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Three fixes for dictionary page session 5997: 1. Heading detection: column_1 cells with article words (die/der/das) now count as content cells, preventing "die Zuschrift, die Zuschriften" from being falsely merged into a spanning heading cell. 2. Step 5j-pre: new artifact cell filter removes short garbled text from OCR on image areas (e.g. "7 EN", "Tr", "\\", "PEE", "a="). Cells survive earlier filters because their rows have real content in other columns. Also cleans up empty rows after removal. 3. Footer "PEE" auto-fixed: artifact filter removes the noise cell, empty row gets cleaned up, footer detection no longer sees it. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2721 lines
108 KiB
Python
2721 lines
108 KiB
Python
"""
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Grid Editor API — builds a structured, zone-aware grid from Kombi OCR results.
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Takes the merged word positions from paddle-kombi / rapid-kombi and:
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1. Detects bordered boxes on the image (cv_box_detect)
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2. Splits the page into zones (content + box regions)
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3. Clusters words into columns and rows per zone
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4. Returns a hierarchical StructuredGrid for the frontend Excel-like editor
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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 logging
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import re
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import time
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from typing import Any, Dict, List, Optional, Tuple
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import cv2
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import numpy as np
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from fastapi import APIRouter, HTTPException, Request
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from cv_box_detect import detect_boxes, split_page_into_zones
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from cv_vocab_types import PageZone
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from cv_color_detect import detect_word_colors, recover_colored_text
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from cv_ocr_engines import fix_cell_phonetics, fix_ipa_continuation_cell, _text_has_garbled_ipa, _lookup_ipa, _words_to_reading_order_text, _group_words_into_lines
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from cv_words_first import _cluster_rows, _build_cells
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from ocr_pipeline_session_store import (
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get_session_db,
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get_session_image,
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update_session_db,
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)
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logger = logging.getLogger(__name__)
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router = APIRouter(prefix="/api/v1/ocr-pipeline", tags=["grid-editor"])
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# ---------------------------------------------------------------------------
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# Helpers
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# ---------------------------------------------------------------------------
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def _filter_border_strip_words(words: List[Dict]) -> Tuple[List[Dict], int]:
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"""Remove page-border decoration strip words BEFORE column detection.
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Scans from each page edge inward to find the first significant x-gap
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(>30 px). If the edge cluster contains <15 % of total words, those
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words are removed as border-strip artifacts (alphabet letters,
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illustration fragments).
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Must run BEFORE ``_build_zone_grid`` so that column detection only
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sees real content words and doesn't produce inflated row counts.
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"""
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if len(words) < 10:
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return words, 0
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sorted_words = sorted(words, key=lambda w: w.get("left", 0))
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total = len(sorted_words)
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# -- Left-edge scan (running max right-edge) --
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left_count = 0
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running_right = 0
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for gi in range(total - 1):
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running_right = max(
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running_right,
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sorted_words[gi].get("left", 0) + sorted_words[gi].get("width", 0),
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)
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if sorted_words[gi + 1].get("left", 0) - running_right > 30:
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left_count = gi + 1
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break
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# -- Right-edge scan (running min left) --
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right_count = 0
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running_left = sorted_words[-1].get("left", 0)
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for gi in range(total - 1, 0, -1):
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running_left = min(running_left, sorted_words[gi].get("left", 0))
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prev_right = (
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sorted_words[gi - 1].get("left", 0)
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+ sorted_words[gi - 1].get("width", 0)
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)
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if running_left - prev_right > 30:
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right_count = total - gi
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break
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strip_ids: set = set()
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if left_count > 0 and left_count / total < 0.20:
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strip_ids = {id(w) for w in sorted_words[:left_count]}
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elif right_count > 0 and right_count / total < 0.20:
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strip_ids = {id(w) for w in sorted_words[total - right_count :]}
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if not strip_ids:
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return words, 0
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return [w for w in words if id(w) not in strip_ids], len(strip_ids)
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def _cluster_columns_by_alignment(
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words: List[Dict],
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zone_w: int,
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rows: List[Dict],
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) -> List[Dict[str, Any]]:
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"""Detect columns by clustering left-edge alignment across rows.
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Hybrid approach:
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1. Group words by row, find "group start" positions within each row
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(words preceded by a large gap or first word in row)
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2. Cluster group-start left-edges by X-proximity across rows
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3. Filter by row coverage (how many rows have a group start here)
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4. Merge nearby clusters
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5. Build column boundaries
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This filters out mid-phrase word positions (e.g. IPA transcriptions,
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second words in multi-word entries) by only considering positions
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where a new word group begins within a row.
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"""
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if not words or not rows:
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return []
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total_rows = len(rows)
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if total_rows == 0:
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return []
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# --- Group words by row ---
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row_words: Dict[int, List[Dict]] = {}
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for w in words:
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y_center = w["top"] + w["height"] / 2
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best = min(rows, key=lambda r: abs(r["y_center"] - y_center))
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row_words.setdefault(best["index"], []).append(w)
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# --- Compute adaptive gap threshold for group-start detection ---
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all_gaps: List[float] = []
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for ri, rw_list in row_words.items():
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sorted_rw = sorted(rw_list, key=lambda w: w["left"])
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for i in range(len(sorted_rw) - 1):
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right = sorted_rw[i]["left"] + sorted_rw[i]["width"]
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gap = sorted_rw[i + 1]["left"] - right
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if gap > 0:
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all_gaps.append(gap)
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if all_gaps:
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sorted_gaps = sorted(all_gaps)
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median_gap = sorted_gaps[len(sorted_gaps) // 2]
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heights = [w["height"] for w in words if w.get("height", 0) > 0]
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median_h = sorted(heights)[len(heights) // 2] if heights else 25
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# Column boundary: gap > 3× median gap or > 1.5× median word height
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gap_threshold = max(median_gap * 3, median_h * 1.5, 30)
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else:
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gap_threshold = 50
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# --- Find group-start positions (left-edges that begin a new column) ---
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start_positions: List[tuple] = [] # (left_edge, row_index)
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for ri, rw_list in row_words.items():
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sorted_rw = sorted(rw_list, key=lambda w: w["left"])
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# First word in row is always a group start
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start_positions.append((sorted_rw[0]["left"], ri))
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for i in range(1, len(sorted_rw)):
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right_prev = sorted_rw[i - 1]["left"] + sorted_rw[i - 1]["width"]
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gap = sorted_rw[i]["left"] - right_prev
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if gap >= gap_threshold:
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start_positions.append((sorted_rw[i]["left"], ri))
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start_positions.sort(key=lambda x: x[0])
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logger.info(
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"alignment columns: %d group-start positions from %d words "
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"(gap_threshold=%.0f, %d rows)",
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len(start_positions), len(words), gap_threshold, total_rows,
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)
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if not start_positions:
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x_min = min(w["left"] for w in words)
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x_max = max(w["left"] + w["width"] for w in words)
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return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
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# --- Cluster group-start positions by X-proximity ---
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tolerance = max(10, int(zone_w * 0.01))
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clusters: List[Dict[str, Any]] = []
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cur_edges = [start_positions[0][0]]
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cur_rows = {start_positions[0][1]}
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for left, row_idx in start_positions[1:]:
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if left - cur_edges[-1] <= tolerance:
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cur_edges.append(left)
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cur_rows.add(row_idx)
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else:
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clusters.append({
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"mean_x": int(sum(cur_edges) / len(cur_edges)),
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"min_edge": min(cur_edges),
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"max_edge": max(cur_edges),
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"count": len(cur_edges),
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"distinct_rows": len(cur_rows),
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"row_coverage": len(cur_rows) / total_rows,
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})
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cur_edges = [left]
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cur_rows = {row_idx}
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clusters.append({
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"mean_x": int(sum(cur_edges) / len(cur_edges)),
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"min_edge": min(cur_edges),
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"max_edge": max(cur_edges),
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"count": len(cur_edges),
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"distinct_rows": len(cur_rows),
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"row_coverage": len(cur_rows) / total_rows,
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})
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# --- Filter by row coverage ---
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# These thresholds must be high enough to avoid false columns in flowing
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# text (random inter-word gaps) while still detecting real columns in
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# vocabulary worksheets (which typically have >80% row coverage).
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MIN_COVERAGE_PRIMARY = 0.35
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MIN_COVERAGE_SECONDARY = 0.12
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MIN_WORDS_SECONDARY = 4
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MIN_DISTINCT_ROWS = 3
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# Content boundary for left-margin detection
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content_x_min = min(w["left"] for w in words)
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content_x_max = max(w["left"] + w["width"] for w in words)
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content_span = content_x_max - content_x_min
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primary = [
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c for c in clusters
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if c["row_coverage"] >= MIN_COVERAGE_PRIMARY
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and c["distinct_rows"] >= MIN_DISTINCT_ROWS
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]
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primary_ids = {id(c) for c in primary}
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secondary = [
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c for c in clusters
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if id(c) not in primary_ids
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and c["row_coverage"] >= MIN_COVERAGE_SECONDARY
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and c["count"] >= MIN_WORDS_SECONDARY
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and c["distinct_rows"] >= MIN_DISTINCT_ROWS
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]
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# Tertiary: narrow left-margin columns (page refs, markers) that have
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# too few rows for secondary but are clearly left-aligned and separated
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# from the main content. These appear at the far left or far right and
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# have a large gap to the nearest significant cluster.
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used_ids = {id(c) for c in primary} | {id(c) for c in secondary}
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sig_xs = [c["mean_x"] for c in primary + secondary]
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MIN_DISTINCT_ROWS_TERTIARY = max(MIN_DISTINCT_ROWS + 1, 4)
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MIN_COVERAGE_TERTIARY = 0.05 # at least 5% of rows
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tertiary = []
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for c in clusters:
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if id(c) in used_ids:
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continue
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if c["distinct_rows"] < MIN_DISTINCT_ROWS_TERTIARY:
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continue
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if c["row_coverage"] < MIN_COVERAGE_TERTIARY:
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continue
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# Must be near left or right content margin (within 15%)
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rel_pos = (c["mean_x"] - content_x_min) / content_span if content_span else 0.5
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if not (rel_pos < 0.15 or rel_pos > 0.85):
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continue
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# Must have significant gap to nearest significant cluster
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if sig_xs:
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min_dist = min(abs(c["mean_x"] - sx) for sx in sig_xs)
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if min_dist < max(30, content_span * 0.02):
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continue
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tertiary.append(c)
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if tertiary:
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for c in tertiary:
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logger.info(
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" tertiary (margin) cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
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c["mean_x"], c["min_edge"], c["max_edge"],
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c["count"], c["distinct_rows"], c["row_coverage"] * 100,
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)
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significant = sorted(primary + secondary + tertiary, key=lambda c: c["mean_x"])
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for c in significant:
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logger.info(
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" significant cluster: x=%d (range %d-%d), %d words, %d rows (%.0f%%)",
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c["mean_x"], c["min_edge"], c["max_edge"],
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c["count"], c["distinct_rows"], c["row_coverage"] * 100,
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)
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logger.info(
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||
"alignment columns: %d clusters, %d primary, %d secondary → %d significant",
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len(clusters), len(primary), len(secondary), len(significant),
|
||
)
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||
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if not significant:
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# Fallback: single column covering all content
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x_min = min(w["left"] for w in words)
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x_max = max(w["left"] + w["width"] for w in words)
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return [{"index": 0, "type": "column_text", "x_min": x_min, "x_max": x_max}]
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||
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# --- Merge nearby clusters ---
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merge_distance = max(25, int(zone_w * 0.03))
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merged = [significant[0].copy()]
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for s in significant[1:]:
|
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if s["mean_x"] - merged[-1]["mean_x"] < merge_distance:
|
||
prev = merged[-1]
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total = prev["count"] + s["count"]
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prev["mean_x"] = (
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prev["mean_x"] * prev["count"] + s["mean_x"] * s["count"]
|
||
) // total
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prev["count"] = total
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prev["min_edge"] = min(prev["min_edge"], s["min_edge"])
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prev["max_edge"] = max(prev["max_edge"], s["max_edge"])
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prev["distinct_rows"] = max(prev["distinct_rows"], s["distinct_rows"])
|
||
else:
|
||
merged.append(s.copy())
|
||
|
||
logger.info(
|
||
"alignment columns: %d after merge (distance=%d)",
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||
len(merged), merge_distance,
|
||
)
|
||
|
||
# --- Build column boundaries ---
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||
margin = max(5, int(zone_w * 0.005))
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content_x_min = min(w["left"] for w in words)
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content_x_max = max(w["left"] + w["width"] for w in words)
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||
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columns: List[Dict[str, Any]] = []
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for i, cluster in enumerate(merged):
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x_min = max(content_x_min, cluster["min_edge"] - margin)
|
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if i + 1 < len(merged):
|
||
x_max = merged[i + 1]["min_edge"] - margin
|
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else:
|
||
x_max = content_x_max
|
||
|
||
columns.append({
|
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"index": i,
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"type": f"column_{i + 1}" if len(merged) > 1 else "column_text",
|
||
"x_min": x_min,
|
||
"x_max": x_max,
|
||
})
|
||
|
||
return columns
|
||
|
||
|
||
# Characters that are typically OCR artefacts from box border lines.
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||
# Intentionally excludes ! (red markers) and . , ; (real punctuation).
|
||
_GRID_GHOST_CHARS = set("|1lI[](){}/\\-—–_~=+")
|
||
|
||
|
||
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
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||
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
|
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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)
|
||
|
||
|
||
_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
|
||
|
||
|
||
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
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# 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."""
|
||
from cv_vocab_types import PageZone
|
||
|
||
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 _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. Veränderung, 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
|
||
# Skip garbled IPA without brackets (e.g. "ska:f – ska:vz")
|
||
# but NOT text with real IPA symbols (e.g. "Theme [θˈiːm]")
|
||
_REAL_IPA_CHARS = set("ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ")
|
||
if _text_has_garbled_ipa(text) and not any(c in _REAL_IPA_CHARS for c in text):
|
||
continue
|
||
heading_row_indices.append(ri)
|
||
|
||
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 _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": [],
|
||
}
|
||
|
||
# Build cells
|
||
cells = _build_cells(zone_words, columns, rows, 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
|
||
}
|
||
|
||
|
||
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,
|
||
) -> None:
|
||
"""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.
|
||
"""
|
||
if not words or img_w <= 0:
|
||
return
|
||
|
||
margin_cutoff = img_w * 0.30
|
||
# Phase 1: find candidate strips using single-char words
|
||
left_strip = [
|
||
w for w in words
|
||
if len((w.get("text") or "").strip()) == 1
|
||
and w["left"] + w.get("width", 0) / 2 < margin_cutoff
|
||
]
|
||
right_strip = [
|
||
w for w in words
|
||
if len((w.get("text") or "").strip()) == 1
|
||
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) < 8:
|
||
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,
|
||
)
|
||
|
||
|
||
def _filter_footer_words(
|
||
words: List[Dict],
|
||
img_h: int,
|
||
log: Any,
|
||
session_id: str,
|
||
) -> None:
|
||
"""Remove isolated words in the bottom 5% of the page (page numbers).
|
||
|
||
Modifies *words* in place.
|
||
"""
|
||
if not words or img_h <= 0:
|
||
return
|
||
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
|
||
# 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:
|
||
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: removed %d footer words ('%s')",
|
||
session_id, len(footer_words), total_text,
|
||
)
|
||
|
||
|
||
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,
|
||
)
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Core computation (used by build-grid endpoint and regression tests)
|
||
# ---------------------------------------------------------------------------
|
||
|
||
async def _build_grid_core(session_id: str, session: dict) -> dict:
|
||
"""Core grid building logic — pure computation, no HTTP or DB side effects.
|
||
|
||
Args:
|
||
session_id: Session identifier (for logging and image loading).
|
||
session: Full session dict from get_session_db().
|
||
|
||
Returns:
|
||
StructuredGrid result dict.
|
||
|
||
Raises:
|
||
ValueError: If session data is incomplete.
|
||
"""
|
||
t0 = time.time()
|
||
|
||
# 1. Validate and load word results
|
||
word_result = session.get("word_result")
|
||
if not word_result or not word_result.get("cells"):
|
||
raise ValueError("No word results found. Run paddle-kombi or rapid-kombi first.")
|
||
|
||
img_w = word_result.get("image_width", 0)
|
||
img_h = word_result.get("image_height", 0)
|
||
if not img_w or not img_h:
|
||
raise ValueError("Missing image dimensions in word_result")
|
||
|
||
# 2. Flatten all word boxes from cells
|
||
all_words = _flatten_word_boxes(word_result["cells"])
|
||
if not all_words:
|
||
raise ValueError("No word boxes found in cells")
|
||
|
||
logger.info("build-grid session %s: %d words from %d cells",
|
||
session_id, len(all_words), len(word_result["cells"]))
|
||
|
||
# 2b. Filter decorative margin columns (alphabet graphics).
|
||
# Some worksheets have a decorative alphabet strip along one margin
|
||
# (A-Z in a graphic). OCR reads these as single-char words aligned
|
||
# vertically. Detect and remove them before grid building.
|
||
_filter_decorative_margin(all_words, img_w, logger, session_id)
|
||
|
||
# 2c. Filter footer rows (page numbers at the very bottom).
|
||
# Isolated short text in the bottom 5% of the page is typically a
|
||
# page number ("64", "S. 12") and not real content.
|
||
_filter_footer_words(all_words, img_h, logger, session_id)
|
||
|
||
# 2c2. Filter OCR junk from header illustrations.
|
||
# Low-confidence short fragments above the first real content row.
|
||
_filter_header_junk(all_words, img_h, logger, session_id)
|
||
|
||
# 2d. Filter words inside user-defined exclude regions (from Structure step).
|
||
# These are explicitly marked by the user, so ALL words inside are removed
|
||
# regardless of confidence.
|
||
structure_result = session.get("structure_result")
|
||
exclude_rects = []
|
||
if structure_result:
|
||
for er in structure_result.get("exclude_regions", []):
|
||
exclude_rects.append({
|
||
"x": er["x"], "y": er["y"],
|
||
"w": er["w"], "h": er["h"],
|
||
})
|
||
if exclude_rects:
|
||
before = len(all_words)
|
||
filtered = []
|
||
for w in all_words:
|
||
w_cx = w["left"] + w.get("width", 0) / 2
|
||
w_cy = w["top"] + w.get("height", 0) / 2
|
||
inside = any(
|
||
er["x"] <= w_cx <= er["x"] + er["w"]
|
||
and er["y"] <= w_cy <= er["y"] + er["h"]
|
||
for er in exclude_rects
|
||
)
|
||
if not inside:
|
||
filtered.append(w)
|
||
removed = before - len(filtered)
|
||
if removed:
|
||
all_words = filtered
|
||
logger.info(
|
||
"build-grid session %s: removed %d words inside %d user exclude region(s)",
|
||
session_id, removed, len(exclude_rects),
|
||
)
|
||
|
||
# 2e. Filter words inside detected graphic/image regions
|
||
# Only remove LOW-CONFIDENCE words (likely OCR artifacts from images).
|
||
# High-confidence words are real text even if they overlap a detected
|
||
# graphic region (e.g. colored text that graphic detection couldn't
|
||
# fully distinguish from an image).
|
||
_GRAPHIC_CONF_THRESHOLD = 50 # keep words with conf >= 50
|
||
graphic_rects = []
|
||
if structure_result:
|
||
for g in structure_result.get("graphics", []):
|
||
graphic_rects.append({
|
||
"x": g["x"], "y": g["y"],
|
||
"w": g["w"], "h": g["h"],
|
||
})
|
||
if graphic_rects:
|
||
before = len(all_words)
|
||
filtered = []
|
||
for w in all_words:
|
||
w_cx = w["left"] + w.get("width", 0) / 2
|
||
w_cy = w["top"] + w.get("height", 0) / 2
|
||
inside = any(
|
||
gr["x"] <= w_cx <= gr["x"] + gr["w"]
|
||
and gr["y"] <= w_cy <= gr["y"] + gr["h"]
|
||
for gr in graphic_rects
|
||
)
|
||
if inside and w.get("conf", 0) < _GRAPHIC_CONF_THRESHOLD:
|
||
continue # remove low-confidence artifact
|
||
filtered.append(w)
|
||
removed = before - len(filtered)
|
||
if removed:
|
||
all_words = filtered
|
||
logger.info(
|
||
"build-grid session %s: removed %d low-conf words inside %d graphic region(s)",
|
||
session_id, removed, len(graphic_rects),
|
||
)
|
||
|
||
# 3. Load image for box detection
|
||
img_png = await get_session_image(session_id, "cropped")
|
||
if not img_png:
|
||
img_png = await get_session_image(session_id, "dewarped")
|
||
if not img_png:
|
||
img_png = await get_session_image(session_id, "original")
|
||
|
||
zones_data: List[Dict[str, Any]] = []
|
||
boxes_detected = 0
|
||
recovered_count = 0
|
||
border_prefiltered = False
|
||
img_bgr = None
|
||
|
||
content_x, content_y, content_w, content_h = _get_content_bounds(all_words)
|
||
|
||
if img_png:
|
||
# Decode image for color detection + box detection
|
||
arr = np.frombuffer(img_png, dtype=np.uint8)
|
||
img_bgr = cv2.imdecode(arr, cv2.IMREAD_COLOR)
|
||
|
||
if img_bgr is not None:
|
||
# --- Recover colored text that OCR missed (before grid building) ---
|
||
recovered = recover_colored_text(img_bgr, all_words)
|
||
if recovered and graphic_rects:
|
||
# Filter recovered chars inside graphic regions
|
||
recovered = [
|
||
r for r in recovered
|
||
if not any(
|
||
gr["x"] <= r["left"] + r.get("width", 0) / 2 <= gr["x"] + gr["w"]
|
||
and gr["y"] <= r["top"] + r.get("height", 0) / 2 <= gr["y"] + gr["h"]
|
||
for gr in graphic_rects
|
||
)
|
||
]
|
||
if recovered:
|
||
recovered_count = len(recovered)
|
||
all_words.extend(recovered)
|
||
logger.info(
|
||
"build-grid session %s: +%d recovered colored words",
|
||
session_id, recovered_count,
|
||
)
|
||
|
||
# Detect bordered boxes
|
||
boxes = detect_boxes(
|
||
img_bgr,
|
||
content_x=content_x,
|
||
content_w=content_w,
|
||
content_y=content_y,
|
||
content_h=content_h,
|
||
)
|
||
boxes_detected = len(boxes)
|
||
|
||
if boxes:
|
||
# Filter border ghost words before grid building
|
||
all_words, ghost_count = _filter_border_ghosts(all_words, boxes)
|
||
if ghost_count:
|
||
logger.info(
|
||
"build-grid session %s: removed %d border ghost words",
|
||
session_id, ghost_count,
|
||
)
|
||
|
||
# Split page into zones
|
||
page_zones = split_page_into_zones(
|
||
content_x, content_y, content_w, content_h, boxes
|
||
)
|
||
|
||
# Merge content zones separated by box zones
|
||
page_zones = _merge_content_zones_across_boxes(
|
||
page_zones, content_x, content_w
|
||
)
|
||
|
||
# 3b. Detect vertical dividers and split content zones
|
||
vsplit_group_counter = 0
|
||
expanded_zones: List = []
|
||
for pz in page_zones:
|
||
if pz.zone_type != "content":
|
||
expanded_zones.append(pz)
|
||
continue
|
||
zone_words = _words_in_zone(
|
||
all_words, pz.y, pz.height, pz.x, pz.width
|
||
)
|
||
divider_xs = _detect_vertical_dividers(
|
||
zone_words, pz.x, pz.width, pz.y, pz.height
|
||
)
|
||
if divider_xs:
|
||
sub_zones = _split_zone_at_vertical_dividers(
|
||
pz, divider_xs, vsplit_group_counter
|
||
)
|
||
expanded_zones.extend(sub_zones)
|
||
vsplit_group_counter += 1
|
||
# Remove pipe words so they don't appear in sub-zones
|
||
pipe_ids = set(
|
||
id(w) for w in zone_words
|
||
if _PIPE_RE_VSPLIT.match((w.get("text") or "").strip())
|
||
)
|
||
all_words[:] = [w for w in all_words if id(w) not in pipe_ids]
|
||
logger.info(
|
||
"build-grid: vertical split zone %d at x=%s → %d sub-zones",
|
||
pz.index, [int(x) for x in divider_xs], len(sub_zones),
|
||
)
|
||
else:
|
||
expanded_zones.append(pz)
|
||
# Re-index zones
|
||
for i, pz in enumerate(expanded_zones):
|
||
pz.index = i
|
||
page_zones = expanded_zones
|
||
|
||
# --- Union columns from all content zones ---
|
||
# Each content zone detects columns independently. Narrow
|
||
# columns (page refs, markers) may appear in only one zone.
|
||
# Merge column split-points from ALL content zones so every
|
||
# zone shares the full column set.
|
||
# NOTE: Zones from a vertical split are independent and must
|
||
# NOT share columns with each other.
|
||
|
||
# First pass: build grids per zone independently
|
||
zone_grids: List[Dict] = []
|
||
|
||
for pz in page_zones:
|
||
zone_words = _words_in_zone(
|
||
all_words, pz.y, pz.height, pz.x, pz.width
|
||
)
|
||
# Filter recovered single-char artifacts in ALL zones
|
||
# (decorative colored pixel blobs like !, ?, • from
|
||
# recover_colored_text that don't represent real text)
|
||
before = len(zone_words)
|
||
zone_words = [
|
||
w for w in zone_words
|
||
if not (
|
||
w.get("recovered")
|
||
and len(w.get("text", "").strip()) <= 2
|
||
)
|
||
]
|
||
removed = before - len(zone_words)
|
||
if removed:
|
||
logger.info(
|
||
"build-grid: filtered %d recovered artifacts from %s zone %d",
|
||
removed, pz.zone_type, pz.index,
|
||
)
|
||
# Filter words inside image overlay regions (merged box zones)
|
||
if pz.image_overlays:
|
||
before_ov = len(zone_words)
|
||
zone_words = [
|
||
w for w in zone_words
|
||
if not any(
|
||
ov["y"] <= w["top"] + w["height"] / 2 <= ov["y"] + ov["height"]
|
||
and ov["x"] <= w["left"] + w["width"] / 2 <= ov["x"] + ov["width"]
|
||
for ov in pz.image_overlays
|
||
)
|
||
]
|
||
ov_removed = before_ov - len(zone_words)
|
||
if ov_removed:
|
||
logger.info(
|
||
"build-grid: filtered %d words inside image overlays from zone %d",
|
||
ov_removed, pz.index,
|
||
)
|
||
zone_words, bs_removed = _filter_border_strip_words(zone_words)
|
||
if bs_removed:
|
||
border_prefiltered = True
|
||
logger.info(
|
||
"build-grid: pre-filtered %d border-strip words from zone %d",
|
||
bs_removed, pz.index,
|
||
)
|
||
grid = _build_zone_grid(
|
||
zone_words, pz.x, pz.y, pz.width, pz.height,
|
||
pz.index, img_w, img_h,
|
||
skip_first_row_header=bool(pz.image_overlays),
|
||
)
|
||
zone_grids.append({"pz": pz, "words": zone_words, "grid": grid})
|
||
|
||
# Second pass: merge column boundaries from all content zones
|
||
# Exclude zones from vertical splits — they have independent columns.
|
||
content_zones = [
|
||
zg for zg in zone_grids
|
||
if zg["pz"].zone_type == "content"
|
||
and zg["pz"].vsplit_group is None
|
||
]
|
||
if len(content_zones) > 1:
|
||
# Collect column split points (x_min of non-first columns)
|
||
all_split_xs: List[float] = []
|
||
for zg in content_zones:
|
||
raw_cols = zg["grid"].get("_raw_columns", [])
|
||
for col in raw_cols[1:]:
|
||
all_split_xs.append(col["x_min"])
|
||
|
||
if all_split_xs:
|
||
all_split_xs.sort()
|
||
merge_distance = max(25, int(content_w * 0.03))
|
||
merged_xs = [all_split_xs[0]]
|
||
for x in all_split_xs[1:]:
|
||
if x - merged_xs[-1] < merge_distance:
|
||
merged_xs[-1] = (merged_xs[-1] + x) / 2
|
||
else:
|
||
merged_xs.append(x)
|
||
|
||
total_cols = len(merged_xs) + 1
|
||
max_zone_cols = max(
|
||
len(zg["grid"].get("_raw_columns", []))
|
||
for zg in content_zones
|
||
)
|
||
|
||
# Apply union whenever it has at least as many
|
||
# columns as the best single zone. Even with the
|
||
# same count the union boundaries are better because
|
||
# they incorporate evidence from all zones.
|
||
if total_cols >= max_zone_cols:
|
||
cx_min = min(w["left"] for w in all_words)
|
||
cx_max = max(
|
||
w["left"] + w["width"] for w in all_words
|
||
)
|
||
merged_columns: List[Dict[str, Any]] = []
|
||
prev_x = cx_min
|
||
for i, sx in enumerate(merged_xs):
|
||
merged_columns.append({
|
||
"index": i,
|
||
"type": f"column_{i + 1}",
|
||
"x_min": prev_x,
|
||
"x_max": sx,
|
||
})
|
||
prev_x = sx
|
||
merged_columns.append({
|
||
"index": len(merged_xs),
|
||
"type": f"column_{len(merged_xs) + 1}",
|
||
"x_min": prev_x,
|
||
"x_max": cx_max,
|
||
})
|
||
|
||
# Re-build ALL content zones with merged columns
|
||
for zg in zone_grids:
|
||
pz = zg["pz"]
|
||
if pz.zone_type == "content":
|
||
grid = _build_zone_grid(
|
||
zg["words"], pz.x, pz.y,
|
||
pz.width, pz.height,
|
||
pz.index, img_w, img_h,
|
||
global_columns=merged_columns,
|
||
skip_first_row_header=bool(pz.image_overlays),
|
||
)
|
||
zg["grid"] = grid
|
||
logger.info(
|
||
"build-grid session %s: union of %d content "
|
||
"zones → %d merged columns (max single zone: %d)",
|
||
session_id, len(content_zones),
|
||
total_cols, max_zone_cols,
|
||
)
|
||
|
||
for zg in zone_grids:
|
||
pz = zg["pz"]
|
||
grid = zg["grid"]
|
||
# Remove internal _raw_columns before adding to response
|
||
grid.pop("_raw_columns", None)
|
||
|
||
zone_entry: Dict[str, Any] = {
|
||
"zone_index": pz.index,
|
||
"zone_type": pz.zone_type,
|
||
"bbox_px": {
|
||
"x": pz.x, "y": pz.y,
|
||
"w": pz.width, "h": pz.height,
|
||
},
|
||
"bbox_pct": {
|
||
"x": round(pz.x / img_w * 100, 2) if img_w else 0,
|
||
"y": round(pz.y / img_h * 100, 2) if img_h else 0,
|
||
"w": round(pz.width / img_w * 100, 2) if img_w else 0,
|
||
"h": round(pz.height / img_h * 100, 2) if img_h else 0,
|
||
},
|
||
"border": None,
|
||
"word_count": len(zg["words"]),
|
||
**grid,
|
||
}
|
||
|
||
if pz.box:
|
||
zone_entry["border"] = {
|
||
"thickness": pz.box.border_thickness,
|
||
"confidence": pz.box.confidence,
|
||
}
|
||
|
||
if pz.image_overlays:
|
||
zone_entry["image_overlays"] = pz.image_overlays
|
||
|
||
if pz.layout_hint:
|
||
zone_entry["layout_hint"] = pz.layout_hint
|
||
if pz.vsplit_group is not None:
|
||
zone_entry["vsplit_group"] = pz.vsplit_group
|
||
|
||
zones_data.append(zone_entry)
|
||
|
||
# 4. Fallback: no boxes detected → single zone with all words
|
||
if not zones_data:
|
||
# Filter recovered single-char artifacts (same as in zone loop above)
|
||
before = len(all_words)
|
||
filtered_words = [
|
||
w for w in all_words
|
||
if not (w.get("recovered") and len(w.get("text", "").strip()) <= 2)
|
||
]
|
||
removed = before - len(filtered_words)
|
||
if removed:
|
||
logger.info(
|
||
"build-grid session %s: filtered %d recovered artifacts (fallback zone)",
|
||
session_id, removed,
|
||
)
|
||
# Pre-filter border-strip words so column detection is not
|
||
# confused by edge artifacts. When this removes words, Step 4e
|
||
# is skipped (it would otherwise re-detect content as a "strip").
|
||
filtered_words, bs_removed = _filter_border_strip_words(filtered_words)
|
||
if bs_removed:
|
||
border_prefiltered = True
|
||
logger.info(
|
||
"build-grid session %s: pre-filtered %d border-strip words",
|
||
session_id, bs_removed,
|
||
)
|
||
grid = _build_zone_grid(
|
||
filtered_words, content_x, content_y, content_w, content_h,
|
||
0, img_w, img_h,
|
||
)
|
||
grid.pop("_raw_columns", None)
|
||
zones_data.append({
|
||
"zone_index": 0,
|
||
"zone_type": "content",
|
||
"bbox_px": {
|
||
"x": content_x, "y": content_y,
|
||
"w": content_w, "h": content_h,
|
||
},
|
||
"bbox_pct": {
|
||
"x": round(content_x / img_w * 100, 2) if img_w else 0,
|
||
"y": round(content_y / img_h * 100, 2) if img_h else 0,
|
||
"w": round(content_w / img_w * 100, 2) if img_w else 0,
|
||
"h": round(content_h / img_h * 100, 2) if img_h else 0,
|
||
},
|
||
"border": None,
|
||
"word_count": len(all_words),
|
||
**grid,
|
||
})
|
||
|
||
# 4b. Remove junk rows: rows where ALL cells contain only short,
|
||
# low-confidence text (OCR noise, stray marks). Real vocabulary rows
|
||
# have at least one word with conf >= 50 or meaningful text length.
|
||
# Also remove "oversized stub" rows: rows with ≤2 very short words
|
||
# whose word-boxes are significantly taller than the median (e.g.
|
||
# large red page numbers like "( 9" that are not real text content).
|
||
_JUNK_CONF_THRESHOLD = 50
|
||
_JUNK_MAX_TEXT_LEN = 3
|
||
for z in zones_data:
|
||
cells = z.get("cells", [])
|
||
rows = z.get("rows", [])
|
||
if not cells or not rows:
|
||
continue
|
||
|
||
# Compute median word height across the zone for oversized detection
|
||
all_wb_heights = [
|
||
wb["height"]
|
||
for cell in cells
|
||
for wb in cell.get("word_boxes") or []
|
||
if wb.get("height", 0) > 0
|
||
]
|
||
median_wb_h = sorted(all_wb_heights)[len(all_wb_heights) // 2] if all_wb_heights else 28
|
||
|
||
junk_row_indices = set()
|
||
for row in rows:
|
||
ri = row["index"]
|
||
row_cells = [c for c in cells if c.get("row_index") == ri]
|
||
if not row_cells:
|
||
continue
|
||
|
||
row_wbs = [
|
||
wb for cell in row_cells
|
||
for wb in cell.get("word_boxes") or []
|
||
]
|
||
|
||
# Rule 1: ALL word_boxes are low-conf AND short text
|
||
all_junk = True
|
||
for wb in row_wbs:
|
||
text = (wb.get("text") or "").strip()
|
||
conf = wb.get("conf", 0)
|
||
if conf >= _JUNK_CONF_THRESHOLD or len(text) > _JUNK_MAX_TEXT_LEN:
|
||
all_junk = False
|
||
break
|
||
if all_junk and row_wbs:
|
||
junk_row_indices.add(ri)
|
||
continue
|
||
|
||
# Rule 2: oversized stub — ≤3 words, short total text,
|
||
# and word height > 1.8× median (page numbers, stray marks,
|
||
# OCR from illustration labels like "SEA &")
|
||
if len(row_wbs) <= 3:
|
||
total_text = "".join((wb.get("text") or "").strip() for wb in row_wbs)
|
||
max_h = max((wb.get("height", 0) for wb in row_wbs), default=0)
|
||
if len(total_text) <= 5 and max_h > median_wb_h * 1.8:
|
||
junk_row_indices.add(ri)
|
||
continue
|
||
|
||
# Rule 3: scattered debris — rows with only tiny fragments
|
||
# (e.g. OCR artifacts from illustrations/graphics).
|
||
# If the row has no word longer than 2 chars, it's noise.
|
||
longest = max(len((wb.get("text") or "").strip()) for wb in row_wbs)
|
||
if longest <= 2:
|
||
junk_row_indices.add(ri)
|
||
continue
|
||
|
||
if junk_row_indices:
|
||
z["cells"] = [c for c in cells if c.get("row_index") not in junk_row_indices]
|
||
z["rows"] = [r for r in rows if r["index"] not in junk_row_indices]
|
||
logger.info(
|
||
"build-grid: removed %d junk rows from zone %d: %s",
|
||
len(junk_row_indices), z["zone_index"],
|
||
sorted(junk_row_indices),
|
||
)
|
||
|
||
# 4c. Remove oversized word_boxes from individual cells.
|
||
# OCR artifacts from graphics/images (e.g. a huge "N" from a map image)
|
||
# have word heights 3-5x the median. Remove them per-word so they don't
|
||
# pollute cells that also contain valid text in other columns.
|
||
for z in zones_data:
|
||
cells = z.get("cells", [])
|
||
if not cells:
|
||
continue
|
||
all_wh = [
|
||
wb["height"]
|
||
for cell in cells
|
||
for wb in cell.get("word_boxes") or []
|
||
if wb.get("height", 0) > 0
|
||
]
|
||
if not all_wh:
|
||
continue
|
||
med_h = sorted(all_wh)[len(all_wh) // 2]
|
||
oversized_threshold = med_h * 3
|
||
removed_oversized = 0
|
||
for cell in cells:
|
||
wbs = cell.get("word_boxes") or []
|
||
filtered = [wb for wb in wbs if wb.get("height", 0) <= oversized_threshold]
|
||
if len(filtered) < len(wbs):
|
||
removed_oversized += len(wbs) - len(filtered)
|
||
cell["word_boxes"] = filtered
|
||
cell["text"] = _words_to_reading_order_text(filtered)
|
||
if removed_oversized:
|
||
# Remove cells that became empty after oversized removal
|
||
z["cells"] = [c for c in cells if c.get("word_boxes")]
|
||
logger.info(
|
||
"build-grid: removed %d oversized word_boxes (>%dpx) from zone %d",
|
||
removed_oversized, oversized_threshold, z.get("zone_index", 0),
|
||
)
|
||
|
||
# 4d. Remove pipe-character word_boxes (column divider artifacts).
|
||
# OCR reads physical vertical divider lines as "|" or "||" characters.
|
||
# These sit at consistent x positions near column boundaries and pollute
|
||
# cell text. Remove them from word_boxes and rebuild cell text.
|
||
# NOTE: Zones from a vertical split already had pipes removed in step 3b.
|
||
_PIPE_RE = re.compile(r"^\|+$")
|
||
for z in zones_data:
|
||
if z.get("vsplit_group") is not None:
|
||
continue # pipes already removed before split
|
||
removed_pipes = 0
|
||
for cell in z.get("cells", []):
|
||
wbs = cell.get("word_boxes") or []
|
||
filtered = [wb for wb in wbs if not _PIPE_RE.match((wb.get("text") or "").strip())]
|
||
if len(filtered) < len(wbs):
|
||
removed_pipes += len(wbs) - len(filtered)
|
||
cell["word_boxes"] = filtered
|
||
cell["text"] = _words_to_reading_order_text(filtered)
|
||
# Remove cells that became empty after pipe removal
|
||
if removed_pipes:
|
||
z["cells"] = [c for c in z.get("cells", []) if (c.get("word_boxes") or c.get("text", "").strip())]
|
||
logger.info(
|
||
"build-grid: removed %d pipe-divider word_boxes from zone %d",
|
||
removed_pipes, z.get("zone_index", 0),
|
||
)
|
||
|
||
# Also strip pipe chars from word_box text and cell text that may remain
|
||
# from OCR reading syllable-separation marks (e.g. "zu|trau|en" → "zutrauen").
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
for wb in cell.get("word_boxes", []):
|
||
wbt = wb.get("text", "")
|
||
if "|" in wbt:
|
||
wb["text"] = wbt.replace("|", "")
|
||
text = cell.get("text", "")
|
||
if "|" in text:
|
||
cleaned = text.replace("|", "").strip()
|
||
if cleaned != text:
|
||
cell["text"] = cleaned
|
||
|
||
# 4e. Detect and remove page-border decoration strips.
|
||
# Skipped when the pre-filter already removed border words BEFORE
|
||
# column detection — re-running would incorrectly detect the
|
||
# leftmost content column as a "strip".
|
||
border_strip_removed = 0
|
||
if border_prefiltered:
|
||
logger.info("Step 4e: skipped (border pre-filter already applied)")
|
||
else:
|
||
# Some textbooks have decorative alphabet strips along the page
|
||
# edge. OCR picks up scattered letters from these as artifacts.
|
||
# Detection: find the first significant x-gap (>30 px) from each
|
||
# page edge between a small cluster (<20 %) and the main content.
|
||
for z in zones_data:
|
||
cells = z.get("cells", [])
|
||
if not cells:
|
||
continue
|
||
all_wbs_with_cell: List[tuple] = [] # (left, wb, cell)
|
||
for cell in cells:
|
||
for wb in cell.get("word_boxes") or []:
|
||
all_wbs_with_cell.append((wb.get("left", 0), wb, cell))
|
||
if len(all_wbs_with_cell) < 10:
|
||
continue
|
||
all_wbs_with_cell.sort(key=lambda t: t[0])
|
||
total = len(all_wbs_with_cell)
|
||
|
||
# -- Left-edge scan --
|
||
left_strip_count = 0
|
||
left_gap = 0
|
||
running_right = 0
|
||
for gi in range(total - 1):
|
||
running_right = max(
|
||
running_right,
|
||
all_wbs_with_cell[gi][0] + all_wbs_with_cell[gi][1].get("width", 0),
|
||
)
|
||
gap = all_wbs_with_cell[gi + 1][0] - running_right
|
||
if gap > 30:
|
||
left_strip_count = gi + 1
|
||
left_gap = gap
|
||
break
|
||
|
||
# -- Right-edge scan --
|
||
right_strip_count = 0
|
||
right_gap = 0
|
||
running_left = all_wbs_with_cell[-1][0]
|
||
for gi in range(total - 1, 0, -1):
|
||
running_left = min(running_left, all_wbs_with_cell[gi][0])
|
||
prev_right = (
|
||
all_wbs_with_cell[gi - 1][0]
|
||
+ all_wbs_with_cell[gi - 1][1].get("width", 0)
|
||
)
|
||
gap = running_left - prev_right
|
||
if gap > 30:
|
||
right_strip_count = total - gi
|
||
right_gap = gap
|
||
break
|
||
|
||
strip_wbs: set = set()
|
||
strip_side = ""
|
||
strip_gap = 0
|
||
strip_count = 0
|
||
if left_strip_count > 0 and left_strip_count / total < 0.20:
|
||
strip_side = "left"
|
||
strip_count = left_strip_count
|
||
strip_gap = left_gap
|
||
strip_wbs = {id(t[1]) for t in all_wbs_with_cell[:left_strip_count]}
|
||
elif right_strip_count > 0 and right_strip_count / total < 0.20:
|
||
strip_side = "right"
|
||
strip_count = right_strip_count
|
||
strip_gap = right_gap
|
||
strip_wbs = {id(t[1]) for t in all_wbs_with_cell[total - right_strip_count:]}
|
||
|
||
if not strip_wbs:
|
||
continue
|
||
for cell in cells:
|
||
wbs = cell.get("word_boxes") or []
|
||
filtered = [wb for wb in wbs if id(wb) not in strip_wbs]
|
||
if len(filtered) < len(wbs):
|
||
border_strip_removed += len(wbs) - len(filtered)
|
||
cell["word_boxes"] = filtered
|
||
cell["text"] = _words_to_reading_order_text(filtered)
|
||
z["cells"] = [c for c in cells
|
||
if (c.get("word_boxes") or c.get("text", "").strip())]
|
||
logger.info(
|
||
"Step 4e: removed %d border-strip word_boxes (%s) from zone %d "
|
||
"(gap=%dpx, strip=%d/%d wbs)",
|
||
border_strip_removed, strip_side, z.get("zone_index", 0),
|
||
strip_gap, strip_count, total,
|
||
)
|
||
|
||
# 5. Color annotation on final word_boxes in cells
|
||
if img_bgr is not None:
|
||
all_wb: List[Dict] = []
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
all_wb.extend(cell.get("word_boxes", []))
|
||
detect_word_colors(img_bgr, all_wb)
|
||
|
||
# 5a. Heading detection by color + height (after color is available)
|
||
heading_count = _detect_heading_rows_by_color(zones_data, img_w, img_h)
|
||
if heading_count:
|
||
logger.info("Detected %d heading rows by color+height", heading_count)
|
||
|
||
# 5b. Fix unmatched parentheses in cell text
|
||
# OCR often misses opening "(" while detecting closing ")".
|
||
# If a cell's text has ")" without a matching "(", prepend "(".
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
text = cell.get("text", "")
|
||
if ")" in text and "(" not in text:
|
||
cell["text"] = "(" + text
|
||
|
||
# 5c. IPA phonetic correction — replace garbled OCR phonetics with
|
||
# correct IPA from the dictionary (same as in the OCR pipeline).
|
||
# Only applies to vocabulary tables (≥3 columns: EN | article | DE).
|
||
# Single/two-column layouts are continuous text, not vocab tables.
|
||
all_cells = [cell for z in zones_data for cell in z.get("cells", [])]
|
||
total_cols = sum(len(z.get("columns", [])) for z in zones_data)
|
||
if total_cols >= 3:
|
||
# Find the column that contains IPA brackets → English headwords.
|
||
# Count cells with bracket patterns per col_type. The column with
|
||
# the most brackets is the headword column (IPA sits after or below
|
||
# headwords). Falls back to longest-average if no brackets found.
|
||
col_bracket_count: Dict[str, int] = {}
|
||
col_avg_len: Dict[str, List[int]] = {}
|
||
for cell in all_cells:
|
||
ct = cell.get("col_type", "")
|
||
txt = cell.get("text", "") or ""
|
||
col_avg_len.setdefault(ct, []).append(len(txt))
|
||
if ct.startswith("column_") and '[' in txt:
|
||
col_bracket_count[ct] = col_bracket_count.get(ct, 0) + 1
|
||
# Pick column with most bracket IPA patterns
|
||
en_col_type = None
|
||
if col_bracket_count:
|
||
en_col_type = max(col_bracket_count, key=col_bracket_count.get)
|
||
else:
|
||
# Fallback: longest average text
|
||
best_avg = 0
|
||
for ct, lengths in col_avg_len.items():
|
||
if not ct.startswith("column_"):
|
||
continue
|
||
avg = sum(lengths) / len(lengths) if lengths else 0
|
||
if avg > best_avg:
|
||
best_avg = avg
|
||
en_col_type = ct
|
||
if en_col_type:
|
||
for cell in all_cells:
|
||
if cell.get("col_type") == en_col_type:
|
||
cell["_orig_col_type"] = en_col_type
|
||
cell["col_type"] = "column_en"
|
||
fix_cell_phonetics(all_cells, pronunciation="british")
|
||
for cell in all_cells:
|
||
orig = cell.pop("_orig_col_type", None)
|
||
if orig:
|
||
cell["col_type"] = orig
|
||
|
||
# 5d. Fix IPA continuation cells — cells where the printed
|
||
# phonetic transcription wraps to a line below the headword.
|
||
# These contain garbled IPA (e.g. "[n, nn]", "[1uedtX,1]").
|
||
# Replace garbled text with proper IPA looked up from the
|
||
# headword in the previous row's same column.
|
||
# Note: We check ALL columns, not just en_col_type, because
|
||
# the EN headword column may not be the longest-average column.
|
||
_REAL_IPA_CHARS = set("ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ")
|
||
ipa_cont_fixed = 0
|
||
for z in zones_data:
|
||
rows_sorted = sorted(z.get("rows", []), key=lambda r: r["index"])
|
||
z_cells = z.get("cells", [])
|
||
for idx, row in enumerate(rows_sorted):
|
||
if idx == 0:
|
||
continue
|
||
ri = row["index"]
|
||
row_cells = [c for c in z_cells if c.get("row_index") == ri]
|
||
for cell in row_cells:
|
||
ct = cell.get("col_type", "")
|
||
if not ct.startswith("column_"):
|
||
continue
|
||
cell_text = (cell.get("text") or "").strip()
|
||
if not cell_text:
|
||
# Step 5c may have emptied garbled IPA cells like
|
||
# "[n, nn]" — recover text from word_boxes.
|
||
wb_texts = [w.get("text", "")
|
||
for w in cell.get("word_boxes", [])]
|
||
cell_text = " ".join(wb_texts).strip()
|
||
if not cell_text:
|
||
continue
|
||
|
||
is_bracketed = (
|
||
cell_text.startswith('[') and cell_text.endswith(']')
|
||
)
|
||
|
||
if is_bracketed:
|
||
# Bracketed continuation: "[n, nn]", "[klaoz 'daun]"
|
||
# Text like "employee [im'ploi:]" is NOT fully
|
||
# bracketed and won't match here.
|
||
if not _text_has_garbled_ipa(cell_text):
|
||
continue
|
||
# Already has proper IPA brackets → skip
|
||
if re.search(r'\[[^\]]*[ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ][^\]]*\]', cell_text):
|
||
continue
|
||
else:
|
||
# Unbracketed continuation: "ska:f – ska:vz",
|
||
# "'sekandarr sku:l". Only treat as IPA
|
||
# continuation if this is the ONLY content cell
|
||
# in the row (single-cell row) and the text is
|
||
# garbled IPA without real IPA Unicode symbols.
|
||
content_cells_in_row = [
|
||
c for c in row_cells
|
||
if c.get("col_type", "").startswith("column_")
|
||
and c.get("col_type") != "column_1"
|
||
]
|
||
if len(content_cells_in_row) != 1:
|
||
continue
|
||
if not _text_has_garbled_ipa(cell_text):
|
||
continue
|
||
# Has real IPA symbols → already fixed or valid
|
||
if any(c in _REAL_IPA_CHARS for c in cell_text):
|
||
continue
|
||
|
||
# Find headword in previous row, same column
|
||
prev_ri = rows_sorted[idx - 1]["index"]
|
||
prev_same_col = [
|
||
c for c in z_cells
|
||
if c.get("row_index") == prev_ri
|
||
and c.get("col_type") == ct
|
||
]
|
||
if not prev_same_col:
|
||
continue
|
||
prev_text = prev_same_col[0].get("text", "")
|
||
fixed = fix_ipa_continuation_cell(
|
||
cell_text, prev_text, pronunciation="british",
|
||
)
|
||
if fixed != cell_text:
|
||
cell["text"] = fixed
|
||
ipa_cont_fixed += 1
|
||
logger.info(
|
||
"IPA continuation R%d %s: '%s' → '%s'",
|
||
ri, ct, cell_text, fixed,
|
||
)
|
||
if ipa_cont_fixed:
|
||
logger.info("Fixed %d IPA continuation cells", ipa_cont_fixed)
|
||
|
||
# 5e. Heading detection by single-cell rows — black headings like
|
||
# "Theme" that have normal color and height but are the ONLY cell
|
||
# in their row (excluding page_ref column_1). Must run AFTER 5d
|
||
# so IPA continuation cells are already processed.
|
||
single_heading_count = _detect_heading_rows_by_single_cell(zones_data, img_w, img_h)
|
||
if single_heading_count:
|
||
logger.info("Detected %d heading rows by single-cell heuristic", single_heading_count)
|
||
|
||
# 5f. Strip IPA from headings — headings detected in 5e ran AFTER
|
||
# IPA correction (5c), so they may have dictionary IPA appended
|
||
# (e.g. "Theme [θˈiːm]" → "Theme"). Headings should show the
|
||
# original text only.
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
if cell.get("col_type") != "heading":
|
||
continue
|
||
text = cell.get("text", "")
|
||
# Strip trailing IPA bracket: "Theme [θˈiːm]" → "Theme"
|
||
stripped = re.sub(r'\s*\[[^\]]*\]\s*$', '', text).strip()
|
||
if stripped and stripped != text:
|
||
cell["text"] = stripped
|
||
|
||
# 5g. Extract page_ref cells and footer rows from content zones.
|
||
# Page references (column_1 cells like "p.70") sit in rows that
|
||
# also contain vocabulary — extract them as zone metadata without
|
||
# removing the row. Footer lines (e.g. "two hundred and twelve"
|
||
# = page number at bottom) are standalone rows that should be
|
||
# removed from the table entirely.
|
||
_REAL_IPA_CHARS_SET = set("ˈˌəɪɛɒʊʌæɑɔʃʒθðŋ")
|
||
# Page-ref pattern: "p.70", "P.70", ",.65" (garbled "p"), or bare "70"
|
||
_PAGE_REF_RE = re.compile(r'^[pP,]?\s*\.?\s*\d+$')
|
||
for z in zones_data:
|
||
if z.get("zone_type") != "content":
|
||
continue
|
||
cells = z.get("cells", [])
|
||
rows = z.get("rows", [])
|
||
if not rows:
|
||
continue
|
||
|
||
# Extract column_1 cells that look like page references
|
||
page_refs = []
|
||
page_ref_cell_ids = set()
|
||
for cell in cells:
|
||
if cell.get("col_type") != "column_1":
|
||
continue
|
||
text = (cell.get("text") or "").strip()
|
||
if not text:
|
||
continue
|
||
if not _PAGE_REF_RE.match(text):
|
||
continue
|
||
page_refs.append({
|
||
"row_index": cell.get("row_index"),
|
||
"text": text,
|
||
"bbox_pct": cell.get("bbox_pct", {}),
|
||
})
|
||
page_ref_cell_ids.add(cell.get("cell_id"))
|
||
|
||
# Remove page_ref cells from the table (but keep their rows)
|
||
if page_ref_cell_ids:
|
||
z["cells"] = [c for c in z["cells"]
|
||
if c.get("cell_id") not in page_ref_cell_ids]
|
||
|
||
# Detect footer: last non-header row if it has only 1 cell
|
||
# and the text is NOT IPA (no real IPA Unicode symbols).
|
||
# This catches page numbers like "two hundred and twelve".
|
||
footer_rows = []
|
||
non_header_rows = [r for r in rows if not r.get("is_header")]
|
||
if non_header_rows:
|
||
last_row = non_header_rows[-1]
|
||
last_ri = last_row["index"]
|
||
last_cells = [c for c in z["cells"]
|
||
if c.get("row_index") == last_ri]
|
||
if len(last_cells) == 1:
|
||
text = (last_cells[0].get("text") or "").strip()
|
||
# Not IPA (no real IPA symbols) and not a heading
|
||
has_real_ipa = any(c in _REAL_IPA_CHARS_SET for c in text)
|
||
if text and not has_real_ipa and last_cells[0].get("col_type") != "heading":
|
||
footer_rows.append({
|
||
"row_index": last_ri,
|
||
"text": text,
|
||
"bbox_pct": last_cells[0].get("bbox_pct", {}),
|
||
})
|
||
|
||
# Mark footer rows (keep in table, just tag for frontend)
|
||
if footer_rows:
|
||
footer_ris = {fr["row_index"] for fr in footer_rows}
|
||
for r in z["rows"]:
|
||
if r["index"] in footer_ris:
|
||
r["is_footer"] = True
|
||
for c in z["cells"]:
|
||
if c.get("row_index") in footer_ris:
|
||
c["col_type"] = "footer"
|
||
|
||
if page_refs or footer_rows:
|
||
logger.info(
|
||
"Extracted %d page_refs + %d footer rows from zone %d",
|
||
len(page_refs), len(footer_rows), z.get("zone_index", 0),
|
||
)
|
||
|
||
# Store as zone-level metadata
|
||
if page_refs:
|
||
z["page_refs"] = page_refs
|
||
if footer_rows:
|
||
z["footer"] = footer_rows
|
||
|
||
# 5h. Convert slash-delimited IPA to bracket notation.
|
||
# Dictionary-style pages print IPA between slashes: "tiger /'taiga/"
|
||
# Detect the pattern <headword> /ocr_ipa/ and replace with [dict_ipa]
|
||
# using the IPA dictionary when available, falling back to the OCR text.
|
||
# The regex requires a word character (or ² ³) right before the opening
|
||
# slash to avoid false positives like "sb/sth".
|
||
_SLASH_IPA_RE = re.compile(
|
||
r'(\b[a-zA-Z]+[²³¹]?)\s*' # headword (capture group 1)
|
||
r"(/[^/]{2,}/)" # /ipa/ (capture group 2), min 2 chars
|
||
)
|
||
# Standalone slash IPA at start of text (headword on previous line)
|
||
_STANDALONE_SLASH_IPA_RE = re.compile(r'^/([^/]{2,})/')
|
||
# IPA between slashes never contains spaces, parentheses, or commas.
|
||
# Reject matches that look like grammar: "sb/sth up a) jdn/"
|
||
_SLASH_IPA_REJECT_RE = re.compile(r'[\s(),]')
|
||
slash_ipa_fixed = 0
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
text = cell.get("text", "")
|
||
if "/" not in text:
|
||
continue
|
||
|
||
def _replace_slash_ipa(m: re.Match) -> str:
|
||
nonlocal slash_ipa_fixed
|
||
headword = m.group(1)
|
||
ocr_ipa = m.group(2) # includes slashes
|
||
inner_raw = ocr_ipa.strip("/").strip()
|
||
# Reject if inner content has spaces/parens/commas (grammar)
|
||
if _SLASH_IPA_REJECT_RE.search(inner_raw):
|
||
return m.group(0)
|
||
# Strip superscript digits for lookup
|
||
clean_hw = re.sub(r'[²³¹\d]', '', headword).strip()
|
||
ipa = _lookup_ipa(clean_hw, "british") if clean_hw else None
|
||
if ipa:
|
||
slash_ipa_fixed += 1
|
||
return f"{headword} [{ipa}]"
|
||
# Fallback: keep OCR IPA but convert slashes to brackets
|
||
inner = inner_raw.lstrip("'").strip()
|
||
if inner:
|
||
slash_ipa_fixed += 1
|
||
return f"{headword} [{inner}]"
|
||
return m.group(0)
|
||
|
||
new_text = _SLASH_IPA_RE.sub(_replace_slash_ipa, text)
|
||
|
||
# Second pass: convert remaining /ipa/ after [ipa] from first pass.
|
||
# Pattern: [ipa] /ipa2/ → [ipa] [ipa2] (second pronunciation variant)
|
||
_AFTER_BRACKET_SLASH = re.compile(r'(?<=\])\s*(/[^/]{2,}/)')
|
||
def _replace_trailing_slash(m: re.Match) -> str:
|
||
nonlocal slash_ipa_fixed
|
||
inner = m.group(1).strip("/").strip().lstrip("'").strip()
|
||
if _SLASH_IPA_REJECT_RE.search(inner):
|
||
return m.group(0)
|
||
if inner:
|
||
slash_ipa_fixed += 1
|
||
return f" [{inner}]"
|
||
return m.group(0)
|
||
new_text = _AFTER_BRACKET_SLASH.sub(_replace_trailing_slash, new_text)
|
||
|
||
# Handle standalone /ipa/ at start (no headword in this cell)
|
||
if new_text == text:
|
||
m = _STANDALONE_SLASH_IPA_RE.match(text)
|
||
if m:
|
||
inner = m.group(1).strip()
|
||
if not _SLASH_IPA_REJECT_RE.search(inner):
|
||
inner = inner.lstrip("'").strip()
|
||
if inner:
|
||
new_text = "[" + inner + "]" + text[m.end():]
|
||
slash_ipa_fixed += 1
|
||
|
||
if new_text != text:
|
||
cell["text"] = new_text
|
||
|
||
if slash_ipa_fixed:
|
||
logger.info("Step 5h: converted %d slash-IPA to bracket notation", slash_ipa_fixed)
|
||
|
||
# 5i. Remove blue bullet/artifact word_boxes.
|
||
# Dictionary pages have small blue square bullets (■) before entries.
|
||
# OCR reads these as text artifacts (©, e, *, or even plausible words
|
||
# like "fighily" overlapping the real word "tightly").
|
||
# Detection rules:
|
||
# a) Tiny coloured symbols: area < 200 AND conf < 85 (any non-black)
|
||
# b) Overlapping word_boxes: >40% x-overlap → remove lower confidence
|
||
# c) Duplicate text: consecutive blue wbs with identical text, gap < 6px
|
||
bullet_removed = 0
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
wbs = cell.get("word_boxes") or []
|
||
if len(wbs) < 2:
|
||
continue
|
||
to_remove: set = set()
|
||
|
||
# Rule (a): tiny coloured symbols (bullets, graphic fragments)
|
||
for i, wb in enumerate(wbs):
|
||
cn = wb.get("color_name", "black")
|
||
if (cn != "black"
|
||
and wb.get("width", 0) * wb.get("height", 0) < 200
|
||
and wb.get("conf", 100) < 85):
|
||
to_remove.add(i)
|
||
|
||
# Rule (b) + (c): overlap and duplicate detection
|
||
# Sort by x for pairwise comparison
|
||
indexed = sorted(enumerate(wbs), key=lambda iw: iw[1].get("left", 0))
|
||
for p in range(len(indexed) - 1):
|
||
i1, w1 = indexed[p]
|
||
i2, w2 = indexed[p + 1]
|
||
x1s, x1e = w1.get("left", 0), w1.get("left", 0) + w1.get("width", 0)
|
||
x2s, x2e = w2.get("left", 0), w2.get("left", 0) + w2.get("width", 0)
|
||
overlap = max(0, min(x1e, x2e) - max(x1s, x2s))
|
||
min_w = min(w1.get("width", 1), w2.get("width", 1))
|
||
gap = x2s - x1e
|
||
overlap_pct = overlap / min_w if min_w > 0 else 0
|
||
|
||
# (b) Significant x-overlap: remove the lower-confidence one
|
||
if overlap_pct > 0.40:
|
||
c1 = w1.get("conf", 50)
|
||
c2 = w2.get("conf", 50)
|
||
t1 = (w1.get("text") or "").strip().lower()
|
||
t2 = (w2.get("text") or "").strip().lower()
|
||
|
||
# For very high overlap (>90%) with different text,
|
||
# prefer the word that exists in the IPA dictionary
|
||
# over confidence (OCR can give artifacts high conf).
|
||
if overlap_pct > 0.90 and t1 != t2:
|
||
in_dict_1 = bool(_lookup_ipa(re.sub(r'[²³¹\d/]', '', t1), "british")) if t1.isalpha() else False
|
||
in_dict_2 = bool(_lookup_ipa(re.sub(r'[²³¹\d/]', '', t2), "british")) if t2.isalpha() else False
|
||
if in_dict_1 and not in_dict_2:
|
||
to_remove.add(i2)
|
||
continue
|
||
elif in_dict_2 and not in_dict_1:
|
||
to_remove.add(i1)
|
||
continue
|
||
|
||
if c1 < c2:
|
||
to_remove.add(i1)
|
||
elif c2 < c1:
|
||
to_remove.add(i2)
|
||
else:
|
||
# Same confidence: remove the taller one (bullet slivers)
|
||
if w1.get("height", 0) > w2.get("height", 0):
|
||
to_remove.add(i1)
|
||
else:
|
||
to_remove.add(i2)
|
||
|
||
# (c) Duplicate text: consecutive blue with same text, gap < 6px
|
||
elif (gap < 6
|
||
and w1.get("color_name") == "blue"
|
||
and w2.get("color_name") == "blue"
|
||
and (w1.get("text") or "").strip() == (w2.get("text") or "").strip()):
|
||
# Remove the one with lower confidence; if equal, first one
|
||
c1 = w1.get("conf", 50)
|
||
c2 = w2.get("conf", 50)
|
||
to_remove.add(i1 if c1 <= c2 else i2)
|
||
|
||
if to_remove:
|
||
bullet_removed += len(to_remove)
|
||
filtered = [wb for i, wb in enumerate(wbs) if i not in to_remove]
|
||
cell["word_boxes"] = filtered
|
||
cell["text"] = _words_to_reading_order_text(filtered)
|
||
|
||
# Remove cells that became empty after bullet removal
|
||
if bullet_removed:
|
||
for z in zones_data:
|
||
z["cells"] = [c for c in z.get("cells", [])
|
||
if (c.get("word_boxes") or c.get("text", "").strip())]
|
||
logger.info("Step 5i: removed %d bullet/artifact word_boxes", bullet_removed)
|
||
|
||
# 5j-pre. Remove cells whose text is entirely garbled / artifact noise.
|
||
# OCR on image areas produces short nonsensical fragments ("7 EN", "Tr",
|
||
# "\\", "PEE", "a=") that survive earlier filters because their rows also
|
||
# contain real content in other columns. Remove them here.
|
||
_COMMON_SHORT_WORDS = {
|
||
# German
|
||
"ab", "am", "an", "da", "du", "er", "es", "im", "in", "ja",
|
||
"ob", "so", "um", "zu", "wo", "je", "oh", "or",
|
||
"die", "der", "das", "dem", "den", "des", "ein", "und",
|
||
"auf", "aus", "bei", "bis", "für", "mit", "nur", "von",
|
||
# English
|
||
"a", "i", "an", "as", "at", "be", "by", "do", "go", "he",
|
||
"if", "in", "is", "it", "me", "my", "no", "of", "oh", "ok",
|
||
"on", "or", "so", "to", "up", "us", "we",
|
||
"the", "and", "but", "for", "not",
|
||
}
|
||
_PURE_JUNK_RE = re.compile(r'^[\W\d\s]+$')
|
||
artifact_cells_removed = 0
|
||
for z in zones_data:
|
||
before = len(z.get("cells", []))
|
||
kept = []
|
||
for cell in z.get("cells", []):
|
||
text = (cell.get("text") or "").strip()
|
||
core = text.rstrip(".,;:!?'\"")
|
||
is_artifact = False
|
||
if not core:
|
||
is_artifact = True
|
||
elif _PURE_JUNK_RE.match(core):
|
||
is_artifact = True
|
||
elif len(core) <= 2 and core.lower() not in _COMMON_SHORT_WORDS:
|
||
is_artifact = True
|
||
elif len(core) <= 3 and core.isupper() and core.lower() not in _COMMON_SHORT_WORDS:
|
||
is_artifact = True
|
||
elif len(core) <= 5 and re.search(r'\d', core) and re.search(r'[A-Za-z]', core):
|
||
# Mixed digits + letters in short text (e.g. "7 EN", "a=3")
|
||
is_artifact = True
|
||
if is_artifact:
|
||
kept.append(None) # placeholder
|
||
else:
|
||
kept.append(cell)
|
||
z["cells"] = [c for c in kept if c is not None]
|
||
artifact_cells_removed += before - len(z["cells"])
|
||
if artifact_cells_removed:
|
||
# Also remove rows that became completely empty
|
||
for z in zones_data:
|
||
cell_ris = {c.get("row_index") for c in z.get("cells", [])}
|
||
z["rows"] = [r for r in z.get("rows", []) if r["index"] in cell_ris]
|
||
logger.info("Step 5j-pre: removed %d artifact cells", artifact_cells_removed)
|
||
|
||
# 5j. Normalise word_box order to reading order (group by Y, sort by X).
|
||
# The frontend renders colored cells from word_boxes array order
|
||
# (GridTable.tsx), so they MUST be in left-to-right reading order.
|
||
wb_reordered = 0
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
wbs = cell.get("word_boxes") or []
|
||
if len(wbs) < 2:
|
||
continue
|
||
lines = _group_words_into_lines(wbs, y_tolerance_px=15)
|
||
sorted_wbs = [w for line in lines for w in line]
|
||
# Check if order actually changed
|
||
if [id(w) for w in sorted_wbs] != [id(w) for w in wbs]:
|
||
cell["word_boxes"] = sorted_wbs
|
||
wb_reordered += 1
|
||
if wb_reordered:
|
||
logger.info("Step 5j: re-ordered word_boxes in %d cells to reading order", wb_reordered)
|
||
|
||
duration = time.time() - t0
|
||
|
||
# 6. Build result
|
||
total_cells = sum(len(z.get("cells", [])) for z in zones_data)
|
||
total_columns = sum(len(z.get("columns", [])) for z in zones_data)
|
||
total_rows = sum(len(z.get("rows", [])) for z in zones_data)
|
||
|
||
# Collect color statistics from all word_boxes in cells
|
||
color_stats: Dict[str, int] = {}
|
||
for z in zones_data:
|
||
for cell in z.get("cells", []):
|
||
for wb in cell.get("word_boxes", []):
|
||
cn = wb.get("color_name", "black")
|
||
color_stats[cn] = color_stats.get(cn, 0) + 1
|
||
|
||
# Compute layout metrics for faithful grid reconstruction
|
||
all_content_row_heights: List[float] = []
|
||
for z in zones_data:
|
||
for row in z.get("rows", []):
|
||
if not row.get("is_header", False):
|
||
h = row.get("y_max_px", 0) - row.get("y_min_px", 0)
|
||
if h > 0:
|
||
all_content_row_heights.append(h)
|
||
avg_row_height = (
|
||
sum(all_content_row_heights) / len(all_content_row_heights)
|
||
if all_content_row_heights else 30.0
|
||
)
|
||
font_size_suggestion = max(10, int(avg_row_height * 0.6))
|
||
|
||
result = {
|
||
"session_id": session_id,
|
||
"image_width": img_w,
|
||
"image_height": img_h,
|
||
"zones": zones_data,
|
||
"boxes_detected": boxes_detected,
|
||
"summary": {
|
||
"total_zones": len(zones_data),
|
||
"total_columns": total_columns,
|
||
"total_rows": total_rows,
|
||
"total_cells": total_cells,
|
||
"total_words": len(all_words),
|
||
"recovered_colored": recovered_count,
|
||
"color_stats": color_stats,
|
||
},
|
||
"formatting": {
|
||
"bold_columns": [],
|
||
"header_rows": [],
|
||
},
|
||
"layout_metrics": {
|
||
"page_width_px": img_w,
|
||
"page_height_px": img_h,
|
||
"avg_row_height_px": round(avg_row_height, 1),
|
||
"font_size_suggestion_px": font_size_suggestion,
|
||
},
|
||
"duration_seconds": round(duration, 2),
|
||
}
|
||
|
||
return result
|
||
|
||
|
||
# ---------------------------------------------------------------------------
|
||
# Endpoints
|
||
# ---------------------------------------------------------------------------
|
||
|
||
@router.post("/sessions/{session_id}/build-grid")
|
||
async def build_grid(session_id: str):
|
||
"""Build a structured, zone-aware grid from existing Kombi word results.
|
||
|
||
Requires that paddle-kombi or rapid-kombi has already been run on the session.
|
||
Uses the image for box detection and the word positions for grid structuring.
|
||
|
||
Returns a StructuredGrid with zones, each containing their own
|
||
columns, rows, and cells — ready for the frontend Excel-like editor.
|
||
"""
|
||
session = await get_session_db(session_id)
|
||
if not session:
|
||
raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
|
||
|
||
try:
|
||
result = await _build_grid_core(session_id, session)
|
||
except ValueError as e:
|
||
raise HTTPException(status_code=400, detail=str(e))
|
||
|
||
# Persist to DB
|
||
await update_session_db(session_id, grid_editor_result=result)
|
||
|
||
logger.info(
|
||
"build-grid session %s: %d zones, %d cols, %d rows, %d cells, "
|
||
"%d boxes in %.2fs",
|
||
session_id,
|
||
len(result.get("zones", [])),
|
||
result.get("summary", {}).get("total_columns", 0),
|
||
result.get("summary", {}).get("total_rows", 0),
|
||
result.get("summary", {}).get("total_cells", 0),
|
||
result.get("boxes_detected", 0),
|
||
result.get("duration_seconds", 0),
|
||
)
|
||
|
||
return result
|
||
|
||
|
||
@router.post("/sessions/{session_id}/save-grid")
|
||
async def save_grid(session_id: str, request: Request):
|
||
"""Save edited grid data from the frontend Excel-like editor.
|
||
|
||
Receives the full StructuredGrid with user edits (text changes,
|
||
formatting changes like bold columns, header rows, etc.) and
|
||
persists it to the session's grid_editor_result.
|
||
"""
|
||
session = await get_session_db(session_id)
|
||
if not session:
|
||
raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
|
||
|
||
body = await request.json()
|
||
|
||
# Validate basic structure
|
||
if "zones" not in body:
|
||
raise HTTPException(status_code=400, detail="Missing 'zones' in request body")
|
||
|
||
# Preserve metadata from the original build
|
||
existing = session.get("grid_editor_result") or {}
|
||
result = {
|
||
"session_id": session_id,
|
||
"image_width": body.get("image_width", existing.get("image_width", 0)),
|
||
"image_height": body.get("image_height", existing.get("image_height", 0)),
|
||
"zones": body["zones"],
|
||
"boxes_detected": body.get("boxes_detected", existing.get("boxes_detected", 0)),
|
||
"summary": body.get("summary", existing.get("summary", {})),
|
||
"formatting": body.get("formatting", existing.get("formatting", {})),
|
||
"duration_seconds": existing.get("duration_seconds", 0),
|
||
"edited": True,
|
||
}
|
||
|
||
await update_session_db(session_id, grid_editor_result=result)
|
||
|
||
logger.info("save-grid session %s: %d zones saved", session_id, len(body["zones"]))
|
||
|
||
return {"session_id": session_id, "saved": True}
|
||
|
||
|
||
@router.get("/sessions/{session_id}/grid-editor")
|
||
async def get_grid(session_id: str):
|
||
"""Retrieve the current grid editor state for a session."""
|
||
session = await get_session_db(session_id)
|
||
if not session:
|
||
raise HTTPException(status_code=404, detail=f"Session {session_id} not found")
|
||
|
||
result = session.get("grid_editor_result")
|
||
if not result:
|
||
raise HTTPException(
|
||
status_code=404,
|
||
detail="No grid editor data. Run build-grid first.",
|
||
)
|
||
|
||
return result
|