Journal of Computer Engineering & Information TechnologyISSN : 2324-9307

Research Article, J Regen Med Vol: 7 Issue: 1

Back Propagation Neural Network (BPNN) Based Recognition Of Handwritten Mathematical Equations

Sagar Shinde1* and Rajendra Waghulade2

1JSPM, Narhe Technical Campus, Savitribai Phule Pune University, India

2DNCVP, North Maharashtra University, India

*Corresponding Author : Sagar Shinde
Assistant Professor, Udaipur and JSPM, Narhe Technical Campus, Savitribai Phule Pune University, Maharashtra, India
E-mail: [email protected]

Received: May 29, 2018 Accepted: June 08, 2018 Published: June 15, 2018

Citation: Shinde S, Waghulade R (2018) Back Propagation Neural Network (BPNN) Based Recognition of Handwritten Mathematical Equations. J Comput Eng Inf Technol 7:1. doi: 10.4172/2324-9307.1000192

Abstract

The recognition of handwritten mathematical symbols and equations are the critical and challenging issue in the field of pattern recognition. It is the need to recognize complicated handwritten mathematical equations viz. law of gravity, convolution integral etc. The issues like overwriting of symbols, characters etc. are identified and solved it by selecting best classifier to improve recognition rate. The machine learning approach with enhanced multi layer precentor feed forward back propagation neural network algorithm with an offline mode of recognition has been used to improve throughput, accuracy and overall efficiency of mathematical equations recognition. The hybrid features extracted viz. centroid, boundary box, zoning density, line segment etc. and gradient descent with momentum training algorithm has been used. Adaptive learning is used to carry out the experiment on numerous kinds of equations. Through experimental result, the system is evaluated and illustrated which shows the significant improvement 93.5 % accuracy in recognition of simple as well as complicated mathematical equations. In future current methodology will be the key factor to initiate paperless work and digital world.

Keywords: Math symbols and equations; Hybrid feature; Back propagation neural network; Adaptive learning; 2-D layout; Throughput; Accuracy

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