Fix: Sidebar scrollable + add Eltern-Portal nav link
overflow-hidden → overflow-y-auto so all nav items are reachable. Added /parent (Eltern-Portal) link with people icon. Co-Authored-By: Claude Opus 4.6 (1M context) <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
381
klausur-service/backend/ocr/engines/engines.py
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381
klausur-service/backend/ocr/engines/engines.py
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"""
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OCR engines (RapidOCR, TrOCR, LightOn) and re-exports.
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This module contains the OCR engine wrappers and re-exports all functions
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from the split sub-modules for backward compatibility.
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Sub-modules:
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- cv_ocr_word_assembly: Word grouping and text assembly
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- cv_ocr_vocab_postprocess: Vocabulary postprocessing (char confusion, comma split)
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- cv_ocr_ipa_lookup: Core IPA lookup and bracket handling
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- cv_ocr_ipa_repair: Advanced IPA repair (continuation cells, post-bracket cleanup)
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- cv_ocr_cell_phonetics: Cell-level phonetics for overlay
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- cv_ocr_cell_filter: Cell text filtering, column assignment, bold detection
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Lizenz: Apache 2.0 (kommerziell nutzbar)
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DATENSCHUTZ: Alle Verarbeitung erfolgt lokal.
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"""
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import io
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import logging
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import os
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import re
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from typing import Any, Dict, List, Optional, Tuple
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import numpy as np
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from cv_vocab_types import (
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IPA_AVAILABLE,
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PageRegion,
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RowGeometry,
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_britfone_dict,
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_ipa_convert_american,
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)
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logger = logging.getLogger(__name__)
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try:
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import cv2
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except ImportError:
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cv2 = None # type: ignore[assignment]
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try:
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from PIL import Image
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except ImportError:
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Image = None # type: ignore[assignment,misc]
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# ── Re-exports from sub-modules (backward compatibility) ──────────────────
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from cv_ocr_word_assembly import ( # noqa: F401
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_group_words_into_lines,
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_words_to_reading_order_lines,
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_rejoin_hyphenated,
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_words_to_reading_order_text,
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_words_to_spaced_text,
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)
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from cv_ocr_vocab_postprocess import ( # noqa: F401
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_CHAR_CONFUSION_RULES,
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_DE_INDICATORS_FOR_EN_I,
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_fix_character_confusion,
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_is_singular_plural_pair,
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_split_comma_entries,
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_split_by_comma,
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_find_best_vocab_match,
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_attach_example_sentences,
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)
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from cv_ocr_ipa_lookup import ( # noqa: F401
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_PHONETIC_BRACKET_RE,
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_IPA_CHARS,
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_MIN_WORD_CONF,
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_GRAMMAR_BRACKET_WORDS,
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_lookup_ipa,
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_fix_phonetic_brackets,
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_is_grammar_bracket_content,
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_replace_phonetics_in_text,
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_text_has_garbled_ipa,
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_decompose_compound,
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_insert_missing_ipa,
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)
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from cv_ocr_ipa_repair import ( # noqa: F401
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_has_non_dict_trailing,
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_strip_post_bracket_garbled,
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fix_ipa_continuation_cell,
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_insert_headword_ipa,
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)
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from cv_ocr_cell_phonetics import ( # noqa: F401
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fix_cell_phonetics,
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_has_ipa_gap,
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_sync_word_boxes_after_ipa_insert,
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)
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from cv_ocr_cell_filter import ( # noqa: F401
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_RE_REAL_WORD,
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_RE_ALPHA,
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_COMMON_SHORT_WORDS,
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_KNOWN_ABBREVIATIONS,
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_assign_row_words_to_columns,
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_is_noise_tail_token,
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_is_garbage_text,
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_clean_cell_text,
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_clean_cell_text_lite,
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_measure_stroke_width,
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_classify_bold_cells,
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)
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# ── OCR Engine Wrappers ───────────────────────────────────────────────────
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_rapid_engine = None
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RAPIDOCR_AVAILABLE = False
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try:
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from rapidocr import RapidOCR as _RapidOCRClass
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from rapidocr import LangRec as _LangRec, OCRVersion as _OCRVersion, ModelType as _ModelType
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RAPIDOCR_AVAILABLE = True
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logger.info("RapidOCR available — can be used as alternative to Tesseract")
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except ImportError:
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logger.info("RapidOCR not installed — using Tesseract only")
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def _get_rapid_engine():
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"""Lazy-init RapidOCR engine with PP-OCRv5 Latin model for German support."""
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global _rapid_engine
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if _rapid_engine is None:
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_rapid_engine = _RapidOCRClass(params={
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"Rec.lang_type": _LangRec.LATIN,
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"Rec.model_type": _ModelType.SERVER,
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"Rec.ocr_version": _OCRVersion.PPOCRV5,
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"Det.unclip_ratio": 1.3,
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"Det.box_thresh": 0.4,
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"Global.log_level": "critical",
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})
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logger.info("RapidOCR engine initialized (PP-OCRv5 Latin, unclip_ratio=1.3)")
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return _rapid_engine
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def ocr_region_rapid(
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img_bgr: np.ndarray,
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region: PageRegion,
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) -> List[Dict[str, Any]]:
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"""Run RapidOCR on a specific region, returning word dicts compatible with Tesseract format."""
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engine = _get_rapid_engine()
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crop = img_bgr[region.y:region.y + region.height,
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region.x:region.x + region.width]
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if crop.size == 0:
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return []
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result = engine(crop)
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if result is None or result.boxes is None or result.txts is None:
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return []
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words = []
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boxes = result.boxes
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txts = result.txts
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scores = result.scores
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for i, (box, txt, score) in enumerate(zip(boxes, txts, scores)):
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if not txt or not txt.strip():
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continue
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xs = [p[0] for p in box]
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ys = [p[1] for p in box]
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left = int(min(xs))
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top = int(min(ys))
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w = int(max(xs) - left)
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h = int(max(ys) - top)
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words.append({
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'text': txt.strip(),
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'left': left + region.x,
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'top': top + region.y,
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'width': w,
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'height': h,
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'conf': int(score * 100),
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'region_type': region.type,
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})
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return words
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def ocr_region_trocr(img_bgr: np.ndarray, region: PageRegion, handwritten: bool = False) -> List[Dict[str, Any]]:
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"""Run TrOCR on a region. Returns line-level word dicts."""
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from services.trocr_service import get_trocr_model, _split_into_lines, _check_trocr_available
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if not _check_trocr_available():
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logger.warning("TrOCR not available, falling back to Tesseract")
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if region.height > 0 and region.width > 0:
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ocr_img_crop = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) if img_bgr is not None else None
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if ocr_img_crop is not None:
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return ocr_region(ocr_img_crop, region, lang="eng+deu", psm=6)
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return []
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crop = img_bgr[region.y:region.y + region.height, region.x:region.x + region.width]
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if crop.size == 0:
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return []
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try:
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import torch
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from PIL import Image as _PILImage
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processor, model = get_trocr_model(handwritten=handwritten)
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if processor is None or model is None:
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logger.warning("TrOCR model not loaded, falling back to Tesseract")
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ocr_img_crop = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
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return ocr_region(ocr_img_crop, region, lang="eng+deu", psm=6)
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pil_crop = _PILImage.fromarray(cv2.cvtColor(crop, cv2.COLOR_BGR2RGB))
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lines = _split_into_lines(pil_crop)
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if not lines:
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lines = [pil_crop]
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device = next(model.parameters()).device
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all_text = []
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confidences = []
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for line_img in lines:
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pixel_values = processor(images=line_img, return_tensors="pt").pixel_values.to(device)
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with torch.no_grad():
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generated_ids = model.generate(pixel_values, max_length=128)
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text_line = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
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if text_line:
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all_text.append(text_line)
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confidences.append(0.85 if len(text_line) > 3 else 0.5)
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if not all_text:
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return []
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avg_conf = int(sum(confidences) / len(confidences) * 100)
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line_h = region.height // max(len(all_text), 1)
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words = []
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for i, line in enumerate(all_text):
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words.append({
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"text": line,
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"left": region.x,
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"top": region.y + i * line_h,
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"width": region.width,
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"height": line_h,
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"conf": avg_conf,
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"region_type": region.type,
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})
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return words
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except Exception as e:
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logger.error(f"ocr_region_trocr failed: {e}")
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return []
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def ocr_region_lighton(img_bgr: np.ndarray, region: PageRegion) -> List[Dict[str, Any]]:
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"""Run LightOnOCR-2-1B on a region. Returns line-level word dicts."""
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from services.lighton_ocr_service import get_lighton_model, _check_lighton_available
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if not _check_lighton_available():
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logger.warning("LightOnOCR not available, falling back to RapidOCR/Tesseract")
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if RAPIDOCR_AVAILABLE and img_bgr is not None:
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return ocr_region_rapid(img_bgr, region)
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ocr_img_crop = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY) if img_bgr is not None else None
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return ocr_region(ocr_img_crop, region, lang="eng+deu", psm=6) if ocr_img_crop is not None else []
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crop = img_bgr[region.y:region.y + region.height, region.x:region.x + region.width]
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if crop.size == 0:
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return []
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try:
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import io
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import torch
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from PIL import Image as _PILImage
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processor, model = get_lighton_model()
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if processor is None or model is None:
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logger.warning("LightOnOCR model not loaded, falling back to RapidOCR/Tesseract")
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if RAPIDOCR_AVAILABLE and img_bgr is not None:
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return ocr_region_rapid(img_bgr, region)
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ocr_img_crop = cv2.cvtColor(img_bgr, cv2.COLOR_BGR2GRAY)
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return ocr_region(ocr_img_crop, region, lang="eng+deu", psm=6)
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pil_crop = _PILImage.fromarray(cv2.cvtColor(crop, cv2.COLOR_BGR2RGB))
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conversation = [{"role": "user", "content": [{"type": "image"}]}]
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inputs = processor.apply_chat_template(
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conversation, images=[pil_crop],
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add_generation_prompt=True, return_tensors="pt"
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).to(model.device)
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with torch.no_grad():
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output_ids = model.generate(**inputs, max_new_tokens=1024)
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text = processor.decode(output_ids[0], skip_special_tokens=True).strip()
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if not text:
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return []
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lines = [l.strip() for l in text.split("\n") if l.strip()]
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line_h = region.height // max(len(lines), 1)
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words = []
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for i, line in enumerate(lines):
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words.append({
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"text": line,
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"left": region.x,
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"top": region.y + i * line_h,
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"width": region.width,
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"height": line_h,
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"conf": 85,
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"region_type": region.type,
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})
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return words
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except Exception as e:
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logger.error(f"ocr_region_lighton failed: {e}")
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return []
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async def ocr_region_paddle(
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img_bgr: np.ndarray,
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region: Optional["PageRegion"] = None,
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) -> List[Dict[str, Any]]:
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"""Run OCR via local RapidOCR (default) or remote PaddleOCR (fallback)."""
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force_remote = os.environ.get("FORCE_REMOTE_PADDLE", "").strip() == "1"
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if not force_remote:
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try:
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if region is None:
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h, w = img_bgr.shape[:2]
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_region = PageRegion(type="full_page", x=0, y=0, width=w, height=h)
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else:
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_region = region
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words = ocr_region_rapid(img_bgr, _region)
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if words:
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logger.info("ocr_region_paddle: used local RapidOCR (%d words)", len(words))
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return words
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logger.warning("ocr_region_paddle: RapidOCR returned 0 words, trying remote")
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except Exception as e:
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logger.warning("ocr_region_paddle: RapidOCR failed (%s), trying remote", e)
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from services.paddleocr_remote import ocr_remote_paddle
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if region is not None:
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crop = img_bgr[
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region.y : region.y + region.height,
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region.x : region.x + region.width,
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]
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offset_x, offset_y = region.x, region.y
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else:
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crop = img_bgr
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offset_x, offset_y = 0, 0
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if crop.size == 0:
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return []
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h, w = crop.shape[:2]
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scale = 1.0
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_MAX_DIM = 1500
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if max(h, w) > _MAX_DIM:
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scale = _MAX_DIM / max(h, w)
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new_w, new_h = int(w * scale), int(h * scale)
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crop = cv2.resize(crop, (new_w, new_h), interpolation=cv2.INTER_AREA)
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logger.info("ocr_region_paddle: downscaled %dx%d → %dx%d (scale=%.2f)",
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w, h, new_w, new_h, scale)
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success, jpg_buf = cv2.imencode(".jpg", crop, [cv2.IMWRITE_JPEG_QUALITY, 90])
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if not success:
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logger.error("ocr_region_paddle: cv2.imencode failed")
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return []
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words, _w, _h = await ocr_remote_paddle(jpg_buf.tobytes(), filename="scan.jpg")
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logger.info("ocr_region_paddle: used remote PaddleOCR (%d words)", len(words))
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inv_scale = 1.0 / scale if scale != 1.0 else 1.0
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for wd in words:
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wd["left"] = int(wd["left"] * inv_scale) + offset_x
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wd["top"] = int(wd["top"] * inv_scale) + offset_y
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wd["width"] = int(wd["width"] * inv_scale)
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wd["height"] = int(wd["height"] * inv_scale)
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if region is not None:
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wd["region_type"] = region.type
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return words
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