arabicocr-khatt 0.1.0

Arabic handwritten text recognition — CRNN-CTC with Arabic-specific multi-scale vertical encoding, trained on the KHATT dataset

ArabicOCR-KHATT — Arabic Handwritten Text Recognition (CRNN-CTC)

Arabic handwritten text recognition — line-level OCR (a line or paragraph image → Arabic text), trained on the KHATT dataset.

What makes it Arabic-specific rather than a generic CRNN:

• 3-zone vertical pooling — preserves where dots sit (above vs below the baseline), the only difference between ب/ت/ث and ن/ي
• Input height 96 — keeps diacritic dots large enough (3–5 px) for 3×3 convolutions to detect
• Dot-safe augmentation — shear, kashida stretch, mild rotation; erosion / dilation / elastic are banned because they destroy 2–4 px dots
• Beam-search decoding with an Arabic character bigram LM
• Dot-group CER metric — tracks errors on dot-differentiated letter groups, the #1 Arabic OCR error source

---

Install

pip install arabicocr-khatt

Quickstart

from arabicocr_khatt import ArabicOCR

ocr = ArabicOCR.from_pretrained()        # downloads weights from the HF Hub
text = ocr.recognize("handwritten_page.jpg")   # multi-line pages segmented automatically
print(text)

Or from the command line:

arabicocr handwritten_page.jpg
arabicocr line.png --no-segment --greedy       # single line, fastest decode
arabicocr scan.jpg --checkpoint runs/exp1/crnn_best.pt   # local weights

Try it in the browser: demo Space on Hugging Face.

---

Architecture

Input [B, 1, 96, 1536] grayscale
  → CNN (7 conv layers, full BatchNorm, Dropout2d)
  → adaptive pool to 3 vertical zones (dot position preserved)
  → 2-layer BiLSTM(1536 → 384)
  → FC → CTC loss
  → greedy or beam-search decode (+ Arabic bigram LM), reversed back to RTL

Preprocessing (shared between training and inference): grayscale → CLAHE → dual-polarity Otsu binarization → LANCZOS resize to H=96 → pad to W=1536.

Project structure

ArabicOCR_KHATT/
├─ arabicocr_khatt/            # the installable package
│  ├─ pipeline.py              # ArabicOCR API + CLI (inference single-source)
│  ├─ model.py                 # CRNN + CTC decoders + bigram LM builder
│  ├─ preprocess.py            # CLAHE, binarization, resize, padding
│  ├─ augment.py               # Arabic-safe augmentation
│  ├─ dataset.py               # KHATTDataset
│  ├─ metrics.py               # CER / WER / dot-group CER
│  ├─ train_crnn_ctc.py        # training script
│  ├─ webocr.py                # Gradio test bench (rich debugging UI)
│  ├─ show_metrics.py, monitor.py, compare_runs.py, eval_val.py, ...
│  └─ charset_arabic.txt       # 75-class charset
├─ scripts/upload_to_hf.py     # publish weights + model card to the HF Hub
├─ space/                      # Hugging Face Space demo app
├─ archive/                    # (gitignored) KHATT images + labels + splits
├─ runs/                       # (gitignored) checkpoints + metrics.csv
└─ pyproject.toml

---

Training your own model

Get the KHATT dataset (request access at khatt.ideas2serve.net) and lay it out as:

archive/
├─ images/   *.jpg            # line images
├─ labels/   *.txt            # Arabic text (Windows-1256 / UTF-8)
└─ splits/                    # auto-created train/val/test CSVs (80/10/10)

Then, with the training extras installed:

pip install -e ".[train]"
python -m arabicocr_khatt.train_crnn_ctc                 # trains to runs/exp1
python -m arabicocr_khatt.show_metrics --run ./runs/exp1 # inspect metrics
python -m arabicocr_khatt.monitor                        # live training dashboard

Training uses OneCycleLR, gradient accumulation, early stopping, and logs per-epoch CER / WER / WER(norm) / dot-group CER to runs/<exp>/metrics.csv. Checkpoints are saved as {"model": state_dict, "vocab": [...], "arch_version": 2}.

Web test bench (Gradio)

A rich debugging UI with crop/rotate, polarity control, decoder comparison, confidence heatmaps, char-level diff against ground truth, and batch evaluation:

pip install -e ".[demo]"
python -m arabicocr_khatt.webocr

Publishing weights to the Hub

After training (on the machine that has runs/ and archive/):

hf auth login
python scripts/upload_to_hf.py --run-dir runs/exp1

This validates the checkpoint, builds the bigram LM from the training split, fills the model card with your best-epoch metrics, and uploads everything to FixFips/arabicocr-khatt.

---

Notes and limitations

• Trained on KHATT: performance is best on handwriting similar to that dataset. Printed fonts, very noisy backgrounds, or strongly curved text may fail.
• Line-level model: pages are segmented into lines with classical morphology; complex layouts may segment poorly.
• Labels use Windows-1256 encoding (KHATT standard); the reader falls back to UTF-8 automatically.

Contributing

Contributions are very welcome — Arabic OCR is an underserved area and there is plenty to do. See CONTRIBUTING.md and the good first issues.

License

MIT — see LICENSE.

If you use this project in research, please also cite the KHATT dataset:

Mahmoud, S. A., et al. "KHATT: An open Arabic offline handwritten text database." Pattern Recognition 47.3 (2014): 1096–1112.