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--- |
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license: apache-2.0 |
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language: |
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- fa |
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pipeline_tag: image-to-text |
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widget: |
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- src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/papers/attention.png |
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example_title: "Persian OCR" |
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--- |
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# Persian-OCR |
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**Persian-OCR** is a deep learning model for **Optical Character Recognition (OCR)**, designed specifically for Persian text. |
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The model employs a **CNN + Transformer architecture** trained with **CTC loss** to extract text from images. |
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The model was trained on a custom dataset of approximately **600,000 synthetic Persian text images**. |
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These images were generated from **Wikipedia text** using **49 different Persian fonts**, with sequence lengths ranging from **0 to 150 characters**. |
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On this dataset, the model achieves a **sequence accuracy of 96%**. |
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The model may benefit from **further fine-tuning on real-world data**, and contributions or collaborations are **warmly welcomed**. |
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## 🤝 Contributing |
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Contributions are welcome! If you have a dataset of real-world Persian text or improvements to the model, please open an issue or submit a pull request. |
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## 📬 Contact |
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For collaboration or inquiries, please reach out via farbodpya@gmail.com |
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## Files |
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- `pytorch_model.bin` : PyTorch model weights |
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- `vocab.json` : Character vocabulary |
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- `model.py` : Python script defining the CNN + Transformer OCR model |
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- `utils.py` : Utility functions for OCR, including `ocr_page` and `load_vocab` |
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- `config.json` : Model configuration |
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## Installation |
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``` |
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pip install torch torchvision huggingface_hub |
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``` |
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## Usage |
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``` |
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import torch |
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import json |
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import sys |
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import importlib.util |
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from huggingface_hub import hf_hub_download |
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# 1️⃣ Load vocab |
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vocab_path = hf_hub_download("farbodpya/Persian-OCR", "vocab.json") |
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with open(vocab_path, "r", encoding="utf-8") as f: |
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vocab = json.load(f) |
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idx_to_char = {int(k): v for k, v in vocab["idx_to_char"].items()} |
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# 2️⃣ Import model.py |
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model_file = hf_hub_download("farbodpya/Persian-OCR", "model.py") |
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spec_model = importlib.util.spec_from_file_location("model", model_file) |
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model_module = importlib.util.module_from_spec(spec_model) |
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sys.modules["model"] = model_module |
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spec_model.loader.exec_module(model_module) |
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from model import CNN_Transformer_OCR |
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# 3️⃣ Import utils.py |
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utils_file = hf_hub_download("farbodpya/Persian-OCR", "utils.py") |
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spec_utils = importlib.util.spec_from_file_location("utils", utils_file) |
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utils_module = importlib.util.module_from_spec(spec_utils) |
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sys.modules["utils"] = utils_module |
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spec_utils.loader.exec_module(utils_module) |
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from utils import ocr_page |
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# 4️⃣ Load model weights |
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weights_path = hf_hub_download("farbodpya/Persian-OCR", "pytorch_model.bin") |
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model = CNN_Transformer_OCR(num_classes=len(idx_to_char)+1) |
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model.load_state_dict(torch.load(weights_path, map_location="cpu")) |
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model.eval() |
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# 5️⃣ Run OCR on an image |
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img_path = "sample.png" # replace with your own image |
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text = ocr_page(img_path, model, idx_to_char) |
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print("\n=== Final OCR Page ===\n", text) |
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