OCR based pixel fusion

Rami Al-Hmouz

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)

Abstract

Character recognition is the process that allows the automatic identification of character images, which is generally referred as Optical Character Recognition (OCR). The characters are either handwritten or typed. This study proposed a novel OCR approach based on the likelihood functions of pixels, which were obtained by averaging a trained set of character images. A Bayesian fusion process for all pixel probabilities decides the recognition of characters. Further tests using Support Vector Machine (SVM) classifier were carried out on characters with the same shape. This method was used to test noisy images and achieved an accuracy of 97.95%, thus, outperforming other OCR methods.

Original languageEnglish
Pages (from-to)2319-2325
Number of pages7
JournalJournal of Applied Sciences
Volume12
Issue number22
DOIs
Publication statusPublished - 2012
Externally publishedYes

Keywords

  • Bayes theorem
  • Fusion
  • Optical character recognition
  • Pixel likelihood function

ASJC Scopus subject areas

  • General

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