Deep Learning based Optical Character Recognition

Overview

This Github Repository provides an Optical Character Recognition (OCR) solution focused on detecting lowercase English characters from images using deep learning. The input training dataset includes over 200,000 images of alphanumeric characters generated in 3475 font styles. The project includes building and training custom CNN using pre-trained models (EfficientNetB7, MobileNetV2, VGG19, DenseNet121) for character recognition.

Dataset

The dataset used in this project can be found on Kaggle: Optical Character Recognition (OCR) Dataset.

Getting Started

To get started with this project:

1. Clone this repository to your local machine.
2. Ensure you have Jupyter Notebook installed and running.
3. Install the required dependencies.
4. Download the "Optical Character Recognition (OCR) Dataset" and place it in the designated directory.
5. Open and run the Jupyter Notebook "Optical-Character-Recognition.ipynb" to train and evaluate the model.

Contributing

We welcome contributions to enhance the functionality and efficiency of this script. Feel free to fork, modify, and make pull requests to this repository. To contribute:

1. Fork the Project.
2. Create your Feature Branch (git checkout -b feature/AmazingFeature).
3. Commit your Changes (git commit -m 'Add some AmazingFeature').
4. Push to the Branch (git push origin feature/AmazingFeature).
5. Open a Pull Request against the main branch.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contact

Author: Akhil Chhibber

LinkedIn: https://www.linkedin.com/in/akhilchhibber/

Medium Blogs: https://medium.com/@akhil.chibber