A new method to improve multi font farsi/arabic character segmentation results: using extra classes of some character combinations

  • Authors:

  • Mona Omidyeganeh;Reza Azmi;Kambiz Nayebi;Abbas Javadtalab

  • Affiliations:

  • Iran Telecommunication Research Center (ITRC), Tehran, Iran;Computer Dep., Azzahra University, Vanak, Tehran, Iran;Electrical Eng. Dep., Sharif University, Teharan, Iran;Computer Eng. Dep., Sharif University, Tehran, Iran

  • Venue:
  • MMM'07 Proceedings of the 13th international conference on Multimedia Modeling - Volume Part I
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

A new segmentation algorithm for multifont Farsi/Arabic texts based on conditional labeling of up and down contours was presented in [1]. A preprocessing technique was used to adjust the local base line for each subword. Adaptive base line, up and down contours and their curvatures were used to improve the segmentation results. The algorithm segments 97% of 22236 characters in 18 fonts correctly. However, finding the best way to receive high performance in the multifont case is challengeable. Different characteristics of each font are the reason. Here we propose an idea to consider some extra classes in the recognition stage. The extra classes will be some parts of characters or the combination of 2 or more characters causing most of errors in segmentation stage. These extra classes will be determined statistically. We have used a learn document of 4820 characters for 4 fonts. Segmentation result improves from 96.7% to 99.64%.