A Fused Feature Extraction Approach to OCR: MLP vs. RBF

Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 248)

Abstract

This paper is focused on evaluating the capability of MLP and RBF neural network classifier algorithms for performing handwritten character recognition task. Projection profile features for the character images are extracted and merged with the binarization features obtained after preprocessing every character image. The fused features thus obtained are used to train both the classifiers i.e. MLP and RBF Neural Networks. Simulation studies are examined extensively and the proposed fused features are found to deliver better recognition accuracy when used with RBF Network as a classifier.

Keywords

MLP RBF Hybrid Feature Extraction Character Recognition OCR Neural Network 

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Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Maharaja Surajmal InstituteNew DelhiIndia
  2. 2.UIET, Maharshi Dayanand UniversityRohtakIndia

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