International Journal of
Computer Sciences and Engineering

Scholarly Peer-Reviewed, and Fully Refereed Scientific Research Journal
CNN Based Handwritten Devanagari Digits Recognition
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
  XML View PDF Download  
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.
Downloads (118)     Full view (112)
           
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.