Robust Pattern Recognition Scheme for Devanagari Script

• Published in: Computational Intelligence and Security pp. 1021 - 1026
• Main Authors: Dhurandhar, Amit, Shankarnarayanan, Kartik, Jawale, Rakesh
• Format: Book Chapter Conference Proceeding
• Language: English
• Published: Berlin, Heidelberg Springer Berlin Heidelberg 2005; Springer
• Series: Lecture Notes in Computer Science
• Subjects: Applied sciences; Artificial intelligence; Computer science; control theory; systems; contour sets; Devanagari script recognition; Exact sciences and technology; feature extraction; horizontal scanning; interpolation; priority matching; vertical scanning; Script; Image processing; Priority; Pattern recognition; Character recognition; Flexibility; Typography; Reference system; Filter; Optimal solution; Database; Safety; Character set; Artificial intelligence
• ISBN: 9783540308188; 3540308180
• ISSN: 0302-9743; 1611-3349
• DOI: 10.1007/11596448_152

In this paper, a Devanagari script recognition scheme based on a novel algorithm is proposed. Devanagari script poses new challenges in the field of pattern recognition primarily due to the highly cursive nature of the script seen across its diverse character set. In the proposed algorithm, the character is initially subjected to a simple noise removal filter. Based on a reference co-ordinate system, the significant contours of the character are extracted and characterized as a contour set. The recognition of the character involves comparing these contour sets with those in the enrolled database. The matching of these contour sets is achieved by characterizing each contour based on its length, its relative position in the reference co-ordinate system and an interpolation scheme which eliminates displacement errors. In the Devanagari script, similar contour sets are observed among few characters, hence this method helps to filter out disparate characters and narrow down the possibilities to a limited set. The next step involves focusing on the subtle yet vital differences between the similar contours in this limited set. This is done by a prioritization scheme which concentrates only on those portions of character which reflect its uniqueness. The major challenge in developing the proposed scheme lay in striking the right balance between definiteness and flexibility to derive optimal solutions for out of sample data. Experimental results show the validity and efficiency of the developed scheme for recognition of characters of this script.