# High Cursive Complex Character Recognition using GAN External Classifier

arXiv: https://arxiv.org/abs/2509.03062 · HTML: https://mlsanity.com/paper/2509.03062/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 9 of its 26 references that are in the map.
- First posted on arXiv 3 Sep 2025.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications](https://mlsanity.com/paper/1704.04861/) (2017)
- [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://mlsanity.com/paper/1602.07261/) (2016)
- [Synthetic Data Augmentation using GAN for Improved Liver Lesion Classification](https://mlsanity.com/paper/1801.02385/) (2018)
- [EC-GAN: Low-Sample Classification using Semi-Supervised Algorithms and GANs](https://mlsanity.com/paper/2012.15864/) (2020)

## Abstract (arXiv)

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.