High Cursive Complex Character Recognition using GAN External Classifier
This work addresses a domain-specific challenge in handwriting recognition, particularly for cursive and complex characters, with incremental improvements in robustness.
The paper tackles the problem of classifying highly cursive and complex handwritten characters by proposing an external classifier with a Generative Adversarial Network (ADA-GAN) to augment training data, resulting in more robust and effective performance compared to convolutional neural networks, which show decreased accuracy with increasing character complexity.
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.