A demonstration of TrOCR's capabilities in Optical Character Recognition (OCR), with the results compiled into a video. This repository showcases how TrOCR can be used for accurate text extraction from both printed and handwritten samples.
To get started, clone this repository and install the required packages:
git clone https://github.com/alijawad07/TrOCR-Visualizer.git
cd TrOCR-Visualizer
pip install -r requirements.txtTo run the OCR evaluation, execute:
python main.py --data_path=<path_to_images> --num_samples=<number_of_samples>Replace <path_to_images> with the directory path containing the images you want to process, and <number_of_samples> with the number of samples you want to evaluate.
- Utilizes TrOCR for OCR tasks.
- Includes a feature to save OCR results into a dynamic-resolution video.
- Works out-of-the-box with high accuracy on printed text.
Contributions are welcome! Please read the contribution guidelines first.