CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Handwritten Bangla Character Recognition using Inception Convolutional Neural Network

by Md. Adnan Taufique, Farhana Rahman, Md. Imrul Kayes Pranta, Nasib AL Zahid, Syeda Shabnam Hasan
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 181 - Number 17
Year of Publication: 2018
Authors: Md. Adnan Taufique, Farhana Rahman, Md. Imrul Kayes Pranta, Nasib AL Zahid, Syeda Shabnam Hasan
10.5120/ijca2018917850

Md. Adnan Taufique, Farhana Rahman, Md. Imrul Kayes Pranta, Nasib AL Zahid, Syeda Shabnam Hasan . Handwritten Bangla Character Recognition using Inception Convolutional Neural Network. International Journal of Computer Applications. 181, 17 ( Sep 2018), 48-59. DOI=10.5120/ijca2018917850

@article{ 10.5120/ijca2018917850,
author = { Md. Adnan Taufique, Farhana Rahman, Md. Imrul Kayes Pranta, Nasib AL Zahid, Syeda Shabnam Hasan },
title = { Handwritten Bangla Character Recognition using Inception Convolutional Neural Network },
journal = { International Journal of Computer Applications },
issue_date = { Sep 2018 },
volume = { 181 },
number = { 17 },
month = { Sep },
year = { 2018 },
issn = { 0975-8887 },
pages = { 48-59 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume181/number17/29918-2018917850/ },
doi = { 10.5120/ijca2018917850 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T01:06:16.531393+05:30
%A Md. Adnan Taufique
%A Farhana Rahman
%A Md. Imrul Kayes Pranta
%A Nasib AL Zahid
%A Syeda Shabnam Hasan
%T Handwritten Bangla Character Recognition using Inception Convolutional Neural Network
%J International Journal of Computer Applications
%@ 0975-8887
%V 181
%N 17
%P 48-59
%D 2018
%I Foundation of Computer Science (FCS), NY, USA
Abstract

With the advancement of modern technology the necessity of pattern recognition has increased a lot. Character recognition it's part of pattern recognition. In last few decades there has been some researches on optical character recognition(OCR) for so many languages like - Roman, Japanese, African, Chinese, English and some researches of Indian language like -Tamil, Devanagari, Telugu, Gujratietc and so many other languages. There are very few works on handwritten Bangla character recognition. As it is tough to understand like Bangla language because of different people handwritten varies in fervidity or formation, stripe and angle. For this it's so much challenging to work in this field. In some researches SVM, MLP, ANN, HMM, HLP & CNN has been used for handwritten Bangla character recognition. In this paper an attempt is made to recognize handwritten Bangla character using Convolutional Neural Network along with the method of inception module without feature extraction. The feature extraction occurs during the training phase rather than the dataset preprocessing phase. As CNN can't take input data that varying in shape ,so had to rescaled the dataset images at fixed different size. In total final dataset contains 100000 images of dimension 28x28. 85000 images is used for training and 3000 images is used for testing. After analyzing the results a conclusion is derived on the proposed work and stated the future goals and plans to achieve highest success and accuracy rate.

References
  1. Williams, Kyle, Hussein Suleman, and Jorgina K. do R Paihama. "A comparison of machine learning techniques for handwritten." Proceedings of the South African Institute for Computer Scientists and Information Technologists Conference.ACM, 2013.
  2. Vijayaraghavan, Prashanth, and Misha Sra. "Handwritten Tamil Recognition using a Convolutional Neural Network",2015.
  3. Shanthi, N. and Duraiswamy, K., 2010. A novel SVM-based handwritten Tamil character recognition system. Pattern Analysis and Applications, 13(2), pp.173180.
  4. Kumar, Parveen, Nitin Sharma, and ArunRana. "Handwritten Character Recognition using Different Kernel based SVM Classifier and MLP Neural Network (A COMPARISON)." International Journal of Computer Applications53.11 (2012).
  5. Das, Nibaran, et al. "Handwritten Bangla character recognition using a soft computing paradigm embedded in two pass approach." Pattern Recognition48.6 (2015): 2054-2071.
  6. Rahman, Md Mahbubar, et al. "Bangla handwritten character recognition using convolutional neural network." International Journal of Image, Graphics and Signal Processing (IJIGSP) 7.8 (2015): 42.
  7. Bhowmik, Tapan Kumar, et al. "SVM-based hierarchical architectures for handwritten Bangla character recognition." International Journal on Document Analysis and Recognition (IJDAR) 12.2 (2009): 97-108.
  8. Rahman, Ahmad Fuad Rezaur, R. Rahman, and Michael C. Fairhurst. "Recognition of handwritten Bengali characters: a novel multistage approach." Pattern Recognition 35, no. 5 (2002): 997-1006.
  9. Khan, Haider Adnan, Abdullah Al Helal, and Khawza I. Ahmed. "Handwritten bangla digit recognition using sparse representation classifier." Informatics, Electronics & Vision (ICIEV), 2014 International Conference on. IEEE, 2014.
  10. Bhowmik, T.K., Bhattacharya, U. and Parui, S.K., 2004, November. Recognition of Bangla handwritten characters using an MLP classifier based on stroke features. In International Conference on Neural Information Processing(pp. 814-819).Springer, Berlin, Heidelberg.
  11. Alom, MdZahangir, et al. "Handwritten Bangla Digit Recognition Using Deep Learning." arXiv preprint arXiv:1705.02680 (2017).
  12. Szegedy, Christian, et al. "Going deeper with convolutions." Proceedings of the IEEE conference on computer vision and pattern recognition. 2015.
  13. Scherer, Dominik, Andreas Müller, and Sven Behnke. "Evaluation of pooling operations in convolutional architectures for object recognition." Artificial Neural Networks–ICANN 2010 (2010): 92-101.
  14. Discussion about Convolutional Neural Network http://ufldl.stanford.edu/tutorial/supervised/ConvolutionalNeuralNetwork (LAT*-16 july,2017,11:06pm) .
  15. Stanford cs231n lectures on CNN http://cs231n.github.io /convolutional-networks (LAT*-15 april,2018,12:35am).
  16. A Beginner's Guide To Understanding Convolutional Neural Network https://adeshpande3.github.io/ adeshpande3.github.io/A-Beginner%27s-Guide-ToUnderstanding-Convolutional-Neural-Networks (LAT*-20 june,2017,9:21pm).
  17. Discussion about input dimension of CNNs https://www.quora.com/Does-input-dimension-resolution-affect-the-performance-of-Convolutional-Neural-Networks. (LAT*-30 april,2018,12:00am).
  18. Coursera DNN specialization course lectures https://www.coursera.org/learn/neural-networks-deeplearning/ lecture/A0tBd/ gradient-descent(LAT*-28 January,2018,10:02am).
  19. Selected dataset repository on google code http://code.google.com/p/cmaterdb(LAT*-18 june,2017,
  20. 6:42 pm).
  21. Discussion the characterization of epoch and iteration https://stackoverflow.com/questions/4752626/epoch-vs-iteration-when-training neural-networks(LAT*-28 November,2017,10:s02am).
  22. Baldi, P. and Sadowski, P.J., 2013. Understanding dropout. In Advances in neural information processing systems (pp. 2814-2822).
  23. Kanan, Christopher, and Garrison W. Cottrell. "Color-to-grayscale: does the method matter in image recognition?." PloS one 7.1 (2012): e29740.
  24. Article explaining CNNs on a deeper level https://ujjwalkarn.me/2016/08/11/intuitive-explanation-convnets(LAT*-23 july, 2017,8:50pm) .
  25. Article explaining inception modules https://hacktilldawn.com/2016/09/25/inception-modules-explained-and-implemented(LAT*-18april,2018,10:15
  26. pm).
  27. Discussion on the process of calculating the number of parameters of a convolutional neural network https://stackoverflow.com/questions/42786717/how-to-calculate-the-number-of-parameters-for-convolutiona-neural-neural-network/42787467(LAT*-25april,2018, 6:45pm).
  28. Coursera DNN lecture on activation functions https://www.coursera.org/learn/neural-networks-deep-learning/lecture/ OASKH/ why-do-you-need-non-linear-activationfunctions(LAT*-28january,2018,10:03am).
Index Terms

Computer Science
Information Sciences

Keywords

Handwritten Bangla character Shallow convonet CNN Inception Data Normalization

Powered by PhDFocusTM