legere 0.3.0

Compact CRNN OCR for printed text (Portuguese charset) — pure PyTorch, CPU-friendly, ~360k parameters

Legere

Compact CRNN OCR for printed text (Portuguese charset with accents, digits, punctuation), built from scratch with PyTorch and trained entirely on synthetic pages generated with PIL. No pretrained models, no OCR libraries.

• ~360k parameters (hard limit enforced: 1M) — bundled weights are 1.4 MB
• CRNN: MobileNet-style depthwise separable CNN + 1 BiGRU(128) + linear head, CTC loss
• Classic (non-neural) deskew + projection-based line segmentation
• CPU-friendly: full-HD page in well under a second; INT8/TorchScript exports
• Trained weights ship inside the package — install and read pages immediately

Install

pip install legere              # inference only
pip install legere[pdf]        # + PDF input support (pypdfium2)
pip install legere[train]       # + training extras (rich, psutil)

From a checkout:

pip install -e .[train]

Library usage

from legere import Legere

ocr = Legere()                      # bundled weights, CPU
result = ocr.read("page.png")       # path, or a numpy array (gray or BGR)

print(result.text)                  # full text in reading order
print(result.skew_angle)            # estimated page skew (degrees)
for line in result.lines:
    print(line.text, line.bbox)     # per-line text + (x0, y0, x1, y1)

Options: Legere(model="checkpoints/best.pt") for your own checkpoint, Legere(model="exports/model_int8.ts.pt", torchscript=True) for the INT8 export, beam_width=8 for CTC beam search, threads=N to cap CPU threads.

PDFs (with legere[pdf] installed):

results = ocr.read_pdf("document.pdf", dpi=200)   # one PageResult per page

from legere.pdf import pdf_to_pngs
pdf_to_pngs("document.pdf", out_dir="pages/")     # just convert to PNGs

CLI

legere read page.png                    # text on stdout
legere page.png                         # same (shortcut)
legere read page.png --json out.json    # + per-line text and bboxes
legere read page.png --beam 8           # CTC beam search
legere read page.png --model exports/model_int8.ts.pt --torchscript

legere read document.pdf                # OCR every PDF page (legere[pdf])
legere pdf document.pdf --out-dir pages # convert PDF pages to PNGs (no OCR)

legere train                            # train on synthetic pages
legere export                           # TorchScript fp32 + INT8 exports
legere benchmark                        # CPU latency + page CER report

Pipeline of read: fit to 1920x1080 → adaptive binarization + deskew → line segmentation by horizontal projection (rules removed via morphology) → height-32 line batch through the CRNN → CTC greedy decode → text in reading order.

Project layout

src/legere/
├── charset.py        # character set + encode/decode helpers
├── model.py          # CompactCRNN, parameter budget, CTC greedy/beam decode
├── segment.py        # classic deskew + projection line segmentation
├── lines.py          # line-crop normalization and batching
├── inference.py      # Legere engine + full-page pipeline
├── pdf.py            # PDF page rendering / PNG conversion (legere[pdf])
├── metrics.py        # edit distance, CER, WER
├── cli.py            # `legere` command
├── data/model.pt     # bundled trained weights (state dict + charset)
└── training/         # synthetic pagegen, dataset stream, train/export/benchmark

Training

Training data is generated on the fly: synthetic pages with headers, paragraphs, key/value lines, tables, Portuguese-like words, dates, monetary values and CPF/CNPJ, degraded with noise/blur/JPEG artifacts. Fonts are read from C:\Windows\Fonts (training currently expects Windows).

legere train                          # live Rich dashboard
legere train --no-dashboard           # plain log lines
legere train --resume checkpoints/last.pt

Two synthetic page styles are available via --style (default mixed): document (free-form headers, paragraphs, key/value lines, tables) and report (bordered technical report forms: gray section bars, label/value grid cells, measurements, serials, gray footer). mixed alternates both.

Training content is deliberately dense in easily-confused punctuation (. , ; : in times, decimals and lists); validation reports a dedicated punctuation CER (val punct CER on the dashboard, punct_cer in runs/metrics.jsonl) alongside overall CER/WER.

Checkpoints go to checkpoints/ (best.pt by validation CER, last.pt every eval); metrics append to runs/metrics.jsonl. Training stops early once CER < 1% holds with no further improvement, or on a clear plateau. Useful flags: --threads N, --width-budget N (batch size via padded-width budget; lower it to reduce RAM).

After training, refresh the packaged weights:

legere export --checkpoint checkpoints/best.pt --bundle src/legere/data/model.pt

Export & benchmark

legere export        # writes exports/model_fp32.ts.pt and exports/model_int8.ts.pt
legere benchmark     # line/page CPU latency + full-page CER per variant

Results

Bundled weights: validation CER 0.044% (line level, 2000-line fixed set) at step 26k, trained on the document style only — retraining with --style mixed is recommended for report-style pages. legere benchmark reproduces line/page latency and full-page CER on synthetic pages.

License

MIT