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feat(docling): B1 image-only OCR eval harness (overwatch-cleaned)
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Eval harness for AQA A-level + GCSE-science image-only papers: finalize.py --b1-only,
RapidOCR runner (rapid_pass.py via dsync), GT fixtures (make_b1_gt.py + b1_gt_labels.json),
and fetch_b1_corpus.py to pull the eval corpus from .94 cc.examboards at runtime.

Salvaged from t_15be12ed (which timed out on iteration budget re-running OCR): exam PDFs and
generated OCR caches/reports are NOT committed (third-party copyright + reproducible) — gitignored
and fetched/generated at runtime. Baseline coverage recorded in the task evidence file.

Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
This commit is contained in:
CC Worker 2026-06-08 03:10:10 +00:00
parent 34fc7edd68
commit 69d9c46abe
6 changed files with 676 additions and 12 deletions
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5
api/services/docling/.gitignore vendored Normal file
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@ -0,0 +1,5 @@
# B1 image-only eval corpus + pipeline outputs: fetched/generated at runtime, never committed.
# Exam-board PDFs are third-party copyright (served only via signed URLs); results/ are reproducible.
/samples/b1/
/results/b1_rapid/
/results/final/

139
api/services/docling/finalize.py
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@ -59,6 +59,61 @@ GEOMETRY = [
extract=["--docling", "results/genreport/ocrh556/ocr.json", "--board", "ocr",
"--marks-fill", "results/genreport/ocrh556/marks_fill.json"]),
]
B1_GEOMETRY = [
dict(slug="b1-aqa-biology-7402-1-2023jun", title="AQA A-level Biology 7402/1 2023 Jun (image-only OCR baseline)",
board="aqa", level="A-level", path="B1 image-only OCR (RapidOCR margin-pass)",
storage_loc="cc.examboards/aqa/biology/7402/1/2023-jun/qp.pdf",
pdf="samples/b1/aqa-biology-7402-1-2023jun.pdf",
docling="results/b1_rapid/b1-aqa-biology-7402-1-2023jun/merged.json",
rapid="results/b1_rapid/b1-aqa-biology-7402-1-2023jun/p*.json",
gt_key="b1-aqa-biology-7402-1-2023jun"),
dict(slug="b1-aqa-chemistry-7405-1-2022jun", title="AQA A-level Chemistry 7405/1 2022 Jun (image-only OCR baseline)",
board="aqa", level="A-level", path="B1 image-only OCR (RapidOCR margin-pass)",
storage_loc="cc.examboards/aqa/chemistry/7405/1/2022-jun/qp.pdf",
pdf="samples/b1/aqa-chemistry-7405-1-2022jun.pdf",
docling="results/b1_rapid/b1-aqa-chemistry-7405-1-2022jun/merged.json",
rapid="results/b1_rapid/b1-aqa-chemistry-7405-1-2022jun/p*.json",
gt_key="b1-aqa-chemistry-7405-1-2022jun"),
dict(slug="b1-aqa-physics-7408-1-2022jun", title="AQA A-level Physics 7408/1 2022 Jun (image-only OCR baseline)",
board="aqa", level="A-level", path="B1 image-only OCR (RapidOCR margin-pass)",
storage_loc="cc.examboards/aqa/physics/7408/1/2022-jun/qp.pdf",
pdf="samples/b1/aqa-physics-7408-1-2022jun.pdf",
docling="results/b1_rapid/b1-aqa-physics-7408-1-2022jun/merged.json",
rapid="results/b1_rapid/b1-aqa-physics-7408-1-2022jun/p*.json",
gt_key="b1-aqa-physics-7408-1-2022jun"),
dict(slug="b1-aqa-biology-8461-1h-2022jun", title="AQA GCSE Biology 8461/1H 2022 Jun (image-only OCR baseline)",
board="aqa", level="GCSE", path="B1 image-only OCR (RapidOCR margin-pass)",
storage_loc="cc.examboards/aqa/biology/8461/1h/2022-jun/qp.pdf",
pdf="samples/b1/aqa-biology-8461-1h-2022jun.pdf",
docling="results/b1_rapid/b1-aqa-biology-8461-1h-2022jun/merged.json",
rapid="results/b1_rapid/b1-aqa-biology-8461-1h-2022jun/p*.json",
gt_key="b1-aqa-biology-8461-1h-2022jun"),
dict(slug="b1-aqa-chemistry-8462-1h-2022jun", title="AQA GCSE Chemistry 8462/1H 2022 Jun (image-only OCR baseline)",
board="aqa", level="GCSE", path="B1 image-only OCR (RapidOCR margin-pass)",
storage_loc="cc.examboards/aqa/chemistry/8462/1h/2022-jun/qp.pdf",
pdf="samples/b1/aqa-chemistry-8462-1h-2022jun.pdf",
docling="results/b1_rapid/b1-aqa-chemistry-8462-1h-2022jun/merged.json",
rapid="results/b1_rapid/b1-aqa-chemistry-8462-1h-2022jun/p*.json",
gt_key="b1-aqa-chemistry-8462-1h-2022jun"),
dict(slug="b1-aqa-combined-8464-b1h-2022jun", title="AQA GCSE Combined Science Trilogy 8464/B/1H 2022 Jun (image-only OCR baseline)",
board="aqa", level="GCSE", path="B1 image-only OCR (RapidOCR margin-pass)",
storage_loc="cc.examboards/aqa/combined-science-trilogy/8464/b-1h/2022-jun/qp.pdf",
pdf="samples/b1/aqa-combined-8464-b1h-2022jun.pdf",
docling="results/b1_rapid/b1-aqa-combined-8464-b1h-2022jun/merged.json",
rapid="results/b1_rapid/b1-aqa-combined-8464-b1h-2022jun/p*.json",
gt_key="b1-aqa-combined-8464-b1h-2022jun"),
dict(slug="b1-aqa-combined-8464-c1h-2022jun", title="AQA GCSE Combined Science Trilogy 8464/C/1H 2022 Jun (image-only OCR baseline; 8465 not present in dev catalogue)",
board="aqa", level="GCSE", path="B1 image-only OCR (RapidOCR margin-pass)",
storage_loc="cc.examboards/aqa/combined-science-trilogy/8464/c-1h/2022-jun/qp.pdf",
pdf="samples/b1/aqa-combined-8464-c1h-2022jun.pdf",
docling="results/b1_rapid/b1-aqa-combined-8464-c1h-2022jun/merged.json",
rapid="results/b1_rapid/b1-aqa-combined-8464-c1h-2022jun/p*.json",
gt_key="b1-aqa-combined-8464-c1h-2022jun"),
]
GT_LABELS_PATH = "fixtures/b1_gt_labels.json"
FAST = [
dict(slug="aqa-physics-7408-fast", title="AQA A-level Physics 7408/1 (born-digital)", board="aqa",
level="A-level", pdf="samples/extra/aqa-alevel-physics-7408-1-jun22-qp.pdf",
@ -95,16 +150,65 @@ def jload(p):
return {}
def stats_from(struct, val):
def load_gt_labels():
try:
return json.load(open(GT_LABELS_PATH))
except Exception:
return {}
def part_labels(struct):
labels = []
for q in struct.get("questions", []) or []:
for part in q.get("parts", []) or []:
lab = part.get("label")
if lab:
labels.append(lab)
return labels
def coverage_against_labels(struct, labels):
if not labels:
return None
rec = set(part_labels(struct))
gt = set(labels)
hit = sorted(rec & gt)
miss = sorted(gt - rec)
return {"coverage_pct": round(len(hit) / len(gt) * 100, 1),
"recovered": len(hit), "total": len(gt), "missed": miss,
"source": "fixtures/b1_gt_labels.json"}
def answer_region_count(struct):
top = len(struct.get("regions", []) or [])
per_part = 0
for q in struct.get("questions", []) or []:
for part in q.get("parts", []) or []:
per_part += len(part.get("regions", []) or [])
return top + per_part
def ensure_rapid_cache(p):
if os.path.exists(p["docling"]):
return True
if not os.path.exists(p["pdf"]):
print(f" ! missing source PDF for {p['slug']}: {p['pdf']} (storage_loc={p.get('storage_loc')})")
return False
return run(["scripts/rapid_pass.py", p["pdf"], "b1_rapid/" + p["slug"]])
def stats_from(struct, val, gt_labels=None):
st = struct.get("stats", {}) or {}
mc = st.get("marks_check") or {}
cov = struct.get("coverage", {}) or {}
cov = coverage_against_labels(struct, gt_labels) if gt_labels else (struct.get("coverage", {}) or {})
return {
"board": struct.get("board"), "paper_code": struct.get("paper_code"),
"n_questions": st.get("n_questions"), "n_parts": st.get("n_parts"),
"marks_sum": mc.get("sum"), "official_max": mc.get("expected_max"),
"marks_pct": mc.get("pct"),
"coverage_pct": cov.get("coverage_pct"), "coverage_missed": cov.get("missed", []),
"coverage_pct": cov.get("coverage_pct"), "coverage_recovered": cov.get("recovered"),
"coverage_total": cov.get("total"), "coverage_source": cov.get("source"),
"coverage_missed": cov.get("missed", []), "answer_regions": answer_region_count(struct),
"validate_verdict": (val.get("summary") or {}).get("worst_severity"),
"validate_flags": val.get("flags", []),
"questions_expected": (val.get("summary") or {}).get("questions_expected"),
@ -113,12 +217,15 @@ def stats_from(struct, val):
}
def do_geometry(p, overlays):
def do_geometry(p, overlays, gt_labels=None, prepare_ocr=False):
d = os.path.join(FINAL, p["slug"]); os.makedirs(d, exist_ok=True)
S, F, B, R, T, V = (os.path.join(d, f) for f in
("structured.json", "furniture.json", "bands.json", "page_roles.json",
"template.json", "validate.json"))
ex = ["extract.py"] + p["extract"] + ["--out", S]
if prepare_ocr and not ensure_rapid_cache(p):
raise RuntimeError(f"unable to prepare B1 OCR cache for {p['slug']}")
extract_args = p.get("extract") or ["--docling", p["docling"], "--rapid", p["rapid"], "--board", p.get("board", "aqa")]
ex = ["extract.py"] + extract_args + ["--out", S]
if p.get("gt"):
ex += ["--gt", p["gt"]]
run(ex)
@ -138,7 +245,7 @@ def do_geometry(p, overlays):
odbg = os.path.join(d, "overlays", "debug")
run(["scripts/overlay.py", S, p["pdf"], "--docling", p["docling"], "--bands", B,
"--furniture", F, "--pages", "1,2,3,4,5", "--dpi", "120", "--out", odbg])
return stats_from(jload(S), jload(V)), d
return stats_from(jload(S), jload(V), gt_labels), d
def do_fast(p):
@ -164,6 +271,7 @@ def per_paper_report(p, s, d, kind):
+ (f" (missed {s['coverage_missed'][:8]})" if s.get('coverage_missed') else "")
if s['coverage_pct'] is not None else "- **coverage vs GT:** n/a",
f"- **G6 verdict:** {s['validate_verdict']}",
f"- **answer-region count:** {s.get('answer_regions')}",
]
if s["validate_flags"]:
lines += ["", "**Flags (human-review hints):**"] + [f"- {f}" for f in s["validate_flags"]]
@ -178,21 +286,28 @@ def per_paper_report(p, s, d, kind):
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--no-overlays", action="store_true")
ap.add_argument("--b1-only", action="store_true", help="run only the Sprint B1 image-only OCR eval corpus")
ap.add_argument("--prepare-ocr", action="store_true", help="populate missing B1 RapidOCR caches via dsync before running")
a = ap.parse_args()
os.makedirs(FINAL, exist_ok=True)
catalog = {"generated_at": datetime.datetime.now().isoformat(timespec="seconds"),
"papers": []}
total_imgs = 0
for p in GEOMETRY:
gt_fixtures = load_gt_labels()
geometry = B1_GEOMETRY if a.b1_only else GEOMETRY
fast = [] if a.b1_only else FAST
for p in geometry:
print(f"[geometry] {p['slug']}")
s, d = do_geometry(p, not a.no_overlays)
gt_labels = (gt_fixtures.get(p.get("gt_key") or p["slug"], {}) or {}).get("labels")
s, d = do_geometry(p, not a.no_overlays, gt_labels=gt_labels, prepare_ocr=a.prepare_ocr)
n = per_paper_report(p, s, d, p["path"])
total_imgs += n
catalog["papers"].append({**{k: p[k] for k in ("slug", "title", "board", "level")},
"kind": "geometry", "path": p["path"], "dir": d,
"overlay_images": n, **s})
for p in FAST:
for p in fast:
print(f"[fast] {p['slug']}")
s, d = do_fast(p)
per_paper_report(p, s, d, "born-digital fast-path")
@ -214,13 +329,13 @@ def write_index(catalog, total_imgs):
"`overlays/template/` (human-review view, all pages) and `overlays/debug/` (raw-detection view).",
"Machine catalog: `catalog.json`.", "",
"## Image-only / OCR-path (with geometry + overlays)", "",
"| Paper | Board / level | Q/parts | Marks/max | Coverage | G6 | Images |",
"|---|---|---|---|---|---|---|"]
"| Paper | Board / level | Q/parts | Marks/max | Coverage | Answer regions | G6 | Images |",
"|---|---|---|---|---|---|---|---|"]
for p in g:
cov = f"{p['coverage_pct']}%" if p['coverage_pct'] is not None else "n/a"
L.append(f"| [{p['title']}]({p['slug']}/report.md) | {p['board']} {p['level']} | "
f"{p['n_questions']}/{p['n_parts']} | {p['marks_sum']}/{p['official_max']} "
f"({p['marks_pct']}%) | {cov} | {p['validate_verdict']} | "
f"({p['marks_pct']}%) | {cov} | {p.get('answer_regions')} | {p['validate_verdict']} | "
f"{p['overlay_images']} |")
L += ["", "## Born-digital fast-path (CPU, no geometry)", "",
"| Paper | Board / level | Q/parts | Marks/max | Coverage | G6 |",

356
api/services/docling/fixtures/b1_gt_labels.json Normal file
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@ -0,0 +1,356 @@
{
"b1-aqa-biology-7402-1-2023jun": {
"source_pdf": "cc.examboards/aqa/biology/7402/1/2023-jun/qp.pdf",
"source_method": "AQA born-digital text-layer parsed with existing extract.py AQA grammar; used as reproducible GT label set for image-only OCR baseline.",
"board_detected": "aqa",
"paper_code_detected": "7402/1",
"labels": [
"01.1",
"01.2",
"01.3",
"02.1",
"02.2",
"02.3",
"03.1",
"03.2",
"03.3",
"03.4",
"03.5",
"04.1",
"04.2",
"04.3",
"05.1",
"05.2",
"05.3",
"05.4",
"05.5",
"06.1",
"06.2",
"06.3",
"06.4",
"07.1",
"07.2",
"89.6",
"08.1",
"08.2",
"08.3",
"08.4",
"09.1",
"09.2",
"09.3",
"09.4",
"09.5",
"09.6",
"10.1",
"10.2",
"10.3"
]
},
"b1-aqa-chemistry-7405-1-2022jun": {
"source_pdf": "cc.examboards/aqa/chemistry/7405/1/2022-jun/qp.pdf",
"source_method": "AQA born-digital text-layer parsed with existing extract.py AQA grammar; used as reproducible GT label set for image-only OCR baseline.",
"board_detected": "aqa",
"paper_code_detected": "7405/1",
"labels": [
"01.1",
"01.2",
"01.3",
"01.4",
"01.5",
"01.6",
"02.1",
"02.2",
"02.3",
"02.4",
"02.5",
"03.1",
"03.2",
"03.3",
"03.4",
"03.5",
"04.1",
"04.2",
"04.3",
"04.4",
"04.5",
"05.1",
"05.2",
"05.3",
"05.4",
"05.5",
"05.6",
"05.7",
"06.1",
"06.2",
"06.3",
"06.4",
"06.5",
"06.6",
"06.7",
"07.1",
"07.2",
"07.3",
"07.4",
"07.5",
"07.6",
"07.7",
"08.1",
"08.2",
"08.3",
"08.4",
"08.5"
]
},
"b1-aqa-physics-7408-1-2022jun": {
"source_pdf": "cc.examboards/aqa/physics/7408/1/2022-jun/qp.pdf",
"source_method": "AQA born-digital text-layer parsed with existing extract.py AQA grammar; used as reproducible GT label set for image-only OCR baseline.",
"board_detected": "aqa",
"paper_code_detected": "7408/1",
"labels": [
"01.1",
"01.2",
"01.3",
"01.4",
"01.5",
"02.1",
"02.2",
"02.3",
"02.4",
"03.1",
"03.2",
"03.3",
"03.4",
"03.5",
"04.1",
"04.2",
"04.3",
"04.4",
"04.5",
"05.1",
"05.2",
"05.3",
"05.4",
"05.5",
"05.6",
"06.1",
"06.2",
"06.3",
"07.0",
"08.0",
"09.0",
"10.0",
"11.0",
"12.0",
"13.0",
"14.0",
"15.0",
"16.0",
"17.0",
"18.0",
"19.0",
"20.0",
"21.0",
"22.0",
"23.0",
"24.0",
"25.0",
"26.0",
"27.0",
"28.0",
"29.0",
"30.0",
"31.0"
]
},
"b1-aqa-biology-8461-1h-2022jun": {
"source_pdf": "cc.examboards/aqa/biology/8461/1h/2022-jun/qp.pdf",
"source_method": "AQA born-digital text-layer parsed with existing extract.py AQA grammar; used as reproducible GT label set for image-only OCR baseline.",
"board_detected": "aqa",
"paper_code_detected": "8461/1",
"labels": [
"01.1",
"01.2",
"01.3",
"01.4",
"01.5",
"01.6",
"01.7",
"01.8",
"01.9",
"02.1",
"02.2",
"02.3",
"02.4",
"02.5",
"02.6",
"03.1",
"03.2",
"03.3",
"03.4",
"03.5",
"04.1",
"04.2",
"04.3",
"04.4",
"04.5",
"05.1",
"05.2",
"05.3",
"05.4",
"05.5",
"06.1",
"06.2",
"06.3",
"06.4",
"06.5",
"07.1",
"07.2",
"07.3",
"07.4",
"07.5",
"07.6",
"07.7",
"07.8"
]
},
"b1-aqa-chemistry-8462-1h-2022jun": {
"source_pdf": "cc.examboards/aqa/chemistry/8462/1h/2022-jun/qp.pdf",
"source_method": "AQA born-digital text-layer parsed with existing extract.py AQA grammar; used as reproducible GT label set for image-only OCR baseline.",
"board_detected": "aqa",
"paper_code_detected": "8462/1",
"labels": [
"01.1",
"01.2",
"01.3",
"01.4",
"01.5",
"01.6",
"01.7",
"02.1",
"02.2",
"02.3",
"02.4",
"02.5",
"02.6",
"03.1",
"03.2",
"03.3",
"03.4",
"03.5",
"04.1",
"04.2",
"04.3",
"04.4",
"04.5",
"04.6",
"04.7",
"05.1",
"05.2",
"05.3",
"05.4",
"05.5",
"06.1",
"06.2",
"06.3",
"06.4",
"06.5",
"06.6",
"07.1",
"07.2",
"07.3",
"07.4",
"07.5",
"07.6",
"08.1",
"08.2",
"08.3",
"08.4",
"08.5"
]
},
"b1-aqa-combined-8464-b1h-2022jun": {
"source_pdf": "cc.examboards/aqa/combined-science-trilogy/8464/b-1h/2022-jun/qp.pdf",
"source_method": "AQA born-digital text-layer parsed with existing extract.py AQA grammar; used as reproducible GT label set for image-only OCR baseline.",
"board_detected": "aqa",
"paper_code_detected": null,
"labels": [
"01.1",
"01.2",
"01.3",
"01.4",
"01.5",
"01.6",
"01.7",
"01.8",
"02.1",
"02.2",
"02.3",
"02.4",
"02.5",
"02.6",
"02.7",
"03.1",
"03.2",
"03.3",
"03.4",
"03.5",
"03.6",
"03.7",
"04.1",
"04.2",
"04.3",
"04.4",
"04.5",
"05.1",
"05.2",
"05.3",
"05.4",
"05.5",
"05.6",
"06.1",
"06.2",
"06.3"
]
},
"b1-aqa-combined-8464-c1h-2022jun": {
"source_pdf": "cc.examboards/aqa/combined-science-trilogy/8464/c-1h/2022-jun/qp.pdf",
"source_method": "AQA born-digital text-layer parsed with existing extract.py AQA grammar; used as reproducible GT label set for image-only OCR baseline.",
"board_detected": "aqa",
"paper_code_detected": null,
"labels": [
"01.1",
"01.2",
"01.3",
"01.4",
"01.5",
"02.1",
"02.2",
"02.3",
"02.4",
"02.5",
"03.0",
"04.1",
"04.2",
"04.3",
"04.4",
"04.5",
"04.6",
"04.7",
"05.1",
"05.2",
"05.3",
"05.4",
"06.1",
"06.2",
"06.3",
"06.4",
"06.5",
"07.1",
"07.2",
"07.3",
"07.4",
"07.5",
"07.6"
]
}
}

87
api/services/docling/scripts/fetch_b1_corpus.py Normal file
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@ -0,0 +1,87 @@
#!/usr/bin/env python3
"""Populate the gitignored B1 image-only eval corpus from the .94 exam-board store.
The B1 eval papers are NOT committed (third-party copyright; served only via signed URLs).
This script downloads each B1_GEOMETRY paper's `storage_loc` object from cc.examboards via the
Storage API into its local `pdf` path (under samples/b1/), so finalize.py --b1-only and the
B1-2/B1-3 generalization work can run against a real corpus.
Run from api/services/docling/ inside the cc-api-dev container (SUPABASE_URL/SERVICE_ROLE_KEY in env):
python3 scripts/fetch_b1_corpus.py # fetch all B1 papers (skip existing)
python3 scripts/fetch_b1_corpus.py --force # re-download
python3 scripts/fetch_b1_corpus.py --only b1-aqa-physics-7408-1-2022jun
python3 scripts/fetch_b1_corpus.py --list # show what would be fetched, no download
"""
from __future__ import annotations
import argparse
import os
import sys
# Import the canonical B1 corpus definition (slug, storage_loc, local pdf path) from finalize.
_HERE = os.path.dirname(os.path.abspath(__file__))
_DOCLING_DIR = os.path.dirname(_HERE)
sys.path.insert(0, _DOCLING_DIR)
from finalize import B1_GEOMETRY # noqa: E402
def _split_storage_loc(storage_loc: str) -> tuple[str, str]:
"""'cc.examboards/aqa/biology/7402/1/2023-jun/qp.pdf' -> ('cc.examboards', 'aqa/.../qp.pdf')."""
bucket, _, path = storage_loc.partition("/")
if not bucket or not path:
raise ValueError(f"malformed storage_loc: {storage_loc!r}")
return bucket, path
def _entries(only: str | None):
for p in B1_GEOMETRY:
loc = p.get("storage_loc")
pdf = p.get("pdf")
if not loc or not pdf:
continue
if only and p.get("slug") != only:
continue
yield p["slug"], loc, pdf
def main() -> int:
ap = argparse.ArgumentParser(description="Fetch the B1 image-only eval corpus from .94 cc.examboards")
ap.add_argument("--force", action="store_true", help="re-download even if the local file exists")
ap.add_argument("--only", help="fetch a single paper by slug")
ap.add_argument("--list", action="store_true", help="list what would be fetched and exit")
args = ap.parse_args()
todo = list(_entries(args.only))
if not todo:
print("no matching B1 papers", file=sys.stderr)
return 1
if args.list:
for slug, loc, pdf in todo:
print(f"{slug}\t{loc}\t-> {pdf}")
return 0
from modules.database.supabase.utils.storage import StorageAdmin
storage = StorageAdmin()
ok = skipped = 0
for slug, loc, pdf in todo:
dest = os.path.join(_DOCLING_DIR, pdf) if not os.path.isabs(pdf) else pdf
if os.path.exists(dest) and not args.force:
print(f"[skip] {slug} (exists)")
skipped += 1
continue
bucket, path = _split_storage_loc(loc)
data = storage.download_file(bucket, path)
os.makedirs(os.path.dirname(dest), exist_ok=True)
with open(dest, "wb") as fh:
fh.write(data)
print(f"[ok] {slug} <- {bucket}/{path} ({len(data)} bytes)")
ok += 1
print(f"fetched {ok}, skipped {skipped}, of {len(todo)}")
return 0
if __name__ == "__main__":
raise SystemExit(main())

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import json, sys
from pathlib import Path
base=Path('/app/api/services/docling')
sys.path.insert(0, str(base))
import extract
papers=[
('b1-aqa-biology-7402-1-2023jun','samples/b1/aqa-biology-7402-1-2023jun.pdf','cc.examboards/aqa/biology/7402/1/2023-jun/qp.pdf'),
('b1-aqa-chemistry-7405-1-2022jun','samples/b1/aqa-chemistry-7405-1-2022jun.pdf','cc.examboards/aqa/chemistry/7405/1/2022-jun/qp.pdf'),
('b1-aqa-physics-7408-1-2022jun','samples/b1/aqa-physics-7408-1-2022jun.pdf','cc.examboards/aqa/physics/7408/1/2022-jun/qp.pdf'),
('b1-aqa-biology-8461-1h-2022jun','samples/b1/aqa-biology-8461-1h-2022jun.pdf','cc.examboards/aqa/biology/8461/1h/2022-jun/qp.pdf'),
('b1-aqa-chemistry-8462-1h-2022jun','samples/b1/aqa-chemistry-8462-1h-2022jun.pdf','cc.examboards/aqa/chemistry/8462/1h/2022-jun/qp.pdf'),
('b1-aqa-combined-8464-b1h-2022jun','samples/b1/aqa-combined-8464-b1h-2022jun.pdf','cc.examboards/aqa/combined-science-trilogy/8464/b-1h/2022-jun/qp.pdf'),
('b1-aqa-combined-8464-c1h-2022jun','samples/b1/aqa-combined-8464-c1h-2022jun.pdf','cc.examboards/aqa/combined-science-trilogy/8464/c-1h/2022-jun/qp.pdf'),
]
out={}
for slug, rel, storage in papers:
lines=extract.lines_from_pdftext(str(base/rel))
board, code=extract.detect_board(lines)
if board != 'aqa':
raise RuntimeError(f'{slug}: expected AQA board, detected {board!r} ({code!r})')
parts=extract.parse_text_by_board(lines, board)
labels=list(parts)
out[slug]={
'source_pdf': storage,
'source_method': 'AQA born-digital text-layer parsed with existing extract.py AQA grammar; used as reproducible GT label set for image-only OCR baseline.',
'board_detected': board,
'paper_code_detected': code,
'labels': labels,
}
print(slug, board, code, len(labels), labels[:5], labels[-5:])
Path(base/'fixtures').mkdir(exist_ok=True)
Path(base/'fixtures/b1_gt_labels.json').write_text(json.dumps(out, indent=2)+"\n")

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#!/usr/bin/env python3
"""
rapid_pass.py — generalise the proven AQA "RapidOCR margin-pass" (95.2% on the image-only
8463 paper) to any AQA paper. Born-digital AQA QPs ship a text layer, so we force RapidOCR
over the *rendered* page (`force_ocr:true`) to simulate the image-only redistribution case
and recover the boxed `NN.M` question numbers Tesseract shatters.
For each page it writes results/<outdir>/p{N}.json (a full per-page DoclingDocument, the
shape extract.py's aqa_questions_rapid expects) and a merged.json (for board / front-matter
detection). All GPU work is serialised + OOM-resilient through dsync.
Usage:
python scripts/rapid_pass.py samples/extra/aqa-alevel-physics-7408-1-jun22-qp.pdf rapid_7408
python scripts/rapid_pass.py <pdf> <outdir-slug> [first_page] [last_page]
"""
import os, sys, json, subprocess, re
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import dsync
OPTS = {"ocr_engine": "rapidocr", "force_ocr": True}
def npages(pdf):
out = subprocess.check_output(["pdfinfo", pdf]).decode()
return int(out.split("Pages:")[1].split()[0])
def main():
pdf = sys.argv[1]
slug = sys.argv[2]
if os.path.isabs(slug) or ".." in slug.split(os.sep) or not re.fullmatch(r"[A-Za-z0-9._/-]+", slug):
raise SystemExit(f"unsafe output slug: {slug!r}")
n = npages(pdf)
first = int(sys.argv[3]) if len(sys.argv) > 3 else 1
last = min(int(sys.argv[4]), n) if len(sys.argv) > 4 else n
if first > n or first > last:
print(f"requested page range {first}-{last} is outside PDF ({n} pages); nothing to do")
return
outdir = os.path.join("results", slug)
os.makedirs(outdir, exist_ok=True)
r = dsync._redis()
print(f"redis: {'connected' if r else 'NO CACHE'} pdf={pdf} pages {first}-{last}/{n}")
merged = {"texts": [], "tables": [], "pictures": [], "pages": {}, "_failed_pages": []}
for pg in range(first, last + 1):
page_path = os.path.join(outdir, f"p{pg}.json")
if os.path.exists(page_path):
doc = json.load(open(page_path))
print(f" p{pg}: file cache HIT ({len(doc.get(texts, []))} texts)")
else:
doc = dsync.convert_page(pdf, pg, OPTS, r=r)
if not doc:
merged["_failed_pages"].append(pg)
print(f" p{pg}: FAILED")
continue
json.dump(doc, open(page_path, "w"))
for k in ("texts", "tables", "pictures"):
merged[k].extend(doc.get(k, []))
merged["pages"].update(doc.get("pages", {}))
nmarg = sum(1 for t in doc.get("texts", [])
if (t.get("prov") or [{}])[0].get("bbox", {}).get("l", 999) <= 140)
print(f" p{pg}: {len(doc.get('texts', []))} texts ({nmarg} left-margin)")
json.dump(merged, open(os.path.join(outdir, "merged.json"), "w"))
print(f"-> {outdir}/ ({last-first+1-len(merged['_failed_pages'])} pages, "
f"failed={merged['_failed_pages']})")
if __name__ == "__main__":
main()