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refactor(ocr-pipeline): use left-edge alignment approach for sub-column detection

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Replace gap-based splitting with alignment-bin approach: cluster word
left-edges within 8px tolerance, find the leftmost bin with >= 10% of
words as the true column start, split off any words to its left as a
sub-column. This correctly handles both page references ("p.59") and
misread exclamation marks ("!" → "I") even when the pixel gap is small.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
This commit is contained in:
Benjamin Admin
2026-03-02 18:56:38 +01:00
parent f13116345b
commit 7252f9a956
2 changed files with 87 additions and 97 deletions
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132
klausur-service/backend/cv_vocab_pipeline.py
View File

@@ -1037,12 +1037,16 @@ def _detect_columns_by_clustering(
def _detect_sub_columns(
geometries: List[ColumnGeometry],
content_w: int,
_edge_tolerance: int = 8,
_min_col_start_ratio: float = 0.10,
) -> List[ColumnGeometry]:
"""Split columns that contain internal sub-columns based on left-edge clustering.
"""Split columns that contain internal sub-columns based on left-edge alignment.
Detects cases where a minority of words in a column are left-aligned at a
different position than the majority (e.g. page references "p.59" next to
vocabulary words).
For each column, clusters word left-edges into alignment bins (within
``_edge_tolerance`` px). The leftmost bin whose word count reaches
``_min_col_start_ratio`` of the column total is treated as the true column
start. Any words to the left of that bin form a sub-column, provided they
number >= 2 and < 35 % of total.
Returns a new list of ColumnGeometry — potentially longer than the input.
"""
@@ -1057,114 +1061,86 @@ def _detect_sub_columns(
continue
# Collect left-edges of confident words
left_edges: List[int] = []
for w in geo.words:
if w.get('conf', 0) >= 30:
left_edges.append(w['left'])
if len(left_edges) < 3:
confident = [w for w in geo.words if w.get('conf', 0) >= 30]
if len(confident) < 3:
result.append(geo)
continue
# Sort and find the largest gap between consecutive left-edge values
sorted_edges = sorted(left_edges)
best_gap = 0
best_gap_pos = 0 # split point: values <= best_gap_pos go left
for i in range(len(sorted_edges) - 1):
gap = sorted_edges[i + 1] - sorted_edges[i]
if gap > best_gap:
best_gap = gap
best_gap_pos = (sorted_edges[i] + sorted_edges[i + 1]) // 2
# --- Cluster left-edges into alignment bins ---
sorted_edges = sorted(w['left'] for w in confident)
bins: List[Tuple[int, int, int, int]] = [] # (center, count, min_edge, max_edge)
cur = [sorted_edges[0]]
for i in range(1, len(sorted_edges)):
if sorted_edges[i] - cur[-1] <= _edge_tolerance:
cur.append(sorted_edges[i])
else:
bins.append((sum(cur) // len(cur), len(cur), min(cur), max(cur)))
cur = [sorted_edges[i]]
bins.append((sum(cur) // len(cur), len(cur), min(cur), max(cur)))
# Gap must be significant relative to column width
min_gap = max(15, int(geo.width * 0.08))
if best_gap < min_gap:
# --- Find the leftmost bin qualifying as a real column start ---
total = len(confident)
min_count = max(3, int(total * _min_col_start_ratio))
col_start_bin = None
for b in bins:
if b[1] >= min_count:
col_start_bin = b
break
if col_start_bin is None:
result.append(geo)
continue
# Split words into left (minority candidate) and right groups
left_words = [w for w in geo.words if w.get('conf', 0) >= 30 and w['left'] <= best_gap_pos]
right_words = [w for w in geo.words if w.get('conf', 0) >= 30 and w['left'] > best_gap_pos]
# Words to the left of the column-start bin are sub-column candidates
split_threshold = col_start_bin[2] - _edge_tolerance
sub_words = [w for w in geo.words if w['left'] < split_threshold]
main_words = [w for w in geo.words if w['left'] >= split_threshold]
# Also include low-conf words by position
for w in geo.words:
if w.get('conf', 0) < 30:
if w['left'] <= best_gap_pos:
left_words.append(w)
else:
right_words.append(w)
total = len(left_words) + len(right_words)
if total == 0:
if len(sub_words) < 2 or len(sub_words) / len(geo.words) >= 0.35:
result.append(geo)
continue
# Determine minority/majority
if len(left_words) <= len(right_words):
minority, majority = left_words, right_words
minority_is_left = True
else:
minority, majority = right_words, left_words
minority_is_left = False
# --- Build two sub-column geometries ---
max_sub_left = max(w['left'] for w in sub_words)
split_x = (max_sub_left + col_start_bin[2]) // 2
# Check minority constraints
minority_ratio = len(minority) / total
if minority_ratio >= 0.35 or len(minority) < 2:
result.append(geo)
continue
sub_x = geo.x
sub_width = split_x - geo.x
main_x = split_x
main_width = (geo.x + geo.width) - split_x
# Build two sub-column geometries
if minority_is_left:
# Minority is left sub-column, majority is right
sub_x = geo.x
sub_width = best_gap_pos - geo.x
main_x = best_gap_pos
main_width = (geo.x + geo.width) - best_gap_pos
else:
# Minority is right sub-column, majority is left
main_x = geo.x
main_width = best_gap_pos - geo.x
sub_x = best_gap_pos
sub_width = (geo.x + geo.width) - best_gap_pos
# Sanity check widths
if sub_width <= 0 or main_width <= 0:
result.append(geo)
continue
sub_geo = ColumnGeometry(
index=0, # will be re-indexed below
index=0,
x=sub_x,
y=geo.y,
width=sub_width,
height=geo.height,
word_count=len(minority),
words=minority,
word_count=len(sub_words),
words=sub_words,
width_ratio=sub_width / content_w if content_w > 0 else 0.0,
)
main_geo = ColumnGeometry(
index=0, # will be re-indexed below
index=0,
x=main_x,
y=geo.y,
width=main_width,
height=geo.height,
word_count=len(majority),
words=majority,
word_count=len(main_words),
words=main_words,
width_ratio=main_width / content_w if content_w > 0 else 0.0,
)
# Insert in left-to-right order
if sub_x < main_x:
result.append(sub_geo)
result.append(main_geo)
else:
result.append(main_geo)
result.append(sub_geo)
result.append(sub_geo)
result.append(main_geo)
logger.info(
f"SubColumnSplit: column idx={geo.index} split at gap={best_gap}px, "
f"minority={len(minority)} words (left={minority_is_left}), "
f"majority={len(majority)} words"
f"SubColumnSplit: column idx={geo.index} split at x={split_x}, "
f"sub={len(sub_words)} words (left), main={len(main_words)} words, "
f"col_start_bin=({col_start_bin[0]}, n={col_start_bin[1]})"
)
# Re-index by left-to-right order

52
klausur-service/backend/tests/test_cv_vocab_pipeline.py
View File

@@ -1177,7 +1177,7 @@ class TestRegionContentCheck:
# =============================================
class TestSubColumnDetection:
"""Tests for _detect_sub_columns() left-edge clustering."""
"""Tests for _detect_sub_columns() left-edge alignment detection."""
def _make_word(self, left: int, text: str = "word", conf: int = 90) -> dict:
return {'left': left, 'top': 100, 'width': 50, 'height': 20,
@@ -1191,27 +1191,46 @@ class TestSubColumnDetection:
)
def test_sub_column_split_page_refs(self):
"""Column with 3 'p.XX' left + 20 EN words right → split into 2."""
"""3 page-refs left + 30 vocab words right → split into 2.
The leftmost bin with >= 10% of words (i.e. >= 4) is the vocab bin
at left=250, so the 3 page-refs are outliers.
"""
content_w = 1000
# 3 page-ref words at left=100, 20 vocab words at left=250
page_words = [self._make_word(100, f"p.{59+i}") for i in range(3)]
vocab_words = [self._make_word(250, f"word{i}") for i in range(20)]
vocab_words = [self._make_word(250, f"word{i}") for i in range(30)]
all_words = page_words + vocab_words
geo = self._make_geo(x=80, width=300, words=all_words, content_w=content_w)
result = _detect_sub_columns([geo], content_w)
assert len(result) == 2, f"Expected 2 columns, got {len(result)}"
# Left sub-column should be narrower with fewer words
left_col = result[0]
right_col = result[1]
assert left_col.x < right_col.x
assert left_col.word_count == 3
assert right_col.word_count == 20
# Indices should be 0, 1
assert right_col.word_count == 30
assert left_col.index == 0
assert right_col.index == 1
def test_sub_column_split_exclamation_marks(self):
"""5 '!' (misread as I/|) left + 80 example words → split into 2.
Mirrors the real-world case where red ! marks are OCR'd as I, |, B, 1
at a position slightly left of the example sentence start.
"""
content_w = 1500
bang_words = [self._make_word(950 + i, chr(ord('I')), conf=60) for i in range(5)]
example_words = [self._make_word(975 + (i * 3), f"word{i}") for i in range(80)]
all_words = bang_words + example_words
geo = self._make_geo(x=940, width=530, words=all_words, content_w=content_w)
result = _detect_sub_columns([geo], content_w)
assert len(result) == 2
assert result[0].word_count == 5
assert result[1].word_count == 80
def test_no_split_uniform_alignment(self):
"""All words aligned at same position → no change."""
content_w = 1000
@@ -1228,7 +1247,6 @@ class TestSubColumnDetection:
content_w = 1000
words = [self._make_word(50, "a")] * 3 + [self._make_word(120, "b")] * 10
geo = self._make_geo(x=40, width=140, words=words, content_w=content_w)
# width_ratio = 140/1000 = 0.14 < 0.15
result = _detect_sub_columns([geo], content_w)
@@ -1241,7 +1259,6 @@ class TestSubColumnDetection:
right_words = [self._make_word(300, f"b{i}") for i in range(12)]
all_words = left_words + right_words
geo = self._make_geo(x=80, width=400, words=all_words, content_w=content_w)
# 8/20 = 0.4 >= 0.35 → no split
result = _detect_sub_columns([geo], content_w)
@@ -1250,26 +1267,23 @@ class TestSubColumnDetection:
def test_sub_column_reindexing(self):
"""After split, indices are correctly 0, 1, 2 across all columns."""
content_w = 1000
# First column: no split
# First column: no split (all words at same alignment)
words1 = [self._make_word(50, f"de{i}") for i in range(10)]
geo1 = ColumnGeometry(index=0, x=30, y=50, width=200, height=500,
word_count=10, words=words1, width_ratio=0.2)
# Second column: will split
# Second column: will split (3 outliers + 30 main)
page_words = [self._make_word(400, f"p.{i}") for i in range(3)]
en_words = [self._make_word(550, f"en{i}") for i in range(15)]
en_words = [self._make_word(550, f"en{i}") for i in range(30)]
geo2 = ColumnGeometry(index=1, x=380, y=50, width=300, height=500,
word_count=18, words=page_words + en_words, width_ratio=0.3)
word_count=33, words=page_words + en_words, width_ratio=0.3)
result = _detect_sub_columns([geo1, geo2], content_w)
assert len(result) == 3
assert [g.index for g in result] == [0, 1, 2]
# First column unchanged
assert result[0].word_count == 10
# Sub-column (page refs)
assert result[1].word_count == 3
# Main column (EN words)
assert result[2].word_count == 15
assert result[2].word_count == 30
def test_no_split_too_few_words(self):
"""Column with fewer than 5 words → no split attempted."""
@@ -1283,10 +1297,10 @@ class TestSubColumnDetection:
assert len(result) == 1
def test_no_split_single_minority_word(self):
"""Only 1 word in minority cluster → no split (need >= 2)."""
"""Only 1 word left of column start → no split (need >= 2)."""
content_w = 1000
minority = [self._make_word(100, "p.59")]
majority = [self._make_word(300, f"w{i}") for i in range(20)]
majority = [self._make_word(300, f"w{i}") for i in range(30)]
geo = self._make_geo(x=80, width=350, words=minority + majority, content_w=content_w)
result = _detect_sub_columns([geo], content_w)