---
license: apache-2.0
language:
  - fa
pipeline_tag: image-to-text
widget:
  - src: https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/papers/attention.png
    example_title: "Persian OCR"
---

# Persian-OCR

**Persian-OCR** is a deep learning model for **Optical Character Recognition (OCR)**, designed specifically for Persian text.  
The model employs a **CNN + Transformer architecture** trained with **CTC loss** to extract text from images.

The model was trained on a custom dataset of approximately **600,000 synthetic Persian text images**.  
These images were generated from **Wikipedia text** using **49 different Persian fonts**, with sequence lengths ranging from **0 to 150 characters**.  

On this dataset, the model achieves a **sequence accuracy of 96%**.  

The model may benefit from **further fine-tuning on real-world data**, and contributions or collaborations are **warmly welcomed**.

## 🤝 Contributing
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.




## 📬 Contact
For collaboration or inquiries, please reach out via farbodpya@gmail.com




## Files

- `pytorch_model.bin` : PyTorch model weights  
- `vocab.json` : Character vocabulary  
- `model.py` : Python script defining the CNN + Transformer OCR model  
- `utils.py` : Utility functions for OCR, including `ocr_page` and `load_vocab`  
- `config.json` : Model configuration  

## Installation
```
pip install torch torchvision huggingface_hub
```


## Usage
```

import torch
import json
import sys
import importlib.util
from huggingface_hub import hf_hub_download

# 1️⃣ Load vocab
vocab_path = hf_hub_download("farbodpya/Persian-OCR", "vocab.json")
with open(vocab_path, "r", encoding="utf-8") as f:
    vocab = json.load(f)
idx_to_char = {int(k): v for k, v in vocab["idx_to_char"].items()}

# 2️⃣ Import model.py 
model_file = hf_hub_download("farbodpya/Persian-OCR", "model.py")
spec_model = importlib.util.spec_from_file_location("model", model_file)
model_module = importlib.util.module_from_spec(spec_model)
sys.modules["model"] = model_module
spec_model.loader.exec_module(model_module)
from model import CNN_Transformer_OCR

# 3️⃣ Import utils.py 
utils_file = hf_hub_download("farbodpya/Persian-OCR", "utils.py")
spec_utils = importlib.util.spec_from_file_location("utils", utils_file)
utils_module = importlib.util.module_from_spec(spec_utils)
sys.modules["utils"] = utils_module
spec_utils.loader.exec_module(utils_module)
from utils import ocr_page

# 4️⃣ Load model weights
weights_path = hf_hub_download("farbodpya/Persian-OCR", "pytorch_model.bin")
model = CNN_Transformer_OCR(num_classes=len(idx_to_char)+1)
model.load_state_dict(torch.load(weights_path, map_location="cpu"))
model.eval()

# 5️⃣ Run OCR on an image
img_path = "sample.png"  # replace with your own image
text = ocr_page(img_path, model, idx_to_char)
print("\n=== Final OCR Page ===\n", text)