Offline Handwriting Recognition System Using Convolutional Network

Authors

  • Aathira Manoj  Computer Engineering, PVPPCOE, Mumbai, Maharashtra, India
  • Priyanka Borate  Computer Engineering, PVPPCOE, Mumbai, Maharashtra, India
  • Pankaj Jain  Computer Engineering, PVPPCOE, Mumbai, Maharashtra, India
  • Vidya Sanas  Computer Engineering, PVPPCOE, Mumbai, Maharashtra, India
  • Rupali Pashte  Computer Engineering, PVPPCOE, Mumbai, Maharashtra, India

Keywords:

Pre-processing, Segmentation, Feature extraction, CNN, LeNet-5.

Abstract

In this paper, we have used Convolutional Neural Network (CNN) for offline handwriting recognition. Our Convolutional Network is based on the LeNet-5 network. We have modified it by changing the number of neurons in each layer. We have also added a dropout layer, which has resulted in slower, but accurate learning from the training set. We have used MNIST database for testing.

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International Journal of Scientific Research in Science, Engineering and Technology

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Published

2017-12-31

Issue

Volume 2, Issue 2, March-April-2016

Section

Research Articles

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This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Aathira Manoj, Priyanka Borate, Pankaj Jain, Vidya Sanas, Rupali Pashte, " Offline Handwriting Recognition System Using Convolutional Network, International Journal of Scientific Research in Science, Engineering and Technology(IJSRSET), Print ISSN : 2395-1990, Online ISSN : 2394-4099, Volume 2, Issue 2, pp.869-872, March-April-2016.