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Offline Handwritten Hindi Character Recognition Using Deep Learning with Augmented Dataset

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Cyber Security in Intelligent Computing and Communications

Part of the book series: Studies in Computational Intelligence ((SCI,volume 1007))

Abstract

It is highly desirable to develop an offline handwritten character recognition system for the Hindi script with an acceptable accuracy level. This script has many characters, including numerals and different shape variations of these characters. Moreover, the Hindi script also has compound characters as well as characters with modifiers. It makes the task of character recognition more complex. Even the shape of characters written by an individual may have significant variation at different times. Hence, incorporating all these issues is very challenging. This paper proposes the augmented data set to overcome these problems up to an extent. The augmented data set introduces the hypothetical changes in the original data set to increase the variations in the data samples, which may increase the variety of samples. Thus, the accuracy of the recognition system improves. For the classification of characters, Convolutional Neural Networks (CNNs) are employed. Based on CNN, classification models are more suitable for recognition tasks where the images are taken as inputs. So, utilizing deep learning algorithms for recognition of Hindi script characters with the projected scheme will improve the accuracy of the system.

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Authors and Affiliations

  1. Central University of Rajasthan, Ajmer, India

    Ajay Indian

  2. Gurukula Kangri Vishwavidyalaya, Haridwar, India

    Karamjit Bhatia & Krishan Kumar

Authors
  1. Ajay Indian
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  2. Karamjit Bhatia
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  3. Krishan Kumar
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Correspondence to Ajay Indian .

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Editors and Affiliations

  1. GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India

    Rajeev Agrawal

  2. Kennesaw State University, Kennesaw, GA, USA

    Jing He

  3. Malaviya National Institute of Technology (MNIT), Jaipur, India

    Emmanuel Shubhakar Pilli

  4. Department of Master of Computer Applications, GL Bajaj Institute of Technology and Management, Greater Noida, Uttar Pradesh, India

    Sanjeev Kumar

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Indian, A., Bhatia, K., Kumar, K. (2022). Offline Handwritten Hindi Character Recognition Using Deep Learning with Augmented Dataset. In: Agrawal, R., He, J., Shubhakar Pilli, E., Kumar, S. (eds) Cyber Security in Intelligent Computing and Communications. Studies in Computational Intelligence, vol 1007. Springer, Singapore. https://doi.org/10.1007/978-981-16-8012-0_11

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  • DOI: https://doi.org/10.1007/978-981-16-8012-0_11

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-16-8011-3

  • Online ISBN: 978-981-16-8012-0

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