Offline Recognition of Malayalam and Kannada Handwritten Documents Using Deep Learning

  • Ayna Asokan Department of Electronics and Communication Engineering, NSS College of Engineering, Palakkad, Kerala, India.
  • Sreeleja N Unnithan Department of Electronics and Communication Engineering, NSS College of Engineering, Palakkad, Kerala, India.
Keywords: Holistic, Analytic, Handwriting recognition


For a variety of reasons, handwritten text can be digitalized. It is used in a variety of government entities, including banks, post offices, and archaeological departments. Handwriting recognition, on the other hand, is a difficult task as everyone has a different writing style. There are essentially two methods for handwritten recognition: a holistic and an analytic approach. The previous methods of handwriting recognition are time- consuming. However, as deep neural networks have progressed, the approach has become more straightforward than previous methods. Furthermore, the bulk of existing solutions are limited to a single language. To recognise multilanguage handwritten manuscripts offline, this work employs an analytic approach. It describes how to convert Malayalam and Kannada handwritten manuscripts into editable text. Lines are separated from the input document first. After that, word segmentation is performed. Finally, each word is broken down into individual characters. An artificial neural network is utilised for feature extraction and classification. After that, the result is converted to a word document.


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How to Cite
Asokan, A., & N Unnithan, S. (2021). Offline Recognition of Malayalam and Kannada Handwritten Documents Using Deep Learning. International Journal of Computer Communication and Informatics, 3(2), 12-24.

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