StrikeNet — strike/clean word classifier

StrikeNet is the 79k-parameter CNN that pdf-strikethrough-detect uses to resolve pixel-ambiguous strikethrough cases on scanned document pages. A thin strike over an ascender-less word is pixel-identical to an ordinary glyph chain; geometry alone cannot separate them, so a learned model casts the deciding vote. On born-digital PDFs the package reads vector strokes and annotations directly and never invokes this model.

• Input: one standardized grey word crop, 32×160 (ink-positive, height-normalized). Produced by pdf_strikethrough.cnn.std_crop; the exact geometry is recorded in strike_verdict_cnn.meta.json and enforced at load time (cnn._check_geometry) so training and inference preprocessing cannot drift.
• Output: a single logit → sigmoid probability that the word is struck.
• Decision thresholds (in the meta): p_hi = 0.85, p_lo = 0.15. A word scoring ≥ p_hi is struck, ≤ p_lo is clean, and the [p_lo, p_hi) band is "unsure" and deferred to geometry. The training script sets these from data: p_hi as a split-conformal threshold on held-out struck-word probabilities (distribution-free recall floor of 1 − alpha), p_lo mirrored on the clean class. Override per-call with ScanConfig.recall_first(cnn_p_hi=…) / precision_first(cnn_p_lo=…).

How it fits the pipeline

Scanned page → OCR words + morphological stroke geometry (lines.py) → StrikeNet adjudicates the auto/review tier crops → struck words, char-level partial resolution, struck-aware markdown. Full architecture: the project README.

Evaluation

Measured on the project's reproducible 10-document public regulatory-redline corpus (US Copyright Office, FDIC, CEQ ×2, EPA ×3, California CCPA/CPPA, Gretna LA development code; 54.7k struck words, each sha256-pinned). Reproduce with benchmarks/scanned_recovery.py.

Metric	Value	Source
Scanned-path strike recovery (RapidOCR)	97%	scanned_recovery.py, 3 docs / 24 pages / 2,170 known strikes
Scanned-path strike recovery (Azure DI)	95%	same harness, Azure Document Intelligence words
Native vector detections independently confirmed by the flag signal	99.8%	confirmation_rate.py (context; native path, not this model)

A per-document precision/recall figure from a labeled-corpus retrain (R-cal) is planned; the hosted weights here are the reproducible v0.9.0 shipped model.

Usage

The package runs on ONNX Runtime with no torch dependency. Download is digest-verified before the graph is ever loaded — a tampered host cannot swap the model.

import pdf_strikethrough as st

st.ensure_model(
    "https://huggingface.co/niles-liu/strikenet/resolve/main/strike_verdict_cnn.onnx",
    "fac2c51baaa75ee782196bdfe7452638cb48c7deddb21163b1ac6a0a72ae4457",
    meta_url="https://huggingface.co/niles-liu/strikenet/resolve/main/strike_verdict_cnn.meta.json",
)
assert "p_hi" in st.get_model_meta()          # thresholds + crop geometry now loaded
result = st.detect_pdf("scanned-redline.pdf", ocr=st.rapidocr_backend())

Training & reproducibility

Trained from a labeled crop set exported by the detector itself, so the shipped weights are reproducible and the failing-page → better-model loop is one command per step:

pdf-strikethrough detect scan.pdf --ocr rapidocr --dump-crops crops_out/   # export scored crops
# label crops_out/crops.jsonl: set each row's "label" to "struck" or "clean"
python training/train_strikenet.py crops_out/ -o model_out/ --epochs 40    # train + calibrate + ONNX

Full loop and label format: training/README.md.

Limitations

• Handwritten / freehand strikes are the main known gap: trained on rendered digital strikes, so a wavy pen scribble or heavy cross-out may be missed.
• Horizontal, left-to-right text only. Vertical writing modes (CJK/Mongolian) and RTL strike axes are out of scope.
• The model only sees word crops the geometry stage flags as candidates; it does not itself find words on the page.

License

MIT, matching the parent package.