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

  • Milind BhaleraoEmail author
  • Sanjiv Bonde
  • Abhijeet Nandedkar
  • Sushma Pilawan
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (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.

Keywords

Devanagari handwritten characters SVM classifier Quadratic classifier Feature extraction HOG 

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Copyright information

© Springer International Publishing AG  2018

Authors and Affiliations

  • Milind Bhalerao
    • 1
    Email author
  • Sanjiv Bonde
    • 1
  • Abhijeet Nandedkar
    • 1
  • Sushma Pilawan
    • 1
  1. 1.Shri Guru Gobind Singhji Institute of Engineering and TechnologyNandedIndia

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