zone %d word: y=%d x=%d h=%d w=%d '%s'",
+                zone_index, w['top'], w['left'], w['height'], w['width'],
+                w.get('text', '')[:40],
+            )
+        for r in rows:
+            logger.info(
+                "  zone %d row %d: y_min=%d y_max=%d y_center=%.0f",
+                zone_index, r['index'], r['y_min'], r['y_max'], r['y_center'],
+            )
+
+    # Use global columns if provided, otherwise detect per zone
+    columns = global_columns if global_columns else _cluster_columns_by_alignment(zone_words, zone_w, rows)
+
+    # Merge inline marker columns (bullets, numbering) into adjacent text
+    if not global_columns:
+        columns = _merge_inline_marker_columns(columns, zone_words)
+
+    if not columns or not rows:
+        return {
+            "columns": [],
+            "rows": [],
+            "cells": [],
+            "header_rows": [],
+        }
+
+    # Split word boxes that straddle column boundaries (e.g. "sichzie"
+    # spanning Col 1 + Col 2).  Must happen after column detection and
+    # before cell assignment.
+    # Keep original words for colspan detection (split destroys span info).
+    original_zone_words = zone_words
+    if len(columns) >= 2:
+        zone_words = _split_cross_column_words(zone_words, columns)
+
+    # Build cells
+    cells = _build_cells(zone_words, columns, rows, img_w, img_h)
+
+    # --- Detect colspan (merged cells spanning multiple columns) ---
+    # Uses the ORIGINAL (pre-split) words to detect word-blocks that span
+    # multiple columns.  _split_cross_column_words would have destroyed
+    # this information by cutting words at column boundaries.
+    if len(columns) >= 2:
+        cells = _detect_colspan_cells(original_zone_words, columns, rows, cells, img_w, img_h)
+
+    # Prefix cell IDs with zone index
+    for cell in cells:
+        cell["cell_id"] = f"Z{zone_index}_{cell['cell_id']}"
+        cell["zone_index"] = zone_index
+
+    # Detect header rows (pass columns for spanning header detection)
+    header_rows = _detect_header_rows(rows, zone_words, zone_y, columns,
+                                      skip_first_row_header=skip_first_row_header)
+
+    # Merge cells in spanning header rows into a single col-0 cell
+    if header_rows and len(columns) >= 2:
+        for hri in header_rows:
+            header_cells = [c for c in cells if c["row_index"] == hri]
+            if len(header_cells) <= 1:
+                continue
+            # Collect all word_boxes and text from all columns
+            all_wb = []
+            all_text_parts = []
+            for hc in sorted(header_cells, key=lambda c: c["col_index"]):
+                all_wb.extend(hc.get("word_boxes", []))
+                if hc.get("text", "").strip():
+                    all_text_parts.append(hc["text"].strip())
+            # Remove all header cells, replace with one spanning cell
+            cells = [c for c in cells if c["row_index"] != hri]
+            if all_wb:
+                x_min = min(wb["left"] for wb in all_wb)
+                y_min = min(wb["top"] for wb in all_wb)
+                x_max = max(wb["left"] + wb["width"] for wb in all_wb)
+                y_max = max(wb["top"] + wb["height"] for wb in all_wb)
+                cells.append({
+                    "cell_id": f"R{hri:02d}_C0",
+                    "row_index": hri,
+                    "col_index": 0,
+                    "col_type": "spanning_header",
+                    "text": " ".join(all_text_parts),
+                    "confidence": 0.0,
+                    "bbox_px": {"x": x_min, "y": y_min,
+                                "w": x_max - x_min, "h": y_max - y_min},
+                    "bbox_pct": {
+                        "x": round(x_min / img_w * 100, 2) if img_w else 0,
+                        "y": round(y_min / img_h * 100, 2) if img_h else 0,
+                        "w": round((x_max - x_min) / img_w * 100, 2) if img_w else 0,
+                        "h": round((y_max - y_min) / img_h * 100, 2) if img_h else 0,
+                    },
+                    "word_boxes": all_wb,
+                    "ocr_engine": "words_first",
+                    "is_bold": True,
+                })
+
+    # Convert columns to output format with percentages
+    out_columns = []
+    for col in columns:
+        x_min = col["x_min"]
+        x_max = col["x_max"]
+        out_columns.append({
+            "index": col["index"],
+            "label": col["type"],
+            "x_min_px": round(x_min),
+            "x_max_px": round(x_max),
+            "x_min_pct": round(x_min / img_w * 100, 2) if img_w else 0,
+            "x_max_pct": round(x_max / img_w * 100, 2) if img_w else 0,
+            "bold": False,
+        })
+
+    # Convert rows to output format with percentages
+    out_rows = []
+    for row in rows:
+        out_rows.append({
+            "index": row["index"],
+            "y_min_px": round(row["y_min"]),
+            "y_max_px": round(row["y_max"]),
+            "y_min_pct": round(row["y_min"] / img_h * 100, 2) if img_h else 0,
+            "y_max_pct": round(row["y_max"] / img_h * 100, 2) if img_h else 0,
+            "is_header": row["index"] in header_rows,
+        })
+
+    return {
+        "columns": out_columns,
+        "rows": out_rows,
+        "cells": cells,
+        "header_rows": header_rows,
+        "_raw_columns": columns,  # internal: for propagation to other zones
+    }

klausur-service/backend/legal_corpus_chunking.py

@@ -0,0 +1,197 @@
+"""
+Legal Corpus Chunking — Text splitting, semantic chunking, and HTML-to-text conversion.
+
+Provides German-aware sentence splitting, paragraph splitting, semantic chunking
+with overlap, and HTML-to-text conversion for legal document ingestion.
+"""
+
+import re
+from typing import Dict, List, Optional, Tuple
+
+
+# German abbreviations that don't end sentences
+GERMAN_ABBREVIATIONS = {
+    'bzw', 'ca', 'chr', 'd.h', 'dr', 'etc', 'evtl', 'ggf', 'inkl', 'max',
+    'min', 'mio', 'mrd', 'nr', 'prof', 's', 'sog', 'u.a', 'u.ä', 'usw',
+    'v.a', 'vgl', 'vs', 'z.b', 'z.t', 'zzgl', 'abs', 'art', 'aufl',
+    'bd', 'betr', 'bzgl', 'dgl', 'ebd', 'hrsg', 'jg', 'kap', 'lt',
+    'rdnr', 'rn', 'std', 'str', 'tel', 'ua', 'uvm', 'va', 'zb',
+    'bsi', 'tr', 'owasp', 'iso', 'iec', 'din', 'en'
+}
+
+
+def split_into_sentences(text: str) -> List[str]:
+    """Split text into sentences with German language support."""
+    if not text:
+        return []
+
+    text = re.sub(r'\s+', ' ', text).strip()
+
+    # Protect abbreviations
+    protected_text = text
+    for abbrev in GERMAN_ABBREVIATIONS:
+        pattern = re.compile(r'\b' + re.escape(abbrev) + r'\.', re.IGNORECASE)
+        protected_text = pattern.sub(abbrev.replace('.', '<DOT>') + '<ABBR>', protected_text)
+
+    # Protect decimal/ordinal numbers and requirement IDs (e.g., "O.Data_1")
+    protected_text = re.sub(r'(\d)\.(\d)', r'\1<DECIMAL>\2', protected_text)
+    protected_text = re.sub(r'(\d+)\.(\s)', r'\1<ORD>\2', protected_text)
+    protected_text = re.sub(r'([A-Z])\.([A-Z])', r'\1<REQ>\2', protected_text)  # O.Data_1
+
+    # Split on sentence endings
+    sentence_pattern = r'(?<=[.!?])\s+(?=[A-ZÄÖÜ0-9])|(?<=[.!?])$'
+    raw_sentences = re.split(sentence_pattern, protected_text)
+
+    # Restore protected characters
+    sentences = []
+    for s in raw_sentences:
+        s = s.replace('<DOT>', '.').replace('<ABBR>', '.').replace('<DECIMAL>', '.').replace('<ORD>', '.').replace('<REQ>', '.')
+        s = s.strip()
+        if s:
+            sentences.append(s)
+
+    return sentences
+
+
+def split_into_paragraphs(text: str) -> List[str]:
+    """Split text into paragraphs."""
+    if not text:
+        return []
+
+    raw_paragraphs = re.split(r'\n\s*\n', text)
+    return [para.strip() for para in raw_paragraphs if para.strip()]
+
+
+def chunk_text_semantic(
+    text: str,
+    chunk_size: int = 1000,
+    overlap: int = 200,
+) -> List[Tuple[str, int]]:
+    """
+    Semantic chunking that respects paragraph and sentence boundaries.
+    Matches NIBIS chunking strategy for consistency.
+
+    Returns list of (chunk_text, start_position) tuples.
+    """
+    if not text:
+        return []
+
+    if len(text) <= chunk_size:
+        return [(text.strip(), 0)]
+
+    paragraphs = split_into_paragraphs(text)
+    overlap_sentences = max(1, overlap // 100)  # Convert char overlap to sentence overlap
+
+    chunks = []
+    current_chunk_parts: List[str] = []
+    current_chunk_length = 0
+    chunk_start = 0
+    position = 0
+
+    for para in paragraphs:
+        if len(para) > chunk_size:
+            # Large paragraph: split into sentences
+            sentences = split_into_sentences(para)
+
+            for sentence in sentences:
+                sentence_len = len(sentence)
+
+                if sentence_len > chunk_size:
+                    # Very long sentence: save current chunk first
+                    if current_chunk_parts:
+                        chunk_text = ' '.join(current_chunk_parts)
+                        chunks.append((chunk_text, chunk_start))
+                        overlap_buffer = current_chunk_parts[-overlap_sentences:] if overlap_sentences > 0 else []
+                        current_chunk_parts = list(overlap_buffer)
+                        current_chunk_length = sum(len(s) + 1 for s in current_chunk_parts)
+
+                    # Add long sentence as its own chunk
+                    chunks.append((sentence, position))
+                    current_chunk_parts = [sentence]
+                    current_chunk_length = len(sentence) + 1
+                    position += sentence_len + 1
+                    continue
+
+                if current_chunk_length + sentence_len + 1 > chunk_size and current_chunk_parts:
+                    # Current chunk is full, save it
+                    chunk_text = ' '.join(current_chunk_parts)
+                    chunks.append((chunk_text, chunk_start))
+                    overlap_buffer = current_chunk_parts[-overlap_sentences:] if overlap_sentences > 0 else []
+                    current_chunk_parts = list(overlap_buffer)
+                    current_chunk_length = sum(len(s) + 1 for s in current_chunk_parts)
+                    chunk_start = position - current_chunk_length
+
+                current_chunk_parts.append(sentence)
+                current_chunk_length += sentence_len + 1
+                position += sentence_len + 1
+        else:
+            # Small paragraph: try to keep together
+            para_len = len(para)
+            if current_chunk_length + para_len + 2 > chunk_size and current_chunk_parts:
+                chunk_text = ' '.join(current_chunk_parts)
+                chunks.append((chunk_text, chunk_start))
+                last_para_sentences = split_into_sentences(current_chunk_parts[-1] if current_chunk_parts else "")
+                overlap_buffer = last_para_sentences[-overlap_sentences:] if overlap_sentences > 0 and last_para_sentences else []
+                current_chunk_parts = list(overlap_buffer)
+                current_chunk_length = sum(len(s) + 1 for s in current_chunk_parts)
+                chunk_start = position - current_chunk_length
+
+            if current_chunk_parts:
+                current_chunk_parts.append(para)
+                current_chunk_length += para_len + 2
+            else:
+                current_chunk_parts = [para]
+                current_chunk_length = para_len
+                chunk_start = position
+
+            position += para_len + 2
+
+    # Don't forget the last chunk
+    if current_chunk_parts:
+        chunk_text = ' '.join(current_chunk_parts)
+        chunks.append((chunk_text, chunk_start))
+
+    # Clean up whitespace
+    return [(re.sub(r'\s+', ' ', c).strip(), pos) for c, pos in chunks if c.strip()]
+
+
+def extract_article_info(text: str) -> Optional[Dict]:
+    """Extract article number and paragraph from text."""
+    # Pattern for "Artikel X" or "Art. X"
+    article_match = re.search(r'(?:Artikel|Art\.?)\s+(\d+)', text)
+    paragraph_match = re.search(r'(?:Absatz|Abs\.?)\s+(\d+)', text)
+
+    if article_match:
+        return {
+            "article": article_match.group(1),
+            "paragraph": paragraph_match.group(1) if paragraph_match else None,
+        }
+    return None
+
+
+def html_to_text(html_content: str) -> str:
+    """Convert HTML to clean text."""
+    # Remove script and style tags
+    html_content = re.sub(r'<script[^>]*>.*?</script>', '', html_content, flags=re.DOTALL)
+    html_content = re.sub(r'<style[^>]*>.*?</style>', '', html_content, flags=re.DOTALL)
+    # Remove comments
+    html_content = re.sub(r'<!--.*?-->', '', html_content, flags=re.DOTALL)
+    # Replace common HTML entities
+    html_content = html_content.replace('&nbsp;', ' ')
+    html_content = html_content.replace('&amp;', '&')
+    html_content = html_content.replace('&lt;', '<')
+    html_content = html_content.replace('&gt;', '>')
+    html_content = html_content.replace('&quot;', '"')
+    # Convert breaks and paragraphs to newlines for better chunking
+    html_content = re.sub(r'<br\s*/?>', '\n', html_content, flags=re.IGNORECASE)
+    html_content = re.sub(r'</p>', '\n\n', html_content, flags=re.IGNORECASE)
+    html_content = re.sub(r'</div>', '\n', html_content, flags=re.IGNORECASE)
+    html_content = re.sub(r'</h[1-6]>', '\n\n', html_content, flags=re.IGNORECASE)
+    # Remove remaining HTML tags
+    text = re.sub(r'<[^>]+>', ' ', html_content)
+    # Clean up whitespace (but preserve paragraph breaks)
+    text = re.sub(r'[ \t]+', ' ', text)
+    text = re.sub(r'\n[ \t]+', '\n', text)
+    text = re.sub(r'[ \t]+\n', '\n', text)
+    text = re.sub(r'\n{3,}', '\n\n', text)
+    return text.strip()

klausur-service/backend/legal_corpus_registry.py

@@ -0,0 +1,608 @@
+"""
+Legal Corpus Registry — Regulation metadata and definitions.
+
+Pure data module: contains the Regulation dataclass and the REGULATIONS list
+with all EU regulations, DACH national laws, and EDPB guidelines.
+"""
+
+from dataclasses import dataclass
+from typing import List, Optional
+
+
+@dataclass
+class Regulation:
+    """Regulation metadata."""
+    code: str
+    name: str
+    full_name: str
+    regulation_type: str
+    source_url: str
+    description: str
+    celex: Optional[str] = None  # CELEX number for EUR-Lex direct access
+    local_path: Optional[str] = None
+    language: str = "de"
+    requirement_count: int = 0
+
+
+# All regulations from Compliance Hub (EU + DACH national laws + guidelines)
+REGULATIONS: List[Regulation] = [
+    Regulation(
+        code="GDPR",
+        name="DSGVO",
+        full_name="Verordnung (EU) 2016/679 - Datenschutz-Grundverordnung",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2016/679/oj/deu",
+        description="Grundverordnung zum Schutz natuerlicher Personen bei der Verarbeitung personenbezogener Daten.",
+        celex="32016R0679",
+        requirement_count=99,
+    ),
+    Regulation(
+        code="EPRIVACY",
+        name="ePrivacy-Richtlinie",
+        full_name="Richtlinie 2002/58/EG",
+        regulation_type="eu_directive",
+        source_url="https://eur-lex.europa.eu/eli/dir/2002/58/oj/deu",
+        description="Datenschutz in der elektronischen Kommunikation, Cookies und Tracking.",
+        celex="32002L0058",
+        requirement_count=25,
+    ),
+    Regulation(
+        code="TDDDG",
+        name="TDDDG",
+        full_name="Telekommunikation-Digitale-Dienste-Datenschutz-Gesetz",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/ttdsg/TDDDG.pdf",
+        description="Deutsche Umsetzung der ePrivacy-Richtlinie (30 Paragraphen).",
+        requirement_count=30,
+    ),
+    Regulation(
+        code="SCC",
+        name="Standardvertragsklauseln",
+        full_name="Durchfuehrungsbeschluss (EU) 2021/914",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/dec_impl/2021/914/oj/deu",
+        description="Standardvertragsklauseln fuer Drittlandtransfers.",
+        celex="32021D0914",
+        requirement_count=18,
+    ),
+    Regulation(
+        code="DPF",
+        name="EU-US Data Privacy Framework",
+        full_name="Durchfuehrungsbeschluss (EU) 2023/1795",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/dec_impl/2023/1795/oj",
+        description="Angemessenheitsbeschluss fuer USA-Transfers.",
+        celex="32023D1795",
+        requirement_count=12,
+    ),
+    Regulation(
+        code="AIACT",
+        name="EU AI Act",
+        full_name="Verordnung (EU) 2024/1689 - KI-Verordnung",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2024/1689/oj/deu",
+        description="EU-Verordnung zur Regulierung von KI-Systemen nach Risikostufen.",
+        celex="32024R1689",
+        requirement_count=85,
+    ),
+    Regulation(
+        code="CRA",
+        name="Cyber Resilience Act",
+        full_name="Verordnung (EU) 2024/2847",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2024/2847/oj/deu",
+        description="Cybersicherheitsanforderungen, SBOM-Pflicht.",
+        celex="32024R2847",
+        requirement_count=45,
+    ),
+    Regulation(
+        code="NIS2",
+        name="NIS2-Richtlinie",
+        full_name="Richtlinie (EU) 2022/2555",
+        regulation_type="eu_directive",
+        source_url="https://eur-lex.europa.eu/eli/dir/2022/2555/oj/deu",
+        description="Cybersicherheit fuer wesentliche Einrichtungen.",
+        celex="32022L2555",
+        requirement_count=46,
+    ),
+    Regulation(
+        code="EUCSA",
+        name="EU Cybersecurity Act",
+        full_name="Verordnung (EU) 2019/881",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2019/881/oj/deu",
+        description="ENISA und Cybersicherheitszertifizierung.",
+        celex="32019R0881",
+        requirement_count=35,
+    ),
+    Regulation(
+        code="DATAACT",
+        name="Data Act",
+        full_name="Verordnung (EU) 2023/2854",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2023/2854/oj/deu",
+        description="Fairer Datenzugang, IoT-Daten, Cloud-Wechsel.",
+        celex="32023R2854",
+        requirement_count=42,
+    ),
+    Regulation(
+        code="DGA",
+        name="Data Governance Act",
+        full_name="Verordnung (EU) 2022/868",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2022/868/oj/deu",
+        description="Weiterverwendung oeffentlicher Daten.",
+        celex="32022R0868",
+        requirement_count=35,
+    ),
+    Regulation(
+        code="DSA",
+        name="Digital Services Act",
+        full_name="Verordnung (EU) 2022/2065",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2022/2065/oj/deu",
+        description="Digitale Dienste, Transparenzpflichten.",
+        celex="32022R2065",
+        requirement_count=93,
+    ),
+    Regulation(
+        code="EAA",
+        name="European Accessibility Act",
+        full_name="Richtlinie (EU) 2019/882",
+        regulation_type="eu_directive",
+        source_url="https://eur-lex.europa.eu/eli/dir/2019/882/oj/deu",
+        description="Barrierefreiheit digitaler Produkte.",
+        celex="32019L0882",
+        requirement_count=25,
+    ),
+    Regulation(
+        code="DSM",
+        name="DSM-Urheberrechtsrichtlinie",
+        full_name="Richtlinie (EU) 2019/790",
+        regulation_type="eu_directive",
+        source_url="https://eur-lex.europa.eu/eli/dir/2019/790/oj/deu",
+        description="Urheberrecht, Text- und Data-Mining.",
+        celex="32019L0790",
+        requirement_count=22,
+    ),
+    Regulation(
+        code="PLD",
+        name="Produkthaftungsrichtlinie",
+        full_name="Richtlinie (EU) 2024/2853",
+        regulation_type="eu_directive",
+        source_url="https://eur-lex.europa.eu/eli/dir/2024/2853/oj/deu",
+        description="Produkthaftung inkl. Software und KI.",
+        celex="32024L2853",
+        requirement_count=18,
+    ),
+    Regulation(
+        code="GPSR",
+        name="General Product Safety",
+        full_name="Verordnung (EU) 2023/988",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2023/988/oj/deu",
+        description="Allgemeine Produktsicherheit.",
+        celex="32023R0988",
+        requirement_count=30,
+    ),
+    Regulation(
+        code="BSI-TR-03161-1",
+        name="BSI-TR-03161 Teil 1",
+        full_name="BSI Technische Richtlinie - Allgemeine Anforderungen",
+        regulation_type="bsi_standard",
+        source_url="https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/Publikationen/TechnischeRichtlinien/TR03161/BSI-TR-03161-1.pdf?__blob=publicationFile&v=6",
+        description="Allgemeine Sicherheitsanforderungen (45 Pruefaspekte).",
+        requirement_count=45,
+    ),
+    Regulation(
+        code="BSI-TR-03161-2",
+        name="BSI-TR-03161 Teil 2",
+        full_name="BSI Technische Richtlinie - Web-Anwendungen",
+        regulation_type="bsi_standard",
+        source_url="https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/Publikationen/TechnischeRichtlinien/TR03161/BSI-TR-03161-2.pdf?__blob=publicationFile&v=5",
+        description="Web-Sicherheit (40 Pruefaspekte).",
+        requirement_count=40,
+    ),
+    Regulation(
+        code="BSI-TR-03161-3",
+        name="BSI-TR-03161 Teil 3",
+        full_name="BSI Technische Richtlinie - Hintergrundsysteme",
+        regulation_type="bsi_standard",
+        source_url="https://www.bsi.bund.de/SharedDocs/Downloads/DE/BSI/Publikationen/TechnischeRichtlinien/TR03161/BSI-TR-03161-3.pdf?__blob=publicationFile&v=5",
+        description="Backend-Sicherheit (35 Pruefaspekte).",
+        requirement_count=35,
+    ),
+    # Additional regulations for financial sector and health
+    Regulation(
+        code="DORA",
+        name="DORA",
+        full_name="Verordnung (EU) 2022/2554 - Digital Operational Resilience Act",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2022/2554/oj/deu",
+        description="Digitale operationale Resilienz fuer den Finanzsektor. IKT-Risikomanagement, Vorfallmeldung, Resilienz-Tests.",
+        celex="32022R2554",
+        requirement_count=64,
+    ),
+    Regulation(
+        code="PSD2",
+        name="PSD2",
+        full_name="Richtlinie (EU) 2015/2366 - Zahlungsdiensterichtlinie",
+        regulation_type="eu_directive",
+        source_url="https://eur-lex.europa.eu/eli/dir/2015/2366/oj/deu",
+        description="Zahlungsdienste im Binnenmarkt. Starke Kundenauthentifizierung, Open Banking APIs.",
+        celex="32015L2366",
+        requirement_count=117,
+    ),
+    Regulation(
+        code="AMLR",
+        name="AML-Verordnung",
+        full_name="Verordnung (EU) 2024/1624 - Geldwaeschebekaempfung",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2024/1624/oj/deu",
+        description="Verhinderung der Nutzung des Finanzsystems zur Geldwaesche und Terrorismusfinanzierung.",
+        celex="32024R1624",
+        requirement_count=89,
+    ),
+    Regulation(
+        code="EHDS",
+        name="EHDS",
+        full_name="Verordnung (EU) 2025/327 - Europaeischer Gesundheitsdatenraum",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2025/327/oj/deu",
+        description="Europaeischer Raum fuer Gesundheitsdaten. Primaer- und Sekundaernutzung von Gesundheitsdaten.",
+        celex="32025R0327",
+        requirement_count=95,
+    ),
+    Regulation(
+        code="MiCA",
+        name="MiCA",
+        full_name="Verordnung (EU) 2023/1114 - Markets in Crypto-Assets",
+        regulation_type="eu_regulation",
+        source_url="https://eur-lex.europa.eu/eli/reg/2023/1114/oj/deu",
+        description="Regulierung von Kryptowerten, Stablecoins und Crypto-Asset-Dienstleistern.",
+        celex="32023R1114",
+        requirement_count=149,
+    ),
+    # =====================================================================
+    # DACH National Laws — Deutschland (P1)
+    # =====================================================================
+    Regulation(
+        code="DE_DDG",
+        name="Digitale-Dienste-Gesetz",
+        full_name="Digitale-Dienste-Gesetz (DDG)",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/ddg/",
+        description="Deutsches Umsetzungsgesetz zum DSA. Regelt Impressumspflicht (§5), Informationspflichten fuer digitale Dienste und Cookies.",
+        requirement_count=30,
+    ),
+    Regulation(
+        code="DE_BGB_AGB",
+        name="BGB AGB-Recht",
+        full_name="BGB §§305-310, 312-312k — AGB und Fernabsatz",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/bgb/",
+        description="Deutsches AGB-Recht (§§305-310 BGB) und Fernabsatzrecht (§§312-312k BGB). Klauselverbote, Inhaltskontrolle, Widerrufsrecht, Button-Loesung.",
+        local_path="DE_BGB_AGB.txt",
+        requirement_count=40,
+    ),
+    Regulation(
+        code="DE_EGBGB",
+        name="EGBGB Art. 246-248",
+        full_name="Einfuehrungsgesetz zum BGB — Informationspflichten",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/bgbeg/",
+        description="Informationspflichten bei Verbrauchervertraegen (Art. 246), Fernabsatz (Art. 246a), E-Commerce (Art. 246c).",
+        local_path="DE_EGBGB.txt",
+        requirement_count=20,
+    ),
+    Regulation(
+        code="DE_UWG",
+        name="UWG Deutschland",
+        full_name="Gesetz gegen den unlauteren Wettbewerb (UWG)",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/uwg_2004/",
+        description="Unlauterer Wettbewerb: irrefuehrende Werbung, Spam-Verbot, Preisangaben, Online-Marketing-Regeln.",
+        requirement_count=25,
+    ),
+    Regulation(
+        code="DE_HGB_RET",
+        name="HGB Aufbewahrung",
+        full_name="HGB §§238-261, 257 — Handelsbuecher und Aufbewahrungsfristen",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/hgb/",
+        description="Buchfuehrungspflicht, Aufbewahrungsfristen 6/10 Jahre, Anforderungen an elektronische Aufbewahrung.",
+        local_path="DE_HGB_RET.txt",
+        requirement_count=15,
+    ),
+    Regulation(
+        code="DE_AO_RET",
+        name="AO Aufbewahrung",
+        full_name="Abgabenordnung §§140-148 — Steuerliche Aufbewahrungspflichten",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/ao_1977/",
+        description="Steuerliche Buchfuehrungs- und Aufbewahrungspflichten. 6/10 Jahre Fristen, Datenzugriff durch Finanzbehoerden.",
+        local_path="DE_AO_RET.txt",
+        requirement_count=12,
+    ),
+    Regulation(
+        code="DE_TKG",
+        name="TKG 2021",
+        full_name="Telekommunikationsgesetz 2021",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/tkg_2021/",
+        description="Telekommunikationsregulierung: Kundenschutz, Datenschutz, Vertragslaufzeiten, Netzinfrastruktur.",
+        requirement_count=45,
+    ),
+    # =====================================================================
+    # DACH National Laws — Oesterreich (P1)
+    # =====================================================================
+    Regulation(
+        code="AT_ECG",
+        name="E-Commerce-Gesetz AT",
+        full_name="E-Commerce-Gesetz (ECG) Oesterreich",
+        regulation_type="at_law",
+        source_url="https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=20001703",
+        description="Oesterreichisches E-Commerce-Gesetz: Impressum/Offenlegungspflicht (§5), Informationspflichten, Haftung von Diensteanbietern.",
+        language="de",
+        requirement_count=30,
+    ),
+    Regulation(
+        code="AT_TKG",
+        name="TKG 2021 AT",
+        full_name="Telekommunikationsgesetz 2021 Oesterreich",
+        regulation_type="at_law",
+        source_url="https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=20011678",
+        description="Oesterreichisches TKG: Cookie-Bestimmungen (§165), Kommunikationsgeheimnis, Endgeraetezugriff.",
+        language="de",
+        requirement_count=40,
+    ),
+    Regulation(
+        code="AT_KSCHG",
+        name="KSchG Oesterreich",
+        full_name="Konsumentenschutzgesetz (KSchG) Oesterreich",
+        regulation_type="at_law",
+        source_url="https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=10002462",
+        description="Konsumentenschutz: AGB-Kontrolle (§6 Klauselverbote, §9 Verbandsklage), Ruecktrittsrecht, Informationspflichten.",
+        language="de",
+        requirement_count=35,
+    ),
+    Regulation(
+        code="AT_FAGG",
+        name="FAGG Oesterreich",
+        full_name="Fern- und Auswaertsgeschaefte-Gesetz (FAGG) Oesterreich",
+        regulation_type="at_law",
+        source_url="https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=20008847",
+        description="Fernabsatzrecht: Informationspflichten, Widerrufsrecht 14 Tage, Button-Loesung, Ausnahmen.",
+        language="de",
+        requirement_count=20,
+    ),
+    Regulation(
+        code="AT_UGB_RET",
+        name="UGB Aufbewahrung AT",
+        full_name="UGB §§189-216, 212 — Rechnungslegung und Aufbewahrung Oesterreich",
+        regulation_type="at_law",
+        source_url="https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=10001702",
+        description="Oesterreichische Rechnungslegungspflicht und Aufbewahrungsfristen (7 Jahre). Buchfuehrung, Jahresabschluss.",
+        local_path="AT_UGB_RET.txt",
+        language="de",
+        requirement_count=15,
+    ),
+    Regulation(
+        code="AT_BAO_RET",
+        name="BAO §132 AT",
+        full_name="Bundesabgabenordnung §132 — Aufbewahrung Oesterreich",
+        regulation_type="at_law",
+        source_url="https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=10003940",
+        description="Steuerliche Aufbewahrungspflicht 7 Jahre fuer Buecher, Aufzeichnungen und Belege. Grundstuecke 22 Jahre.",
+        language="de",
+        requirement_count=5,
+    ),
+    Regulation(
+        code="AT_MEDIENG",
+        name="MedienG §§24-25 AT",
+        full_name="Mediengesetz §§24-25 Oesterreich — Impressum und Offenlegung",
+        regulation_type="at_law",
+        source_url="https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=10000719",
+        description="Impressum/Offenlegungspflicht fuer periodische Medien und Websites in Oesterreich.",
+        language="de",
+        requirement_count=10,
+    ),
+    # =====================================================================
+    # DACH National Laws — Schweiz (P1)
+    # =====================================================================
+    Regulation(
+        code="CH_DSV",
+        name="DSV Schweiz",
+        full_name="Datenschutzverordnung (DSV) Schweiz — SR 235.11",
+        regulation_type="ch_law",
+        source_url="https://www.fedlex.admin.ch/eli/cc/2022/568/de",
+        description="Ausfuehrungsverordnung zum revDSG: Meldepflichten, DSFA-Verfahren, Auslandtransfers, technische Massnahmen.",
+        language="de",
+        requirement_count=30,
+    ),
+    Regulation(
+        code="CH_OR_AGB",
+        name="OR AGB/Aufbewahrung CH",
+        full_name="Obligationenrecht — AGB-Kontrolle und Aufbewahrung Schweiz (SR 220)",
+        regulation_type="ch_law",
+        source_url="https://www.fedlex.admin.ch/eli/cc/27/317_321_377/de",
+        description="Art. 8 OR (AGB-Inhaltskontrolle), Art. 19/20 (Vertragsfreiheit), Art. 957-958f (Buchfuehrung, 10 Jahre Aufbewahrung).",
+        local_path="CH_OR_AGB.txt",
+        language="de",
+        requirement_count=20,
+    ),
+    Regulation(
+        code="CH_UWG",
+        name="UWG Schweiz",
+        full_name="Bundesgesetz gegen den unlauteren Wettbewerb Schweiz (SR 241)",
+        regulation_type="ch_law",
+        source_url="https://www.fedlex.admin.ch/eli/cc/1988/223_223_223/de",
+        description="Lauterkeitsrecht: Impressumspflicht, irrefuehrende Werbung, aggressive Verkaufsmethoden, AGB-Transparenz.",
+        language="de",
+        requirement_count=20,
+    ),
+    Regulation(
+        code="CH_FMG",
+        name="FMG Schweiz",
+        full_name="Fernmeldegesetz Schweiz (SR 784.10)",
+        regulation_type="ch_law",
+        source_url="https://www.fedlex.admin.ch/eli/cc/1997/2187_2187_2187/de",
+        description="Telekommunikationsregulierung: Fernmeldegeheimnis, Cookies/Tracking (Art. 45c), Spam-Verbot, Datenschutz.",
+        language="de",
+        requirement_count=25,
+    ),
+    # =====================================================================
+    # Deutschland P2
+    # =====================================================================
+    Regulation(
+        code="DE_PANGV",
+        name="PAngV",
+        full_name="Preisangabenverordnung (PAngV 2022)",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/pangv_2022/",
+        description="Preisangaben: Gesamtpreis, Grundpreis, Streichpreise (§11), Online-Preisauszeichnung.",
+        requirement_count=15,
+    ),
+    Regulation(
+        code="DE_DLINFOV",
+        name="DL-InfoV",
+        full_name="Dienstleistungs-Informationspflichten-Verordnung",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/dlinfov/",
+        description="Informationspflichten fuer Dienstleister: Identitaet, Kontakt, Berufshaftpflicht, AGB-Zugang.",
+        requirement_count=10,
+    ),
+    Regulation(
+        code="DE_BETRVG",
+        name="BetrVG §87",
+        full_name="Betriebsverfassungsgesetz §87 Abs.1 Nr.6",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/betrvg/",
+        description="Mitbestimmung bei technischer Ueberwachung: Betriebsrat-Beteiligung bei IT-Systemen, die Arbeitnehmerverhalten ueberwachen koennen.",
+        requirement_count=5,
+    ),
+    # =====================================================================
+    # Oesterreich P2
+    # =====================================================================
+    Regulation(
+        code="AT_ABGB_AGB",
+        name="ABGB AGB-Recht AT",
+        full_name="ABGB §§861-879, 864a — AGB-Kontrolle Oesterreich",
+        regulation_type="at_law",
+        source_url="https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=10001622",
+        description="Geltungskontrolle (§864a), Sittenwidrigkeitskontrolle (§879 Abs.3), allgemeine Vertragsregeln.",
+        local_path="AT_ABGB_AGB.txt",
+        language="de",
+        requirement_count=10,
+    ),
+    Regulation(
+        code="AT_UWG",
+        name="UWG Oesterreich",
+        full_name="Bundesgesetz gegen den unlauteren Wettbewerb Oesterreich",
+        regulation_type="at_law",
+        source_url="https://www.ris.bka.gv.at/GeltendeFassung.wxe?Abfrage=Bundesnormen&Gesetzesnummer=10002665",
+        description="Lauterkeitsrecht AT: irrefuehrende Geschaeftspraktiken, aggressive Praktiken, Preisauszeichnung.",
+        language="de",
+        requirement_count=15,
+    ),
+    # =====================================================================
+    # Schweiz P2
+    # =====================================================================
+    Regulation(
+        code="CH_GEBUV",
+        name="GeBuV Schweiz",
+        full_name="Geschaeftsbuecher-Verordnung Schweiz (SR 221.431)",
+        regulation_type="ch_law",
+        source_url="https://www.fedlex.admin.ch/eli/cc/2002/468_468_468/de",
+        description="Ausfuehrungsvorschriften zur Buchfuehrung: elektronische Aufbewahrung, Integritaet, Datentraeger.",
+        language="de",
+        requirement_count=10,
+    ),
+    Regulation(
+        code="CH_ZERTES",
+        name="ZertES Schweiz",
+        full_name="Bundesgesetz ueber die elektronische Signatur (SR 943.03)",
+        regulation_type="ch_law",
+        source_url="https://www.fedlex.admin.ch/eli/cc/2016/752/de",
+        description="Elektronische Signatur und Zertifizierung: Qualifizierte Signaturen, Zertifizierungsdiensteanbieter.",
+        language="de",
+        requirement_count=10,
+    ),
+    # =====================================================================
+    # Deutschland P3
+    # =====================================================================
+    Regulation(
+        code="DE_GESCHGEHG",
+        name="GeschGehG",
+        full_name="Gesetz zum Schutz von Geschaeftsgeheimnissen",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/geschgehg/",
+        description="Schutz von Geschaeftsgeheimnissen: Definition, angemessene Geheimhaltungsmassnahmen, Reverse Engineering.",
+        requirement_count=10,
+    ),
+    Regulation(
+        code="DE_BSIG",
+        name="BSI-Gesetz",
+        full_name="Gesetz ueber das Bundesamt fuer Sicherheit in der Informationstechnik (BSIG)",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/bsig_2009/",
+        description="BSI-Aufgaben, KRITIS-Meldepflichten, IT-Sicherheitsstandards, Zertifizierung.",
+        requirement_count=20,
+    ),
+    Regulation(
+        code="DE_USTG_RET",
+        name="UStG §14b",
+        full_name="Umsatzsteuergesetz §14b — Aufbewahrung von Rechnungen",
+        regulation_type="de_law",
+        source_url="https://www.gesetze-im-internet.de/ustg_1980/",
+        description="Aufbewahrungspflicht fuer Rechnungen: 10 Jahre, Grundstuecke 20 Jahre, elektronische Aufbewahrung.",
+        local_path="DE_USTG_RET.txt",
+        requirement_count=5,
+    ),
+    # =====================================================================
+    # Schweiz P3
+    # =====================================================================
+    Regulation(
+        code="CH_ZGB_PERS",
+        name="ZGB Persoenlichkeitsschutz CH",
+        full_name="Zivilgesetzbuch Art. 28-28l — Persoenlichkeitsschutz Schweiz (SR 210)",
+        regulation_type="ch_law",
+        source_url="https://www.fedlex.admin.ch/eli/cc/24/233_245_233/de",
+        description="Persoenlichkeitsschutz: Recht am eigenen Bild, Schutz der Privatsphaere, Gegendarstellungsrecht.",
+        language="de",
+        requirement_count=8,
+    ),
+    # =====================================================================
+    # 3 fehlgeschlagene Quellen mit alternativen URLs nachholen
+    # =====================================================================
+    Regulation(
+        code="LU_DPA_LAW",
+        name="Datenschutzgesetz Luxemburg",
+        full_name="Loi du 1er aout 2018 — Datenschutzgesetz Luxemburg",
+        regulation_type="national_law",
+        source_url="https://legilux.public.lu/eli/etat/leg/loi/2018/08/01/a686/jo",
+        description="Luxemburgisches Datenschutzgesetz: Organisation der CNPD, nationale DSGVO-Ergaenzung.",
+        language="fr",
+        requirement_count=40,
+    ),
+    Regulation(
+        code="DK_DATABESKYTTELSESLOVEN",
+        name="Databeskyttelsesloven DK",
+        full_name="Databeskyttelsesloven — Datenschutzgesetz Daenemark",
+        regulation_type="national_law",
+        source_url="https://www.retsinformation.dk/eli/lta/2018/502",
+        description="Daenisches Datenschutzgesetz als ergaenzende Bestimmungen zur DSGVO. Reguliert durch Datatilsynet.",
+        language="da",
+        requirement_count=30,
+    ),
+    Regulation(
+        code="EDPB_GUIDELINES_1_2022",
+        name="EDPB GL Bussgelder",
+        full_name="EDPB Leitlinien 04/2022 zur Berechnung von Bussgeldern nach der DSGVO",
+        regulation_type="eu_guideline",
+        source_url="https://www.edpb.europa.eu/system/files/2023-05/edpb_guidelines_042022_calculationofadministrativefines_en.pdf",
+        description="EDPB-Leitlinien zur Berechnung von Verwaltungsbussgeldern unter der DSGVO.",
+        language="en",
+        requirement_count=15,
+    ),
+]

klausur-service/backend/worksheet_editor_ai.py

@@ -0,0 +1,485 @@
+"""
+Worksheet Editor AI — AI image generation and AI worksheet modification.
+"""
+
+import io
+import json
+import base64
+import logging
+import re
+import time
+import random
+from typing import List, Dict
+
+import httpx
+
+from worksheet_editor_models import (
+    AIImageRequest,
+    AIImageResponse,
+    AIImageStyle,
+    AIModifyRequest,
+    AIModifyResponse,
+    OLLAMA_URL,
+    STYLE_PROMPTS,
+)
+
+logger = logging.getLogger(__name__)
+
+
+# =============================================
+# AI IMAGE GENERATION
+# =============================================
+
+async def generate_ai_image_logic(request: AIImageRequest) -> AIImageResponse:
+    """
+    Generate an AI image using Ollama with a text-to-image model.
+
+    Falls back to a placeholder if Ollama is not available.
+    """
+    from fastapi import HTTPException
+
+    try:
+        # Build enhanced prompt with style
+        style_modifier = STYLE_PROMPTS.get(request.style, "")
+        enhanced_prompt = f"{request.prompt}, {style_modifier}"
+
+        logger.info(f"Generating AI image: {enhanced_prompt[:100]}...")
+
+        # Check if Ollama is available
+        async with httpx.AsyncClient(timeout=10.0) as check_client:
+            try:
+                health_response = await check_client.get(f"{OLLAMA_URL}/api/tags")
+                if health_response.status_code != 200:
+                    raise HTTPException(status_code=503, detail="Ollama service not available")
+            except httpx.ConnectError:
+                logger.warning("Ollama not reachable, returning placeholder")
+                return _generate_placeholder_image(request, enhanced_prompt)
+
+        try:
+            async with httpx.AsyncClient(timeout=300.0) as client:
+                tags_response = await client.get(f"{OLLAMA_URL}/api/tags")
+                available_models = [m.get("name", "") for m in tags_response.json().get("models", [])]
+
+                sd_model = None
+                for model in available_models:
+                    if "stable" in model.lower() or "sd" in model.lower() or "diffusion" in model.lower():
+                        sd_model = model
+                        break
+
+                if not sd_model:
+                    logger.warning("No Stable Diffusion model found in Ollama")
+                    return _generate_placeholder_image(request, enhanced_prompt)
+
+                logger.info(f"SD model found: {sd_model}, but image generation API not implemented")
+                return _generate_placeholder_image(request, enhanced_prompt)
+
+        except Exception as e:
+            logger.error(f"Image generation failed: {e}")
+            return _generate_placeholder_image(request, enhanced_prompt)
+
+    except HTTPException:
+        raise
+    except Exception as e:
+        logger.error(f"AI image generation error: {e}")
+        raise HTTPException(status_code=500, detail=str(e))
+
+
+def _generate_placeholder_image(request: AIImageRequest, prompt: str) -> AIImageResponse:
+    """
+    Generate a placeholder image when AI generation is not available.
+    Creates a simple SVG-based placeholder with the prompt text.
+    """
+    from PIL import Image, ImageDraw, ImageFont
+
+    width, height = request.width, request.height
+
+    style_colors = {
+        AIImageStyle.REALISTIC: ("#2563eb", "#dbeafe"),
+        AIImageStyle.CARTOON: ("#f97316", "#ffedd5"),
+        AIImageStyle.SKETCH: ("#6b7280", "#f3f4f6"),
+        AIImageStyle.CLIPART: ("#8b5cf6", "#ede9fe"),
+        AIImageStyle.EDUCATIONAL: ("#059669", "#d1fae5"),
+    }
+
+    fg_color, bg_color = style_colors.get(request.style, ("#6366f1", "#e0e7ff"))
+
+    img = Image.new('RGB', (width, height), bg_color)
+    draw = ImageDraw.Draw(img)
+
+    draw.rectangle([5, 5, width-6, height-6], outline=fg_color, width=3)
+
+    cx, cy = width // 2, height // 2 - 30
+    draw.ellipse([cx-40, cy-40, cx+40, cy+40], outline=fg_color, width=3)
+    draw.line([cx-20, cy-10, cx+20, cy-10], fill=fg_color, width=3)
+    draw.line([cx, cy-10, cx, cy+20], fill=fg_color, width=3)
+
+    try:
+        font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 14)
+    except Exception:
+        font = ImageFont.load_default()
+
+    max_chars = 40
+    lines = []
+    words = prompt[:200].split()
+    current_line = ""
+    for word in words:
+        if len(current_line) + len(word) + 1 <= max_chars:
+            current_line += (" " + word if current_line else word)
+        else:
+            if current_line:
+                lines.append(current_line)
+            current_line = word
+    if current_line:
+        lines.append(current_line)
+
+    text_y = cy + 60
+    for line in lines[:4]:
+        bbox = draw.textbbox((0, 0), line, font=font)
+        text_width = bbox[2] - bbox[0]
+        draw.text((cx - text_width // 2, text_y), line, fill=fg_color, font=font)
+        text_y += 20
+
+    badge_text = "KI-Bild (Platzhalter)"
+    try:
+        badge_font = ImageFont.truetype("/usr/share/fonts/truetype/dejavu/DejaVuSans.ttf", 10)
+    except Exception:
+        badge_font = font
+    draw.rectangle([10, height-30, 150, height-10], fill=fg_color)
+    draw.text((15, height-27), badge_text, fill="white", font=badge_font)
+
+    buffer = io.BytesIO()
+    img.save(buffer, format='PNG')
+    buffer.seek(0)
+
+    image_base64 = f"data:image/png;base64,{base64.b64encode(buffer.getvalue()).decode('utf-8')}"
+
+    return AIImageResponse(
+        image_base64=image_base64,
+        prompt_used=prompt,
+        error="AI image generation not available. Using placeholder."
+    )
+
+
+# =============================================
+# AI WORKSHEET MODIFICATION
+# =============================================
+
+async def modify_worksheet_with_ai_logic(request: AIModifyRequest) -> AIModifyResponse:
+    """
+    Modify a worksheet using AI based on natural language prompt.
+    """
+    try:
+        logger.info(f"AI modify request: {request.prompt[:100]}...")
+
+        try:
+            canvas_data = json.loads(request.canvas_json)
+        except json.JSONDecodeError:
+            return AIModifyResponse(
+                message="Fehler beim Parsen des Canvas",
+                error="Invalid canvas JSON"
+            )
+
+        system_prompt = """Du bist ein Assistent fuer die Bearbeitung von Arbeitsblaettern.
+Du erhaeltst den aktuellen Zustand eines Canvas im JSON-Format und eine Anweisung des Nutzers.
+Deine Aufgabe ist es, die gewuenschten Aenderungen am Canvas vorzunehmen.
+
+Der Canvas verwendet Fabric.js. Hier sind die wichtigsten Objekttypen:
+- i-text: Interaktiver Text mit fontFamily, fontSize, fill, left, top
+- rect: Rechteck mit left, top, width, height, fill, stroke, strokeWidth
+- circle: Kreis mit left, top, radius, fill, stroke, strokeWidth
+- line: Linie mit x1, y1, x2, y2, stroke, strokeWidth
+
+Das Canvas ist 794x1123 Pixel (A4 bei 96 DPI).
+
+Antworte NUR mit einem JSON-Objekt in diesem Format:
+{
+  "action": "modify" oder "add" oder "delete" oder "info",
+  "objects": [...],  // Neue/modifizierte Objekte (bei modify/add)
+  "message": "Kurze Beschreibung der Aenderung"
+}
+
+Wenn du Objekte hinzufuegst, generiere eindeutige IDs im Format "obj_<timestamp>_<random>".
+"""
+
+        user_prompt = f"""Aktueller Canvas-Zustand:
+```json
+{json.dumps(canvas_data, indent=2)[:5000]}
+```
+
+Nutzer-Anweisung: {request.prompt}
+
+Fuehre die Aenderung durch und antworte mit dem JSON-Objekt."""
+
+        try:
+            async with httpx.AsyncClient(timeout=120.0) as client:
+                response = await client.post(
+                    f"{OLLAMA_URL}/api/generate",
+                    json={
+                        "model": request.model,
+                        "prompt": user_prompt,
+                        "system": system_prompt,
+                        "stream": False,
+                        "options": {
+                            "temperature": 0.3,
+                            "num_predict": 4096
+                        }
+                    }
+                )
+
+                if response.status_code != 200:
+                    logger.warning(f"Ollama error: {response.status_code}, trying local fallback")
+                    return _handle_simple_modification(request.prompt, canvas_data)
+
+                ai_response = response.json().get("response", "")
+
+        except httpx.ConnectError:
+            logger.warning("Ollama not reachable")
+            return _handle_simple_modification(request.prompt, canvas_data)
+        except httpx.TimeoutException:
+            logger.warning("Ollama timeout, trying local fallback")
+            return _handle_simple_modification(request.prompt, canvas_data)
+
+        try:
+            json_start = ai_response.find('{')
+            json_end = ai_response.rfind('}') + 1
+
+            if json_start == -1 or json_end <= json_start:
+                logger.warning(f"No JSON found in AI response: {ai_response[:200]}")
+                return AIModifyResponse(
+                    message="KI konnte die Anfrage nicht verarbeiten",
+                    error="No JSON in response"
+                )
+
+            ai_json = json.loads(ai_response[json_start:json_end])
+            action = ai_json.get("action", "info")
+            message = ai_json.get("message", "Aenderungen angewendet")
+            new_objects = ai_json.get("objects", [])
+
+            if action == "info":
+                return AIModifyResponse(message=message)
+
+            if action == "add" and new_objects:
+                existing_objects = canvas_data.get("objects", [])
+                existing_objects.extend(new_objects)
+                canvas_data["objects"] = existing_objects
+                return AIModifyResponse(
+                    modified_canvas_json=json.dumps(canvas_data),
+                    message=message
+                )
+
+            if action == "modify" and new_objects:
+                existing_objects = canvas_data.get("objects", [])
+                new_ids = {obj.get("id") for obj in new_objects if obj.get("id")}
+                kept_objects = [obj for obj in existing_objects if obj.get("id") not in new_ids]
+                kept_objects.extend(new_objects)
+                canvas_data["objects"] = kept_objects
+                return AIModifyResponse(
+                    modified_canvas_json=json.dumps(canvas_data),
+                    message=message
+                )
+
+            if action == "delete":
+                delete_ids = ai_json.get("delete_ids", [])
+                if delete_ids:
+                    existing_objects = canvas_data.get("objects", [])
+                    canvas_data["objects"] = [obj for obj in existing_objects if obj.get("id") not in delete_ids]
+                    return AIModifyResponse(
+                        modified_canvas_json=json.dumps(canvas_data),
+                        message=message
+                    )
+
+            return AIModifyResponse(message=message)
+
+        except json.JSONDecodeError as e:
+            logger.error(f"Failed to parse AI JSON: {e}")
+            return AIModifyResponse(
+                message="Fehler beim Verarbeiten der KI-Antwort",
+                error=str(e)
+            )
+
+    except Exception as e:
+        logger.error(f"AI modify error: {e}")
+        return AIModifyResponse(
+            message="Ein unerwarteter Fehler ist aufgetreten",
+            error=str(e)
+        )
+
+
+def _handle_simple_modification(prompt: str, canvas_data: dict) -> AIModifyResponse:
+    """
+    Handle simple modifications locally when Ollama is not available.
+    Supports basic commands like adding headings, lines, etc.
+    """
+    prompt_lower = prompt.lower()
+    objects = canvas_data.get("objects", [])
+
+    def generate_id():
+        return f"obj_{int(time.time()*1000)}_{random.randint(1000, 9999)}"
+
+    # Add heading
+    if "ueberschrift" in prompt_lower or "titel" in prompt_lower or "heading" in prompt_lower:
+        text_match = re.search(r'"([^"]+)"', prompt)
+        text = text_match.group(1) if text_match else "Ueberschrift"
+
+        new_text = {
+            "type": "i-text", "id": generate_id(), "text": text,
+            "left": 397, "top": 50, "originX": "center",
+            "fontFamily": "Arial", "fontSize": 28, "fontWeight": "bold", "fill": "#000000"
+        }
+        objects.append(new_text)
+        canvas_data["objects"] = objects
+        return AIModifyResponse(
+            modified_canvas_json=json.dumps(canvas_data),
+            message=f"Ueberschrift '{text}' hinzugefuegt"
+        )
+
+    # Add lines for writing
+    if "linie" in prompt_lower or "line" in prompt_lower or "schreib" in prompt_lower:
+        num_match = re.search(r'(\d+)', prompt)
+        num_lines = int(num_match.group(1)) if num_match else 5
+        num_lines = min(num_lines, 20)
+
+        start_y = 150
+        line_spacing = 40
+
+        for i in range(num_lines):
+            new_line = {
+                "type": "line", "id": generate_id(),
+                "x1": 60, "y1": start_y + i * line_spacing,
+                "x2": 734, "y2": start_y + i * line_spacing,
+                "stroke": "#cccccc", "strokeWidth": 1
+            }
+            objects.append(new_line)
+
+        canvas_data["objects"] = objects
+        return AIModifyResponse(
+            modified_canvas_json=json.dumps(canvas_data),
+            message=f"{num_lines} Schreiblinien hinzugefuegt"
+        )
+
+    # Make text bigger
+    if "groesser" in prompt_lower or "bigger" in prompt_lower or "larger" in prompt_lower:
+        modified = 0
+        for obj in objects:
+            if obj.get("type") in ["i-text", "text", "textbox"]:
+                current_size = obj.get("fontSize", 16)
+                obj["fontSize"] = int(current_size * 1.25)
+                modified += 1
+
+        canvas_data["objects"] = objects
+        if modified > 0:
+            return AIModifyResponse(
+                modified_canvas_json=json.dumps(canvas_data),
+                message=f"{modified} Texte vergroessert"
+            )
+
+    # Center elements
+    if "zentrier" in prompt_lower or "center" in prompt_lower or "mitte" in prompt_lower:
+        center_x = 397
+        for obj in objects:
+            if not obj.get("isGrid"):
+                obj["left"] = center_x
+                obj["originX"] = "center"
+
+        canvas_data["objects"] = objects
+        return AIModifyResponse(
+            modified_canvas_json=json.dumps(canvas_data),
+            message="Elemente zentriert"
+        )
+
+    # Add numbering
+    if "nummer" in prompt_lower or "nummerier" in prompt_lower or "1-10" in prompt_lower:
+        range_match = re.search(r'(\d+)\s*[-bis]+\s*(\d+)', prompt)
+        if range_match:
+            start, end = int(range_match.group(1)), int(range_match.group(2))
+        else:
+            start, end = 1, 10
+
+        y = 100
+        for i in range(start, min(end + 1, start + 20)):
+            new_text = {
+                "type": "i-text", "id": generate_id(), "text": f"{i}.",
+                "left": 40, "top": y, "fontFamily": "Arial", "fontSize": 14, "fill": "#000000"
+            }
+            objects.append(new_text)
+            y += 35
+
+        canvas_data["objects"] = objects
+        return AIModifyResponse(
+            modified_canvas_json=json.dumps(canvas_data),
+            message=f"Nummerierung {start}-{end} hinzugefuegt"
+        )
+
+    # Add rectangle/box
+    if "rechteck" in prompt_lower or "box" in prompt_lower or "kasten" in prompt_lower:
+        new_rect = {
+            "type": "rect", "id": generate_id(),
+            "left": 100, "top": 200, "width": 200, "height": 100,
+            "fill": "transparent", "stroke": "#000000", "strokeWidth": 2
+        }
+        objects.append(new_rect)
+        canvas_data["objects"] = objects
+        return AIModifyResponse(
+            modified_canvas_json=json.dumps(canvas_data),
+            message="Rechteck hinzugefuegt"
+        )
+
+    # Add grid/raster
+    if "raster" in prompt_lower or "grid" in prompt_lower or "tabelle" in prompt_lower:
+        dim_match = re.search(r'(\d+)\s*[x/\u00d7\*mal by]\s*(\d+)', prompt_lower)
+        if dim_match:
+            cols = int(dim_match.group(1))
+            rows = int(dim_match.group(2))
+        else:
+            nums = re.findall(r'(\d+)', prompt)
+            if len(nums) >= 2:
+                cols, rows = int(nums[0]), int(nums[1])
+            else:
+                cols, rows = 3, 4
+
+        cols = min(max(1, cols), 10)
+        rows = min(max(1, rows), 15)
+
+        canvas_width = 794
+        canvas_height = 1123
+        margin = 60
+        available_width = canvas_width - 2 * margin
+        available_height = canvas_height - 2 * margin - 80
+
+        cell_width = available_width / cols
+        cell_height = min(available_height / rows, 80)
+
+        start_x = margin
+        start_y = 120
+
+        grid_objects = []
+        for r in range(rows + 1):
+            y = start_y + r * cell_height
+            grid_objects.append({
+                "type": "line", "id": generate_id(),
+                "x1": start_x, "y1": y,
+                "x2": start_x + cols * cell_width, "y2": y,
+                "stroke": "#666666", "strokeWidth": 1, "isGrid": True
+            })
+
+        for c in range(cols + 1):
+            x = start_x + c * cell_width
+            grid_objects.append({
+                "type": "line", "id": generate_id(),
+                "x1": x, "y1": start_y,
+                "x2": x, "y2": start_y + rows * cell_height,
+                "stroke": "#666666", "strokeWidth": 1, "isGrid": True
+            })
+
+        objects.extend(grid_objects)
+        canvas_data["objects"] = objects
+        return AIModifyResponse(
+            modified_canvas_json=json.dumps(canvas_data),
+            message=f"{cols}x{rows} Raster hinzugefuegt ({cols} Spalten, {rows} Zeilen)"
+        )
+
+    # Default: Ollama needed
+    return AIModifyResponse(
+        message="Diese Aenderung erfordert den KI-Service. Bitte stellen Sie sicher, dass Ollama laeuft.",
+        error="Complex modification requires Ollama"
+    )

klausur-service/backend/worksheet_editor_models.py

@@ -0,0 +1,133 @@
+"""
+Worksheet Editor Models — Enums, Pydantic models, and configuration.
+"""
+
+import os
+import logging
+from typing import Optional, List, Dict
+from enum import Enum
+
+from pydantic import BaseModel, Field
+
+logger = logging.getLogger(__name__)
+
+# =============================================
+# CONFIGURATION
+# =============================================
+
+OLLAMA_URL = os.getenv("OLLAMA_URL", "http://host.docker.internal:11434")
+SD_MODEL = os.getenv("SD_MODEL", "stable-diffusion")  # or specific SD model
+WORKSHEET_STORAGE_DIR = os.getenv("WORKSHEET_STORAGE_DIR",
+    os.path.join(os.path.dirname(os.path.abspath(__file__)), "worksheet-storage"))
+
+# Ensure storage directory exists
+os.makedirs(WORKSHEET_STORAGE_DIR, exist_ok=True)
+
+# =============================================
+# ENUMS & MODELS
+# =============================================
+
+class AIImageStyle(str, Enum):
+    REALISTIC = "realistic"
+    CARTOON = "cartoon"
+    SKETCH = "sketch"
+    CLIPART = "clipart"
+    EDUCATIONAL = "educational"
+
+class WorksheetStatus(str, Enum):
+    DRAFT = "draft"
+    PUBLISHED = "published"
+    ARCHIVED = "archived"
+
+# Style prompt modifiers
+STYLE_PROMPTS = {
+    AIImageStyle.REALISTIC: "photorealistic, high detail, professional photography",
+    AIImageStyle.CARTOON: "cartoon style, colorful, child-friendly, simple shapes",
+    AIImageStyle.SKETCH: "pencil sketch, hand-drawn, black and white, artistic",
+    AIImageStyle.CLIPART: "clipart style, flat design, simple, vector-like",
+    AIImageStyle.EDUCATIONAL: "educational illustration, clear, informative, textbook style"
+}
+
+# =============================================
+# REQUEST/RESPONSE MODELS
+# =============================================
+
+class AIImageRequest(BaseModel):
+    prompt: str = Field(..., min_length=3, max_length=500)
+    style: AIImageStyle = AIImageStyle.EDUCATIONAL
+    width: int = Field(512, ge=256, le=1024)
+    height: int = Field(512, ge=256, le=1024)
+
+class AIImageResponse(BaseModel):
+    image_base64: str
+    prompt_used: str
+    error: Optional[str] = None
+
+class PageData(BaseModel):
+    id: str
+    index: int
+    canvasJSON: str
+
+class PageFormat(BaseModel):
+    width: float = 210
+    height: float = 297
+    orientation: str = "portrait"
+    margins: Dict[str, float] = {"top": 15, "right": 15, "bottom": 15, "left": 15}
+
+class WorksheetSaveRequest(BaseModel):
+    id: Optional[str] = None
+    title: str
+    description: Optional[str] = None
+    pages: List[PageData]
+    pageFormat: Optional[PageFormat] = None
+
+class WorksheetResponse(BaseModel):
+    id: str
+    title: str
+    description: Optional[str]
+    pages: List[PageData]
+    pageFormat: PageFormat
+    createdAt: str
+    updatedAt: str
+
+class AIModifyRequest(BaseModel):
+    prompt: str = Field(..., min_length=3, max_length=1000)
+    canvas_json: str
+    model: str = "qwen2.5vl:32b"
+
+class AIModifyResponse(BaseModel):
+    modified_canvas_json: Optional[str] = None
+    message: str
+    error: Optional[str] = None
+
+class ReconstructRequest(BaseModel):
+    session_id: str
+    page_number: int = 1
+    include_images: bool = True
+    regenerate_graphics: bool = False
+
+class ReconstructResponse(BaseModel):
+    canvas_json: str
+    page_width: int
+    page_height: int
+    elements_count: int
+    vocabulary_matched: int
+    message: str
+    error: Optional[str] = None
+
+# =============================================
+# IN-MEMORY STORAGE (Development)
+# =============================================
+
+worksheets_db: Dict[str, Dict] = {}
+
+# PDF Generation availability
+try:
+    from reportlab.lib import colors  # noqa: F401
+    from reportlab.lib.pagesizes import A4  # noqa: F401
+    from reportlab.lib.units import mm  # noqa: F401
+    from reportlab.pdfgen import canvas  # noqa: F401
+    from reportlab.lib.styles import getSampleStyleSheet  # noqa: F401
+    REPORTLAB_AVAILABLE = True
+except ImportError:
+    REPORTLAB_AVAILABLE = False

klausur-service/backend/worksheet_editor_reconstruct.py

@@ -0,0 +1,255 @@
+"""
+Worksheet Editor Reconstruct — Document reconstruction from vocab sessions.
+"""
+
+import io
+import uuid
+import base64
+import logging
+from typing import List, Dict
+
+import numpy as np
+
+from worksheet_editor_models import (
+    ReconstructRequest,
+    ReconstructResponse,
+)
+
+logger = logging.getLogger(__name__)
+
+
+async def reconstruct_document_logic(request: ReconstructRequest) -> ReconstructResponse:
+    """
+    Reconstruct a document from a vocab session into Fabric.js canvas format.
+
+    This function:
+    1. Loads the original PDF from the vocab session
+    2. Runs OCR with position tracking
+    3. Creates Fabric.js canvas JSON with positioned elements
+    4. Maps extracted vocabulary to their positions
+
+    Returns ReconstructResponse ready to send to the client.
+    """
+    from fastapi import HTTPException
+    from vocab_worksheet_api import _sessions, convert_pdf_page_to_image
+
+    # Check if session exists
+    if request.session_id not in _sessions:
+        raise HTTPException(status_code=404, detail=f"Session {request.session_id} not found")
+
+    session = _sessions[request.session_id]
+
+    if not session.get("pdf_data"):
+        raise HTTPException(status_code=400, detail="Session has no PDF data")
+
+    pdf_data = session["pdf_data"]
+    page_count = session.get("pdf_page_count", 1)
+
+    if request.page_number < 1 or request.page_number > page_count:
+        raise HTTPException(
+            status_code=400,
+            detail=f"Page {request.page_number} not found. PDF has {page_count} pages."
+        )
+
+    vocabulary = session.get("vocabulary", [])
+    page_vocab = [v for v in vocabulary if v.get("source_page") == request.page_number]
+
+    logger.info(f"Reconstructing page {request.page_number} from session {request.session_id}")
+    logger.info(f"Found {len(page_vocab)} vocabulary items for this page")
+
+    image_bytes = await convert_pdf_page_to_image(pdf_data, request.page_number)
+    if not image_bytes:
+        raise HTTPException(status_code=500, detail="Failed to convert PDF page to image")
+
+    from PIL import Image
+    img = Image.open(io.BytesIO(image_bytes))
+    img_width, img_height = img.size
+
+    from hybrid_vocab_extractor import run_paddle_ocr
+    ocr_regions, raw_text = run_paddle_ocr(image_bytes)
+
+    logger.info(f"OCR found {len(ocr_regions)} text regions")
+
+    A4_WIDTH = 794
+    A4_HEIGHT = 1123
+    scale_x = A4_WIDTH / img_width
+    scale_y = A4_HEIGHT / img_height
+
+    fabric_objects = []
+
+    # 1. Add white background
+    fabric_objects.append({
+        "type": "rect", "left": 0, "top": 0,
+        "width": A4_WIDTH, "height": A4_HEIGHT,
+        "fill": "#ffffff", "selectable": False,
+        "evented": False, "isBackground": True
+    })
+
+    # 2. Group OCR regions by Y-coordinate to detect rows
+    sorted_regions = sorted(ocr_regions, key=lambda r: (r.y1, r.x1))
+
+    # 3. Detect headers (larger text at top)
+    headers = []
+    for region in sorted_regions:
+        height = region.y2 - region.y1
+        if region.y1 < img_height * 0.15 and height > 30:
+            headers.append(region)
+
+    # 4. Create text objects for each region
+    vocab_matched = 0
+
+    for region in sorted_regions:
+        left = int(region.x1 * scale_x)
+        top = int(region.y1 * scale_y)
+
+        is_header = region in headers
+
+        region_height = region.y2 - region.y1
+        base_font_size = max(10, min(32, int(region_height * scale_y * 0.8)))
+
+        if is_header:
+            base_font_size = max(base_font_size, 24)
+
+        is_vocab = False
+        vocab_match = None
+        for v in page_vocab:
+            if v.get("english", "").lower() in region.text.lower() or \
+               v.get("german", "").lower() in region.text.lower():
+                is_vocab = True
+                vocab_match = v
+                vocab_matched += 1
+                break
+
+        text_obj = {
+            "type": "i-text",
+            "id": f"text_{uuid.uuid4().hex[:8]}",
+            "left": left, "top": top,
+            "text": region.text,
+            "fontFamily": "Arial",
+            "fontSize": base_font_size,
+            "fontWeight": "bold" if is_header else "normal",
+            "fill": "#000000",
+            "originX": "left", "originY": "top",
+        }
+
+        if is_vocab and vocab_match:
+            text_obj["isVocabulary"] = True
+            text_obj["vocabularyId"] = vocab_match.get("id")
+            text_obj["english"] = vocab_match.get("english")
+            text_obj["german"] = vocab_match.get("german")
+
+        fabric_objects.append(text_obj)
+
+    # 5. If include_images, detect and extract image regions
+    if request.include_images:
+        image_regions = await _detect_image_regions(image_bytes, ocr_regions, img_width, img_height)
+
+        for i, img_region in enumerate(image_regions):
+            img_x1 = int(img_region["x1"])
+            img_y1 = int(img_region["y1"])
+            img_x2 = int(img_region["x2"])
+            img_y2 = int(img_region["y2"])
+
+            cropped = img.crop((img_x1, img_y1, img_x2, img_y2))
+
+            buffer = io.BytesIO()
+            cropped.save(buffer, format='PNG')
+            buffer.seek(0)
+            img_base64 = f"data:image/png;base64,{base64.b64encode(buffer.getvalue()).decode('utf-8')}"
+
+            fabric_objects.append({
+                "type": "image",
+                "id": f"img_{uuid.uuid4().hex[:8]}",
+                "left": int(img_x1 * scale_x),
+                "top": int(img_y1 * scale_y),
+                "width": int((img_x2 - img_x1) * scale_x),
+                "height": int((img_y2 - img_y1) * scale_y),
+                "src": img_base64,
+                "scaleX": 1, "scaleY": 1,
+            })
+
+    import json
+    canvas_data = {
+        "version": "6.0.0",
+        "objects": fabric_objects,
+        "background": "#ffffff"
+    }
+
+    return ReconstructResponse(
+        canvas_json=json.dumps(canvas_data),
+        page_width=A4_WIDTH,
+        page_height=A4_HEIGHT,
+        elements_count=len(fabric_objects),
+        vocabulary_matched=vocab_matched,
+        message=f"Reconstructed page {request.page_number} with {len(fabric_objects)} elements, "
+                f"{vocab_matched} vocabulary items matched"
+    )
+
+
+async def _detect_image_regions(
+    image_bytes: bytes,
+    ocr_regions: list,
+    img_width: int,
+    img_height: int
+) -> List[Dict]:
+    """
+    Detect image/graphic regions in the document.
+
+    Uses a simple approach:
+    1. Find large gaps between text regions (potential image areas)
+    2. Use edge detection to find bounded regions
+    3. Filter out text areas
+    """
+    from PIL import Image
+    import cv2
+
+    try:
+        img = Image.open(io.BytesIO(image_bytes))
+        img_array = np.array(img.convert('L'))
+
+        text_mask = np.ones_like(img_array, dtype=bool)
+        for region in ocr_regions:
+            x1 = max(0, region.x1 - 5)
+            y1 = max(0, region.y1 - 5)
+            x2 = min(img_width, region.x2 + 5)
+            y2 = min(img_height, region.y2 + 5)
+            text_mask[y1:y2, x1:x2] = False
+
+        image_regions = []
+
+        edges = cv2.Canny(img_array, 50, 150)
+        edges[~text_mask] = 0
+
+        contours, _ = cv2.findContours(edges, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+        for contour in contours:
+            x, y, w, h = cv2.boundingRect(contour)
+
+            if w > 50 and h > 50:
+                if w < img_width * 0.9 and h < img_height * 0.9:
+                    region_content = img_array[y:y+h, x:x+w]
+                    variance = np.var(region_content)
+
+                    if variance > 500:
+                        image_regions.append({
+                            "x1": x, "y1": y,
+                            "x2": x + w, "y2": y + h
+                        })
+
+        filtered_regions = []
+        for region in sorted(image_regions, key=lambda r: (r["x2"]-r["x1"])*(r["y2"]-r["y1"]), reverse=True):
+            overlaps = False
+            for existing in filtered_regions:
+                if not (region["x2"] < existing["x1"] or region["x1"] > existing["x2"] or
+                        region["y2"] < existing["y1"] or region["y1"] > existing["y2"]):
+                    overlaps = True
+                    break
+            if not overlaps:
+                filtered_regions.append(region)
+
+        logger.info(f"Detected {len(filtered_regions)} image regions")
+        return filtered_regions[:10]
+
+    except Exception as e:
+        logger.warning(f"Image region detection failed: {e}")
+        return []