| ---
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| language:
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| - ar
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| license: mit
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| library_name: pytorch
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| pipeline_tag: image-to-text
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| tags:
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| - ocr
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| - handwritten-text-recognition
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| - arabic
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| - khatt
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| - crnn
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| - ctc
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| ---
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|
|
| # ArabicOCR-KHATT — Arabic Handwritten Text Recognition (CRNN-CTC)
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|
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| Line-level Arabic handwritten text recognition, trained on the
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| [KHATT](https://khatt.ideas2serve.net/) dataset (11,375 handwritten line images).
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|
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| The architecture is a CRNN (CNN + BiLSTM) with CTC loss, with **Arabic-specific
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| design choices**: input height 96 so diacritic dots stay detectable, 3-zone
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| vertical pooling that preserves *where* dots sit (the only difference between
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| ba/ta/tha/nun/ya), dot-safe augmentation, and beam-search decoding with an
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| Arabic character bigram LM.
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|
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| - **Code / training pipeline:** https://github.com/FixFips/ArabicOCR_KHATT
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| - **Python package:** `pip install arabicocr-khatt`
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|
|
| ## Usage
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|
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| ```python
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| from arabicocr_khatt import ArabicOCR
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|
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| ocr = ArabicOCR.from_pretrained("FixFips/arabicocr-khatt")
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| text = ocr.recognize("handwritten_page.jpg") # segments lines automatically
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| print(text)
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| ```
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|
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| Or from the command line:
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|
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| ```bash
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| pip install arabicocr-khatt
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| arabicocr handwritten_page.jpg
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| ```
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|
|
| ## Validation metrics (KHATT, best epoch 116)
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|
|
| | Metric | Value |
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| |--------|-------|
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| | CER | 6.09% |
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| | WER | 27.26% |
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| | WER (normalized) | 26.06% |
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| | Dot-group CER | 8.24% |
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|
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| Dot-group CER measures errors only on dot-differentiated letter groups
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| (ba/ta/tha, jim/ha/kha, nun/ya) — the #1 error source in Arabic OCR.
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|
|
| ## Files
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|
|
| | File | Purpose |
|
| |------|---------|
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| | `crnn_best.pt` | Model checkpoint: `{"model": state_dict, "vocab": list[str], "arch_version": 2}` |
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| | `bigram_lm.json` | Arabic character bigram LM for beam-search decoding |
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| | `charset_arabic.txt` | 75-class character set (70 characters + 5 special tokens) |
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|
|
| ## Limitations
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|
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| - Line-level model: full pages are segmented into lines with classical
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| morphology before recognition; complex layouts may segment poorly.
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| - Trained only on KHATT handwriting; printed text, historical manuscripts, and
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| heavily diacritized text are out of domain.
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| - No word-level language model — output is not spell-corrected.
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|
|
| ## Citation
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|
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| If you use this model, please also cite the KHATT dataset:
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|
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| > Mahmoud, S. A., et al. "KHATT: An open Arabic offline handwritten text database."
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| > Pattern Recognition 47.3 (2014): 1096-1112.
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|
|
