Main Article Content

Madhuri Yadav
Guru Govind Singh Indraprastha University
India
Biography
Ravindra Kr Purwar
USIC&T, GGSIPU
India
Anchal Jain
Deptt. of CSE, Indraprastha Engineering College
India
Vol. 7 No. 3 (2018), Articles, pages 47-62
DOI: https://doi.org/10.14201/ADCAIJ2018734762
Accepted: Sep 21, 2018
Copyright

Abstract

Handwritten character recognition is a challenging problem which received attention because of its potential benefits in real-life applications. It automates manual paper work, thus saving both time and money, but due to low recognition accuracy it is not yet practically possible. This work achieves higher recognition rates for handwritten isolated characters using Deep learning based Convolutional neural network (CNN). The architecture of these networks is complex and plays important role in success of character recognizer, thus this work experiments on different CNN architectures, investigates different optimization algorithms and trainable parameters. The experiments are conducted on two different types of grayscale datasets to make this work more generic and robust. One of the CNN architecture in combination with adadelta optimization achieved a recognition rate of 97.95%. The experimental results demonstrate that CNN based end-to-end learning achieves recognition rates much better than the traditional techniques.

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