A comparative analysis of image transformations for handwritten Odia numeral recognition

Tusar Kanti Mishra, Banshidhar Majhi, Sandeep Panda

Research output: Chapter in Book/Report/Conference proceedingConference contribution

30 Citations (Scopus)

Abstract

The work proposed in this paper is an attempt to develop two recognizers for Odia handwriting based on basic transformation schemes and compares their pros and cons. Both the recognizers put emphasis on exploiting the inherent characteristics of the Odia numeral images. The proposed method analyzes the use of Discrete Cosine Transformation (DCT) and Discrete Wavelet Transformation (DWT) for this purpose. Recognition by classification is achieved by feeding these vectors as input to a Back Propagation Neural Network (BPNN). Recognition results are obtained and compared by experimentally varying the classifier parameters. Our experimental results come out to be promising. Thus, finally, we come of with a robust recognizer for handwritten Odia numerals.

Original languageEnglish
Title of host publicationProceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013
Pages790-793
Number of pages4
DOIs
Publication statusPublished - 2013
Event2013 2nd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013 - Mysore, India
Duration: 22-08-201325-08-2013

Publication series

NameProceedings of the 2013 International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013

Conference

Conference2013 2nd International Conference on Advances in Computing, Communications and Informatics, ICACCI 2013
Country/TerritoryIndia
CityMysore
Period22-08-1325-08-13

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Information Systems

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