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A Study on the Effect of CNN-Based Transfer Learning on Handwritten Indic and Mixed Numeral Recognition

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Document Analysis and Recognition (DAR 2018)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1020))

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Abstract

Filling up forms at post offices, railway counters, and for application of jobs has become a routine for modern people, especially in a developing country like India. Research on automation for the recognition of such handwritten forms has become mandatory. This applies more for a multilingual country like India. In the present work, we use readily available pre-trained Convolutional Neural Network (CNN) architectures on four different Indic scripts, viz. Bangla, Devanagari, Oriya, and Telugu to achieve a satisfactory recognition rate for handwritten Indic numerals. Furthermore, we have mixed Bangla and Oriya numerals and applied transfer learning for recognition. The main objective of this study is to realize how good a CNN model trained on an entire different dataset (of natural images) works for small and unrelated datasets. As a part of practical application, we have applied the proposed approach to recognize Bangla handwritten pin codes after their extraction from postal letters.

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Authors and Affiliations

  1. Department of Computer Science and Engineering, Indian Institute of Technology (ISM) Dhanbad, Dhanbad, India

    Rahul Pramanik, Prabhat Dansena & Soumen Bag

Authors
  1. Rahul Pramanik
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  2. Prabhat Dansena
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  3. Soumen Bag
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Corresponding author

Correspondence to Rahul Pramanik .

Editor information

Editors and Affiliations

  1. Electronics and Electrical Engineering, Indian Institute of Technology Guwahati, Guwahati, India

    Suresh Sundaram

  2. Computer Science and Engineering, Indian Institute of Technology Jodhpur, Karwar, Rajasthan, India

    Gaurav Harit

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Pramanik, R., Dansena, P., Bag, S. (2019). A Study on the Effect of CNN-Based Transfer Learning on Handwritten Indic and Mixed Numeral Recognition. In: Sundaram, S., Harit, G. (eds) Document Analysis and Recognition. DAR 2018. Communications in Computer and Information Science, vol 1020. Springer, Singapore. https://doi.org/10.1007/978-981-13-9361-7_4

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  • DOI: https://doi.org/10.1007/978-981-13-9361-7_4

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  • Publisher Name: Springer, Singapore

  • Print ISBN: 978-981-13-9360-0

  • Online ISBN: 978-981-13-9361-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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