Communications on Applied Electronics |
Foundation of Computer Science (FCS), NY, USA |
Volume 4 - Number 7 |
Year of Publication: 2016 |
Authors: Sagar Shinde, Rajendra Waghulade |
10.5120/cae2016652125 |
Sagar Shinde, Rajendra Waghulade . Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network. Communications on Applied Electronics. 4, 7 ( March 2016), 1-6. DOI=10.5120/cae2016652125
Handwritten mathematical expressions recognition is yet challenging task due to its intricate spatial structure, tangled semantics and 2-dimensional layout of the characters. There is a still room for enhancement in recognition rate. Artificial neural network is superior to disentangle classification problems. In this paper, feed-forward back propagation neural network is implemented to achieve both character recognition and mathematical structure recognition with upgrade in effective performance in addition to accuracy of the experimental results including lessen efforts. System proves its potency by recognizing expressions in analysis of math documents.