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Deep belief network based approach to recognize handwritten Kannada characters using distributed average of gradients

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Abstract

Even though various advances have been made in recent years, the recognition of handwritten characters is still an open challenge in the Pattern Recognition field. Different approaches are invented for the recognition of printed characters of Indian languages. However, few attempts are done for the recognition of handwritten characters. A high degree of recognition accuracy for the handwritten characters is yet to be achieved. In this paper, a new approach based on deep belief network with the distributed average of gradients feature is presented for the recognition of isolated handwritten characters of Kannada, which is the official language of Karnataka state in India. In the proposed methods, a better accuracy is achieved.

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Authors and Affiliations

  1. Bharathiar University, Coimbatore, India

    S. Karthik

  2. Nagarjuna College of Engineering & Technology, Bengaluru, India

    K. Srikanta Murthy

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  1. S. Karthik
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Karthik, S., Srikanta Murthy, K. Deep belief network based approach to recognize handwritten Kannada characters using distributed average of gradients. Cluster Comput 22 (Suppl 2), 4673–4681 (2019). https://doi.org/10.1007/s10586-018-2274-0

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  • DOI: https://doi.org/10.1007/s10586-018-2274-0

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