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Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network

Sagar Shinde, Rajendra Waghulade. Published in Pattern Recognition.

Communications on Applied Electronics
Year of Publication: 2016
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors: Sagar Shinde, Rajendra Waghulade
10.5120/cae2016652125

Sagar Shinde and Rajendra Waghulade. Article: Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network. Communications on Applied Electronics 4(7):1-6, March 2016. Published by Foundation of Computer Science (FCS), NY, USA. BibTeX

@article{key:article,
	author = {Sagar Shinde and Rajendra Waghulade},
	title = {Article: Handwritten Mathematical Expressions Recognition using Back Propagation Artificial Neural Network},
	journal = {Communications on Applied Electronics},
	year = {2016},
	volume = {4},
	number = {7},
	pages = {1-6},
	month = {March},
	note = {Published by Foundation of Computer Science (FCS), NY, USA}
}

Abstract

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.

References

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Keywords

Character recognition, Math symbol recognition, Handwritten math equations, Feed forward back propagation neural network.