Handwritten Gurmukhi Digit Prediction using Convolutional Neural Network with Keras and Tensorflow
DOI:
https://doi.org//10.32628/IJSRSET1962169Keywords:
Gurmukhi Digit Recognition, Handwritten Digit, Prediction, Convolutional Neural Network, KerasAbstract
Intelligent Character Recognition is a term which is specifically used for the recognition of handwritten character or digit. It is a prominent research area of computer vision field of machine learning or deep learning which trained the machine to analyze the pattern of handwritten character image and identify it. Recognition of handwritten character is a hard process because single person can handwrite the same text in number of ways by making a little variation in holding the pen. Handwriting has no specific font style or size. It differs person to person or more specifically it differs how one is holding the pen. Deep Leaning has brought the breakthrough performance in this research area with its dedicated models like Convolutional Neural Network, Recurrent Neural Network etc. In this paper, we have trained model with Convolutional Neural Network with different number of layers and filters over 10,559 handwritten gurmukhi digit images and validate over 1320 images. Consequently we could achieve the maximum accuracy of 99.24%.
References
- Alex KrizhevskyIlya SutskeverGeoffrey E. Hinton "ImageNet Classification with Deep Convolutional Neural Networks", NIPS 2012
- Kumar, Munish & K. Jindal, M & K. Sharma, R. (2017). A Novel Technique for Line Segmentation in Offline Handwritten Gurmukhi Script Documents. National Academy Science Letters. 40. 273-277. 10.1007/s40009-017-0558-1.
- Pal, Umapada & Chaudhuri, Bidyut. (2004). Chaudhuri, B.B.: Indian script character recognition-A survey. Pattern Recognition 37, 1887-1899. Pattern Recognition. 37. 1887-1899. 10.1016/j.patcog.2004.02.003.
- Siddharth, Kartar & Jangid, Mahesh & Dhir, Renu & Rani, Rajneesh. “Handwritten Gurmukhi Character Recognition Using Statistical and Background Directional Distribution Features”, Internat. J. Computer Sci. Eng. (IJCSE). 3. 2332-2345, 2011.
- Singh G. and Lehri S., “ Recognition of Handwritten Hindi Characters Using Back-propagation Neural Network”, International Journal of Computer Science and Information Technologies, pg. 4892-4895, Vol. 3, Issue 4, 2012.
- Kumar, M., Jindal, M.K. & Sharma, R.K. ,” A Novel Hierachical Technique for Offline Handwritten Gurmukhi Character Recognition” Natl. Acad. Sci. Lett. 37: 567. https://doi.org/10.1007/s40009-014-0280-1,2014
- Verma, K. & Sharma, R.K. Sādhanā.,” Recognition of Online handwritten Gurmukhi characters based on zone and stroke identification” 42: 701. https://doi.org/10.1007/s12046-017-0632-x , 2017
- Ashutosh Aggarwal , Karamjeet Singh, “Use of Gradient Technique for extracting features from Handwritten Gurmukhi Characters and Numerals”,Procedia Computer Science, Volume 46, PP 1716-1723, 2015
- Akm Ashiquzzaman and Abdul Kawsar Tushar, “Handwritten Arabic Numeral Recognition using Deep Learning Neural Network”,IEEE International Conference on Imaging, Vision & Pattern Recognition, At University of Dhaka, Dhaka, Bangladesh, 2017
- S. Acharya, A. K. Pant and P. K. Gyawali, "Deep learning based large scale handwritten Devanagari character recognition," 9th International Conference on Software, Knowledge, Information Management and Applications (SKIMA), Kathmandu, pp. 1-6,2015.
- T. K. Das, A. K. Tripathy and A. K. Mishra, "Optical character recognition using artificial neural network," 2017 International Conference on Computer Communication and Informatics (ICCCI), Coimbatore, 2017, pp. 1-4.doi: 10.1109/ICCCI.2017.8117703
- Saha Sourajit, Nisha Saha, "A Lightning fast approach to classify Bangla Handwritten Characters and Numerals using newly structured Deep Neural Network", Procedia Computer Science, vol. 132, pp. 1760-1770, 2018.
Downloads
Published
Issue
Section
License
Copyright (c) IJSRSET
This work is licensed under a Creative Commons Attribution 4.0 International License.