Algorithms for approximate string matching
Information and Control
Artificial intelligence: a knowledge-based approach
Artificial intelligence: a knowledge-based approach
A Fast and Flexible Thinning Algorithm
IEEE Transactions on Computers
Algebraic Description of Curve Structure
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
The String-to-String Correction Problem
Journal of the ACM (JACM)
Tries for Approximate String Matching
IEEE Transactions on Knowledge and Data Engineering
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Localization, Extraction and Recognition of Text in Telugu Document Images
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Digital Image Processing (3rd Edition)
Digital Image Processing (3rd Edition)
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
A novel look-ahead optimization strategy for trie-based approximate string matching
Pattern Analysis & Applications
Off-Line Handwritten Character Recognition of Devnagari Script
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Robust Text Line, Word And Character Extraction from Telugu Document Image
ICETET '09 Proceedings of the 2009 Second International Conference on Emerging Trends in Engineering & Technology
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This paper shows a character recognition mechanism based on a syntactic PR approach that uses the trie data structure for efficient recognition. It uses approximate matching of the string for classification. During the preprocessing an input character image is transformed into a skeletonized image and discrete curves are found using a 3 x 3 pixel region. A trie, which we call as a sequence trie is used for a look up approach at a lower level to encode a discrete curve pattern of pixels. The sequence of such discrete curves from the input pattern is looked up in the sequence trie. The encoding of several such sequence numbers for the thinned character constructs a pattern string. Approximate string matching is used to compare the encoded pattern string from a template character with the pattern string obtained from the input character. We consider the approximate matching of the string instead of the exact matching to make the approach robust in the presence of noise. Another trie data structure (called pattern trie) is used for the efficient storage and retrieval for approximate matching of the string. We make use of the trie since it takes O(m) in worst case where m is the length of the longest string in the trie. For the approximate string matching we use look ahead with a branch and bound scheme in the trie. Here we apply our method on 43 Telugu characters from the basic Telugu characters for demonstration. The proposed approach has recognised all the test characters given here correctly, however more extensive testing on realistic data is required.