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

Line-level Arabic handwritten text recognition, trained on the KHATT dataset (11,375 handwritten line images).

The architecture is a CRNN (CNN + BiLSTM) with CTC loss, with Arabic-specific design choices: input height 96 so diacritic dots stay detectable, 3-zone vertical pooling that preserves where dots sit (the only difference between ba/ta/tha/nun/ya), dot-safe augmentation, and beam-search decoding with an Arabic character bigram LM.

• Code / training pipeline: https://github.com/FixFips/ArabicOCR_KHATT
• Python package: pip install arabicocr-khatt

Usage

from arabicocr_khatt import ArabicOCR

ocr = ArabicOCR.from_pretrained("FixFips/arabicocr-khatt")
text = ocr.recognize("handwritten_page.jpg")   # segments lines automatically
print(text)

Or from the command line:

pip install arabicocr-khatt
arabicocr handwritten_page.jpg

Validation metrics (KHATT, best epoch 116)

Dot-group CER measures errors only on dot-differentiated letter groups (ba/ta/tha, jim/ha/kha, nun/ya) — the #1 error source in Arabic OCR.

Files

Limitations

• Line-level model: full pages are segmented into lines with classical morphology before recognition; complex layouts may segment poorly.
• Trained only on KHATT handwriting; printed text, historical manuscripts, and heavily diacritized text are out of domain.
• No word-level language model — output is not spell-corrected.

Citation

If you use this model, 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.