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Offline Handwritten Malayalam Word Recognition Using a Deep Architecture

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Soft Computing for Problem Solving

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 816))

Abstract

Handwriting recognition is an important application of pattern recognition subject. Although some research studies of handwriting recognition of a few major Indian scripts can be found in the literature, the same is not true for many of the Indian scripts. Malayalam is one such script, and automatic recognition issues of this script remain largely unexplored till date. On the other hand, there are nearly 40 million people mainly living in the southern part of India whose native language is Malayalam. In the present article, we present our recent study of Malayalam offline handwritten word recognition. The main contributions of the present study are (a) pioneering development of a database for offline handwritten word samples of Malayalam script and (b) its benchmark recognition results based on a transfer learning strategy which involves a deep convolutional neural network (CNN) architecture for feature extraction and a support vector machine (SVM) for classification. Recognition result of the proposed architecture on the writer independent test set of Malayalam handwritten word sample database is quite satisfactory. Moreover, the same architecture has been found to improve the existing state of the art of offline handwriting recognition of several major Indian scripts.

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Author information

Authors and Affiliations

  1. Artificial Intelligence Lab, Department of Computer Applications, Cochin University of Science and Technology, Kerala, India

    P. J. Jino

  2. Department of Computer Applications, Cochin University of Science and Technology, Kerala, India

    Kannan Balakrishnan

  3. CVPR Unit, Indian Statistical Institute, 203 B.T. Road, Kolkata, India

    Ujjwal Bhattacharya

Authors
  1. P. J. Jino
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  2. Kannan Balakrishnan
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  3. Ujjwal Bhattacharya
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Corresponding author

Correspondence to Ujjwal Bhattacharya .

Editor information

Editors and Affiliations

  1. Department of Mathematics, South Asian University New Delhi , New Delhi, India

    Jagdish Chand Bansal

  2. Department of Mathematics, National Institute Of Technology Silchar Department of Mathematics, Silchar, Assam, India

    Kedar Nath Das

  3. Department of Mathematics and Computer Science, Faculty of Science, , Liverpool Hope University, Liverpool, UK

    Atulya Nagar

  4. Department of Mathematics, Indian Institute of Technology Roor Department of Mathematics, Roorkee, Uttarakhand, India

    Kusum Deep

  5. School of Basic Sciences, Indian Institute of Technology Bhubanesw School of Basic Sciences, Bhubaneswar, Odisha, India

    Akshay Kumar Ojha

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Jino, P.J., Balakrishnan, K., Bhattacharya, U. (2019). Offline Handwritten Malayalam Word Recognition Using a Deep Architecture. In: Bansal, J., Das, K., Nagar, A., Deep, K., Ojha, A. (eds) Soft Computing for Problem Solving. Advances in Intelligent Systems and Computing, vol 816. Springer, Singapore. https://doi.org/10.1007/978-981-13-1592-3_73

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