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Combined Classifier Approach for Offline Handwritten Devanagari Character Recognition Using Multiple Features

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Computational Vision and Bio Inspired Computing

Part of the book series: Lecture Notes in Computational Vision and Biomechanics ((LNCVB,volume 28))

Abstract

Offline handwritten character recognition is the process of recognizing given characters from the large set of characters. OCR system mainly focuses on the recognition of printed or handwritten characters of a scanned image. The proposed system extracts features that are based only on gradient of image which is helpful in exact recognition of characters. A technique to recognize handwritten Devanagari characters using combination of quadratic and SVM classifiers is presented in this paper. Features used are directional features that are strength, angle and histogram of gradient (SOG, AOG, HOG). Using a Gaussian filter, the strength and the angle features are down sampled to obtain a feature vector of 392 dimensions. These features are finally concatenated with HOG feature. Applying these to the combination of quadratic and SVM classifiers to obtain maximum accuracy of 95.81% using 3 fold cross validation.

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Authors and Affiliations

  1. Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded, India

    Milind Bhalerao, Sanjiv Bonde, Abhijeet Nandedkar & Sushma Pilawan

Authors
  1. Milind Bhalerao
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  2. Sanjiv Bonde
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  3. Abhijeet Nandedkar
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  4. Sushma Pilawan
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Corresponding author

Correspondence to Milind Bhalerao .

Editor information

Editors and Affiliations

  1. Karunya University , Coimbatore, Tamil Nadu, India

    D. Jude Hemanth

  2. RVS Technical Campus, Coimbatore, Tamil Nadu, India

    S. Smys

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Bhalerao, M., Bonde, S., Nandedkar, A., Pilawan, S. (2018). Combined Classifier Approach for Offline Handwritten Devanagari Character Recognition Using Multiple Features. In: Hemanth, D., Smys, S. (eds) Computational Vision and Bio Inspired Computing . Lecture Notes in Computational Vision and Biomechanics, vol 28. Springer, Cham. https://doi.org/10.1007/978-3-319-71767-8_4

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  • DOI: https://doi.org/10.1007/978-3-319-71767-8_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71766-1

  • Online ISBN: 978-3-319-71767-8

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