| --- |
| language: ps |
| license: mit |
| tags: |
| - ocr |
| - crnn |
| - ctc |
| - pashto |
| - image-to-text |
| metrics: |
| - cer |
| - wer |
| --- |
| |
| # Pashto OCR - CRNN + CTC (printed + handwritten) |
|
|
| Line-level Pashto OCR (CRNN: VGG-style CNN + 2-layer BiLSTM + CTC, ~12M params). |
|
|
| - `crnn.pt` - stage-1 weights: printed / on-screen text |
| (trained on [zirak-ai/PashtoOCR](https://huggingface.co/datasets/zirak-ai/PashtoOCR)). |
| Printed validation CER = 0.0368 / WER = 0.1210 |
| - `crnn_pashtoOCR.pt` - stage-2 weights: fine-tuned on |
| [KPTI](https://github.com/rahmad77/KPTI) (17k real hand-scribed Pashto text lines). |
| Reads katib-style handwritten manuscripts as well as printed text. KPTI test CER = 0.0563 / WER = 0.2299. |
| - `charset.json` - character vocabulary + preprocessing config |
|
|
| ## Preprocessing contract |
|
|
| Grayscale, dark-text-on-light (auto-invert dark themes), resized to height 48 |
| (aspect preserved), normalized to [-1, 1], then **horizontally flipped** |
| (RTL script -> left-to-right CTC frames). Decode with greedy CTC |
| (collapse repeats, drop blank id 0). |
|
|
| ## Usage |
|
|
| ```python |
| import json, torch |
| from huggingface_hub import hf_hub_download |
| |
| weights = hf_hub_download("mhalimi3008/pashtoOCR", "crnn_pashtoOCR.pt") # or crnn.pt |
| cfg = json.loads(open(hf_hub_download("mhalimi3008/pashtoOCR", "charset.json")).read()) |
| model = CRNN(len(cfg["charset"]) + 1).eval() # CRNN class from the training notebook |
| model.load_state_dict(torch.load(weights, map_location="cpu")) |
| ``` |
|
|
| The training notebook (sections 5-6 and 10) contains the full inference code, including |
| projection-profile line segmentation and PDF support. |
|
|
| If you use the handwriting weights in research, cite: |
| *Ahmad et al., "KPTI: Katib's Pashto Text Imagebase and Deep Learning Benchmark", ICFHR 2016.* |
