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fix: relax figure caption narrative prose filter and add sequential matching fallback for cross-page captions

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Research Assistant 2026-06-13 20:54:18 +08:00
parent 181d1268b6
commit 68cdc63780
1 changed files with 56 additions and 0 deletions
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56
paperforge/worker/ocr_figures.py
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@ -148,6 +148,8 @@ def _is_body_mention(block: dict) -> bool:
return True
if raw_role == "figure_caption_candidate":
text = block.get("text", "")
if text and _FIGURE_NUMBER_PATTERN.search(text):
return False
if text and _looks_like_figure_narrative_prose(text):
return True
if block.get("block_label", "") == "text":
@ -496,6 +498,7 @@ def build_figure_inventory(structured_blocks: list[dict], page_width: float = 12
and str(block.get("zone") or "") != "display_zone"
and str(block.get("style_family") or "") != "legend_like"
and _looks_like_figure_narrative_prose(block.get("text", ""))
and _extract_figure_number(block.get("text", "")) is None
):
continue
if not _is_formal_legend(block.get("text", ""), block, page_width):
@ -758,6 +761,59 @@ def build_figure_inventory(structured_blocks: list[dict], page_width: float = 12
if i not in used_asset_indices:
unmatched_assets.append(asset)
# Sequential fallback: match unmatched captions to remaining assets in reading order.
# Captions and figures often appear on different pages — humans match them by
# sequential reading order, not spatial proximity. This is a necessary tradeoff.
# Run BEFORE cluster building so sequential matching gets first pick of assets.
if unmatched_legends and unmatched_assets:
sorted_caps = sorted(
unmatched_legends,
key=lambda b: (b.get("page", 0) or 0, (b.get("bbox") or [0, 0, 0, 0])[1]),
)
sorted_asts = sorted(
unmatched_assets,
key=lambda b: (b.get("page", 0) or 0, (b.get("bbox") or [0, 0, 0, 0])[1]),
)
ai = 0
seq_matched: list[dict] = []
for cap in sorted_caps:
fn = _extract_figure_number(cap.get("text", ""))
if fn is None:
continue
while ai < len(sorted_asts):
asset = sorted_asts[ai]
ap = asset.get("page", 0) or 0
cp = cap.get("page", 0) or 0
if ap >= cp:
break
ai += 1
if ai >= len(sorted_asts):
break
asset = sorted_asts[ai]
ai += 1
fig_id = f"figure_{fn:03d}"
caption_score = score_figure_caption(
cap, nearby_media=True, caption_style_match=False, body_prose_likelihood=False
)
seq_matched.append(cap)
matched_figures.append({
"figure_id": fig_id,
"legend_block_id": cap.get("block_id", ""),
"page": cap.get("page", 0),
"text": cap.get("text", ""),
"figure_number": fn,
"matched_assets": [{
"block_id": asset.get("block_id", ""),
"bbox": asset.get("bbox", [0, 0, 0, 0]),
}],
"confidence": 0.35,
"match_score": {"score": 0.35, "decision": "matched", "evidence": ["sequential_fallback"]},
"flags": ["sequential_match"],
"caption_score": caption_score,
})
for cap in seq_matched:
unmatched_legends[:] = [l for l in unmatched_legends if l is not cap]
# Build unresolved clusters: spatial clusters of unmatched assets on
# pages where all candidate legends were rejected (multi-panel figures
# with axis labels or informal captions)