Abstract
A method for recognizing handwritten Hindi numerals is proposed based on the structural descriptors of a numeral's shape. The method consists of three major steps. The first one is preprocessing, where a handwritten numeral is scanned, normalized and then thinned. Next, a robust algorithm is used to segment the scanned image into stroke(s), based on feature points, and to identify cavity features. The output of this algorithm is a syntactic representation (that is one or more syntactic terms). Finally, this syntactic representation is matched against the set of prototype syntactic representations of handwritten numerals for a possible match. Early experimental results are not only encouraging but also proving the tolerance of the proposed system to recognize a high variability of Hindi numerals' shapes. The system attained a successful recognition rate of 96%.
Original language | English |
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Pages (from-to) | 299-314 |
Number of pages | 16 |
Journal | Journal of Experimental and Theoretical Artificial Intelligence |
Volume | 15 |
Issue number | 3 |
DOIs | |
Publication status | Published - Jul 2003 |
Externally published | Yes |
Keywords
- Handwritten recognition
- Hindu numerals
- Structural descriptors
- Term rewriting
ASJC Scopus subject areas
- Software
- Theoretical Computer Science
- Artificial Intelligence