Classifying Handwritten Digit Recognition Using CNN and PSO
Priyanka B. Barhate1, G. D. Upadhye2
1Ms. Priyanka B. Barhate, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering, Tathawade, Pune, India.
2Mr. G. D. Upadhye, Department of Computer Engineering, JSPM’s Rajarshi Shahu College of Engineering, Tathawade, Pune, India.
Manuscript received on 07 March 2019 | Revised Manuscript received on 14 March 2019 | Manuscript published on 30 July 2019 | PP: 5983-5987 | Volume-8 Issue-2, July 2019 | Retrieval Number: B3675078219/19©BEIESP | DOI: 10.35940/ijrte.B3675.078219
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: A normal human can easily recognize any written or typed or scanned text, numbers, etc., but when it comes to a machine, it is difficult to find out what exactly that given text or numbers. It will be difficult to recognize a handwritten digit for a machine. Many machine learning methods were used to fix the handwritten digit recognition issue. It is growing in more convoluted domains, so its training complexity is also increasing. To overcome this complexity problem, many algorithms have been implemented. In this paper, the Convolutional Neural Network (CNN) and Particle Swarm Optimization (PSO), those two approaches do use for recognition of the isolated handwritten digit. Customized PSO is used to reduce the overall computation time of the proposed system. The customized PSO used with CNN, to decreases the required number of epochs for training. It is used to identify digits in the MNIST handwritten digital database to predict the number. The system has achieved an average of 94.90% accuracy.
Index Terms: Pattern Recognition, Handwritten Digit Recognition, Convolutional Neural Network, Particle Swarm Optimization, Machine Learning.
Scope of the Article: Machine Learning