/HandWrittenDigitRecognition

Primary LanguagePython

Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning

This is the implementation of the paper "Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning" by Amirreza Fateh, Reza Tahmasbi Birgani, Mansoor Fateh, and Vahid Abolghasemi.


For more information, check out our paper on [IEEEXplore](https://ieeexplore.ieee.org/document/10474004)..

Requirements

  • Python 3.12.2
  • Keras 3.1.1
  • Tensorflow 2.16.1
  • scipy 1.12.0
  • scikit-learn 1.4.1
  • numpy 1.26.4
  • pandas 2.2.1
  • opencv-python 4.9.0.80
  • seaborn 0.13.2
  • matplotlib 3.8.3

.bib citation

To cite the paper (early access version), use the following format:

@article{fateh2024advancing,
title={Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning},
author={Fateh, Amirreza and Tahmasbi Birgani, Reza and Fateh, Mansoor and Abolghasemi, Vahid},
journal={IEEE Access},
year={2024},
publisher={Institute of Electrical and Electronics Engineers}
}

SUT DIGITal Dataset License

License

Overview

The DIGITal dataset is provided for research and educational purposes. By using the dataset, you agree to comply with these terms.

Terms of Use

  1. The DIGITal dataset is for non-commercial use only.
  2. You may cite the following paper when using the dataset:
    • "Advancing Multilingual Handwritten Numeral Recognition with Attention-driven Transfer Learning" IEEEXplore, 2024. Link to Paper.

Disclaimer

  1. The DIGITal dataset is provided "as is" without any warranty.
  2. The authors and organization shall not be liable for any damages arising from the use of the dataset.

Contact Information

For inquiries, please contact: