A New Method to Improve Multi Font Farsi/Arabic Character Segmentation Results: Using Extra Classes of Some Character Combinations

• Published in: Advances in Multimedia Modeling pp. 670 - 679
• Main Authors: Omidyeganeh, Mona, Azmi, Reza, Nayebi, Kambiz, Javadtalab, Abbas
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2007; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Arabic Text; Artificial intelligence; Character Segmentation; Computer science; control theory; systems; Exact sciences and technology; Pattern recognition. Digital image processing. Computational geometry; Segmentation Algorithm; Segmentation Result; Text Line; Multimedia; High performance; Statistical analysis; Arabic; Segmentation; Contour line; Text; Curvature; Adaptive method
• ISBN: 3540694218; 9783540694212
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/978-3-540-69423-6_65

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%.