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Character Recognition from Handwritten Image Using Convolutional Neural Networks

  • Ranjan JanaEmail author
  • Siddhartha Bhattacharyya
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 922)

Abstract

Character recognition from handwritten images is of great interest in the pattern recognition research community for their good application in many areas. To implement the system, it requires two steps, viz., feature extraction followed by character recognition based on any classification algorithm. Convolutional neural network (CNN) is an excellent feature extractor and classifier. The performance of a CNN for a particular application depends on the parameters used in the network. In this article, a CNN is implemented for the MNIST dataset with appropriate parameters for training and testing the system. The system provides accuracy up to 98.85%, which is better with respect to others. It also takes very low amount of time for training the system.

Keywords

Handwritten character recognition Optical character recognition Convolutional neural networks Deep neural networks MNIST dataset 

Notes

Acknowledgements

The authors are grateful to IT department, RCC Institute of Information Technology (RCCIIT), Kolkata for providing the requisite infrastructure for the research work. The authors are also grateful to Prof. (Dr.) Ajoy Kumar Roy, Chairman, RCCIIT for his constant inspiration.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Information TechnologyRCC Institute of Information TechnologyKolkataIndia
  2. 2.Department of Computer ApplicationRCC Institute of Information TechnologyKolkataIndia
  3. 3.Faculty of Electrical Engineering and Computer ScienceVSB Technical University of OstravaOstravaCzechia

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