End-to-End Urdu OCR: A Demo Web App For UTRNet This tool was developed by integrating the UTRNet (https://abdur75648.github.io/UTRNe) with a text detection model (YoloV8 finetuned on UrduDoc) for end-to-end Urdu OCR.
The application is deployed on Hugging Face Spaces and is available for a live demo. You can access it here. If you prefer to run it locally, you can clone this repository and follow the instructions provided below.
Note: This version of the application uses a YoloV8 model for text detection. The original version of UTRNet uses ContourNet for this purpose. However, due to deployment issues, we have opted for YoloV8 in this demo. While YoloV8 is as accurate as ContourNet, it offers the advantages of faster processing and greater efficiency.
Clone this repository and install the dependencies using the following command (python 3.10):
Facing issues in downloading model checkpoints properly? See this issue
pip install -r requirements.txtTo install the application, first clone this repository. Then, install the necessary dependencies using the following command:
pip install -r requirements.txt- To run the application, execute the following command:
python app.pyThis command launches a Gradio app, which you can interact with to experience the capabilities of UTRNet.
If you use the code/dataset, please cite the following paper:
@InProceedings{10.1007/978-3-031-41734-4_19,
author="Rahman, Abdur
and Ghosh, Arjun
and Arora, Chetan",
editor="Fink, Gernot A.
and Jain, Rajiv
and Kise, Koichi
and Zanibbi, Richard",
title="UTRNet: High-Resolution Urdu Text Recognition in Printed Documents",
booktitle="Document Analysis and Recognition - ICDAR 2023",
year="2023",
publisher="Springer Nature Switzerland",
address="Cham",
pages="305--324",
isbn="978-3-031-41734-4",
doi="https://doi.org/10.1007/978-3-031-41734-4_19"
}