Open Access   Article

Handwritten Hindi Character Recognition using Deep Learning Techniques

R. Vijaya Kumar Reddy1 , U. Ravi Babu2

Section:Research Paper, Product Type: Journal Paper
Volume-7 , Issue-2 , Page no. 1-7, Feb-2019

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v7i2.17

Online published on Feb 28, 2019

Copyright © R. Vijaya Kumar Reddy, U. Ravi Babu . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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Citation

IEEE Style Citation: R. Vijaya Kumar Reddy, U. Ravi Babu, “Handwritten Hindi Character Recognition using Deep Learning Techniques”, International Journal of Computer Sciences and Engineering, Vol.7, Issue.2, pp.1-7, 2019.

MLA Style Citation: R. Vijaya Kumar Reddy, U. Ravi Babu "Handwritten Hindi Character Recognition using Deep Learning Techniques." International Journal of Computer Sciences and Engineering 7.2 (2019): 1-7.

APA Style Citation: R. Vijaya Kumar Reddy, U. Ravi Babu, (2019). Handwritten Hindi Character Recognition using Deep Learning Techniques. International Journal of Computer Sciences and Engineering, 7(2), 1-7.

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Abstract

In this paper we present a handwritten Hindi character recognition system based on different Deep learning technique. Handwritten character recognition plays an important role and is currently getting the attention of researchers because of possible applications in assisting technology for blind and visually impaired users, human–robot interaction, automatic data entry for business documents, etc. In this work, we propose a technique to recognize handwritten Hindi characters using deep learning approaches like Convolutional Neural Network (CNN) With Optimizer RMSprop (Root Mean Square Propagation) , Adaptive Moment (Adam) Estimation and Deep Feed Forward Neural Networks(DFFNN). The proposed system has been trained on samples of large set of database images and tested on samples images from user defines data set and from this experiment we achieved very high recognition results. Experimental results are compared with other neural network based algorithm.

Key-Words / Index Term

DFFNN, CNN, Softmax classifier, RMSprop and Adam Estimation, Deep Learning

References

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