Open Access   Article Go Back

CNN Based Handwritten Devanagari Digits Recognition

Gaurav Kumar1 , Sachin Kumar2

  1. Dept. of Computer Science and Engineering, NIT, Warangal, India.
  2. Dept. of Computer Science and Engineering, MIT AOE, Pune, India.

Correspondence should be addressed to: gaurav.sachin.007@gmail.com.

Section:Review Paper, Product Type: Journal Paper
Volume-5 , Issue-7 , Page no. 71-74, Jul-2017

CrossRef-DOI:   https://doi.org/10.26438/ijcse/v5i7.7174

Online published on Jul 30, 2017

Copyright © Gaurav Kumar, Sachin Kumar . 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.

View this paper at   Google Scholar | DPI Digital Library

How to Cite this Paper

  • IEEE Citation
  • MLA Citation
  • APA Citation
  • BibTex Citation
  • RIS Citation

IEEE Style Citation: Gaurav Kumar, Sachin Kumar, “CNN Based Handwritten Devanagari Digits Recognition,” International Journal of Computer Sciences and Engineering, Vol.5, Issue.7, pp.71-74, 2017.

MLA Style Citation: Gaurav Kumar, Sachin Kumar "CNN Based Handwritten Devanagari Digits Recognition." International Journal of Computer Sciences and Engineering 5.7 (2017): 71-74.

APA Style Citation: Gaurav Kumar, Sachin Kumar, (2017). CNN Based Handwritten Devanagari Digits Recognition. International Journal of Computer Sciences and Engineering, 5(7), 71-74.

BibTex Style Citation:
@article{Kumar_2017,
author = {Gaurav Kumar, Sachin Kumar},
title = {CNN Based Handwritten Devanagari Digits Recognition},
journal = {International Journal of Computer Sciences and Engineering},
issue_date = {7 2017},
volume = {5},
Issue = {7},
month = {7},
year = {2017},
issn = {2347-2693},
pages = {71-74},
url = {https://www.ijcseonline.org/full_paper_view.php?paper_id=1366},
doi = {https://doi.org/10.26438/ijcse/v5i7.7174}
publisher = {IJCSE, Indore, INDIA},
}

RIS Style Citation:
TY - JOUR
DO = {https://doi.org/10.26438/ijcse/v5i7.7174}
UR - https://www.ijcseonline.org/full_paper_view.php?paper_id=1366
TI - CNN Based Handwritten Devanagari Digits Recognition
T2 - International Journal of Computer Sciences and Engineering
AU - Gaurav Kumar, Sachin Kumar
PY - 2017
DA - 2017/07/30
PB - IJCSE, Indore, INDIA
SP - 71-74
IS - 7
VL - 5
SN - 2347-2693
ER -

VIEWS PDF XML
742 427 downloads 486 downloads
  
  
           

Abstract

Handwritten Digit Recognition has huge demand in commercial, administrative and academic domains. In recent years lot of good work has been done to improve accuracy of Handwritten Digit Recognition System but accuracy of such systems depend on large datasets. Deep Convolutional Neural Network have shown superior results to traditional shallow net-works in many recognition task. In this paper, a convolutional neural network (CNN) based Devanagari digit recognition system is highlighted. The dataset contains 21969 hand written 28x28 size images. The result of proposed system showed 99.07% accuracy on our dataset.

Key-Words / Index Term

Devanagri Digits, CNN, SVM, KNN, CUDA, GPU,Tensorflow

References

[1] N. Matic, I. Guyon, J. Denker and V. Vapnik, "Writer-adaptation for on-line handwritten character recognition," Document Analysis and Recognition, 1993., Proceedings of the Second International Conference on, Tsukuba Science City, 1993, pp. 187-191.
[2] C. Wu, W. Fan, Y. He, J. Sun and S. Naoi, "Handwritten Character Recognition by Alternately Trained Relaxation Convolutional Neural Network," 2014 14th International Conference on Frontiers in Handwriting Recognition, Heraklion, 2014, pp. 291-296.
[3] Sreedharamurthy S K and H.R.Sudarshana Reddy, "Feature Subset Selection Using Genetic Algorithms for Handwritten Kannada Alphabets Recognition", International Journal of Computer Sciences and Engineering, Vol.3, Issue.6, pp.94-99, 201
[4] Loc Thanh Huynh, Hung Thang Phung andToai QuangTon, "Using Convolutional Neural Network to Recognize Handwritten Digits", International Journal of Computer Sciences and Engineering, Vol.3, Issue.5, pp.203-206, 2015.
[5] S.U. Bohra, P.V. Ingole , "Review on Neural Network Based Approach Towards English Handwritten Alphanumeric Characters Recognition", International Journal of Computer Sciences and Engineering, Vol.1, Issue.3, pp.22-25, 2013.
[6] Mohd. Aquib Ansari, Diksha Kurchaniya, Manish Dixit, Punit Kumar Johari, "An Effective Approach to an Image Retrieval using SVM Classifier", International Journal of Computer Sciences and Engineering, Vol.5, Issue.6, pp.62-72, 2017.
[7] S. Lee, S. J. Son, J. Oh and N. Kwak, "Handwritten Music Symbol Classification Using Deep Convolutional Neural Networks," 2016 International Conference on Information Science and Security (ICISS), Pattaya, 2016, pp. 1-5.
[8] A. Mowlaei and K. Faez, "Recognition of isolated handwritten Persian/Arabic characters and numerals using support vector machines," 2003 IEEE XIII Workshop on Neural Networks for Signal Processing (IEEE Cat. No.03TH8718), 2003, pp. 547-554.
[9] A. M. S. Chowdhury and M. S. Rahman, "Towards optimal convolutional neural network parameters for bengali handwritten numerals recognition," 2016 19th International Conference on Computer and Information Technology (ICCIT), Dhaka, 2016, pp. 431-436.