Recognition of Online Handwritten Gurmukhi Strokes using Convolutional Neural Networks

Rishabh Budhouliya, Rajendra Sharma, Harjeet Singh

2020

Abstract

In this paper, we attempt to explore and experiment multiple variations of Convolutional Neural Networks on the basis of their distributions of trainable parameters between convolution and fully connected layers, so as to achieve a state-of-the-art recognition accuracy on a primary dataset which contains isolated stroke samples of Gurmukhi script characters produced by 190 native writers. Furthermore, we investigate the benefit of data augmentation with synthetically generated samples using an approach called stroke warping on the aforementioned dataset with three variants of a Convolutional Neural Network classifier. It has been found that this approach improves classification performance and reduces over-fitting. We extend this finding by suggesting that stroke warping helps in estimating the inherent variances induced in the original data distribution due to different writing styles and thus, increases the generalisation capacity of the classifier.

Paper Citation

in Harvard Style

Budhouliya R., Sharma R. and Singh H. (2020). Recognition of Online Handwritten Gurmukhi Strokes using Convolutional Neural Networks. In Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-395-7, pages 578-586. DOI: 10.5220/0008960005780586

in Bibtex Style

@conference{icaart20,
author={Rishabh Budhouliya and Rajendra Sharma and Harjeet Singh},
title={Recognition of Online Handwritten Gurmukhi Strokes using Convolutional Neural Networks},
booktitle={Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2020},
pages={578-586},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0008960005780586},
isbn={978-989-758-395-7},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 12th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Recognition of Online Handwritten Gurmukhi Strokes using Convolutional Neural Networks
SN - 978-989-758-395-7
AU - Budhouliya R.
AU - Sharma R.
AU - Singh H.
PY - 2020
SP - 578
EP - 586
DO - 10.5220/0008960005780586