Scalable Handwritten Digit Recognition Application using Neural Network and Convolutional Neural Network On Heterogeneous Architecture
Kirankumar Kataraki1, Sumana Maradithaya2
1Kirankumar Kataraki, Research Scholar, Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, India.
2Dr. Sumana Maradithaya, Associate Professor, Department of Information Science and Engineering, Ramaiah Institute of Technology, Bengaluru, India.
Manuscript received on 15 August 2019. | Revised Manuscript received on 25 August 2019. | Manuscript published on 30 September 2019. | PP: 1373-1376 | Volume-8 Issue-3 September 2019 | Retrieval Number: B3415078219/19©BEIESP | DOI: 10.35940/ijrte.B3415.098319
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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: Recognition of handwritten digit is one of the popular problem associated with computer vision applications. The goal of our research work is to develop scalable Neural Network(NN) and Convolutional Neural Network (CNN) model that would be able to recognize and determine the handwritten digits from its image. Capability of developing the new algorithms and improve the existing algorithms is determined by the accuracy and speed factor for training and testing the models. In this context, performance of the GPUs and CPUs for handwritten digit system and effects of accelerating the training models have been analyzed. The training and testing has been conducted from publicly available MNIST handwritten database. Web based, offline and online handwritten digit recognition system is developed by using Convolutional Neural Network.
Index Terms: Neural Network(NN), Convolutional Neural Network (CNN), Handwritten Digit, Scalable.
Scope of the Article: Neural Information Processing