High Cursive Complex Character Recognition using GAN External Classifier
Abstract
Handwritten characters can be trickier to classify due to their complex and cursive nature compared to simple and non-cursive characters. We present an external classifier along with a Generative Adversarial Network that can classify highly cursive and complex characters. The generator network produces fake handwritten character images, which are then used to augment the training data after adding adversarially perturbed noise and achieving a confidence score above a threshold with the discriminator network. The results show that the accuracy of convolutional neural networks decreases as character complexity increases, but our proposed model, ADA-GAN, remains more robust and effective for both cursive and complex characters.
Cite
@article{arxiv.2509.03062,
title = {High Cursive Complex Character Recognition using GAN External Classifier},
author = {S M Rafiuddin},
journal= {arXiv preprint arXiv:2509.03062},
year = {2025}
}
Comments
Comments: 10 pages, 8 figures, published in the Proceedings of the 2nd International Conference on Computing Advancements (ICCA 2022). Paper introduces ADA-GAN with an external classifier for complex cursive handwritten character recognition, evaluated on MNIST and BanglaLekha datasets, showing improved robustness compared to CNN baselines