Text-Independent Handwriting Classification Using Line and Texture-Based Features

  • T. Shreekanth
  • M. B. Punith Kumar
  • Akshay KrishnanEmail author
Conference paper
Part of the Lecture Notes in Computational Vision and Biomechanics book series (LNCVB, volume 30)


This paper addresses the problem of making a machine recognize the writer by means of the handwriting. It delineates the preprocessing methods used to enhance handwritings, so as to ease the process of feature extraction. It discusses six statistical texture-based features that characterize a handwriting. Once these features are extracted, a nearest neighbor approach is used to classify a sample handwriting into one of those in the database. The methods are verified on a self-compiled database, and a performance evaluation is also performed. This method can be used to identify an unknown handwriting and is in specific demand in the forensic domain. Unlike other biometric identification methods, handwriting-based identification is the least intrusive.


Handwriting recognition Text independent Feature extraction Preprocessing Classification 


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • T. Shreekanth
    • 1
  • M. B. Punith Kumar
    • 2
  • Akshay Krishnan
    • 1
    Email author
  1. 1.Department of ECESJCEMysoreIndia
  2. 2.Department of ECEPESCEMandyaIndia

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