Automatic Recognition of Unconstrained Off-Line Bangla Handwritten Numerals
Identifieur interne : 001D81 ( Main/Exploration ); précédent : 001D80; suivant : 001D82Automatic Recognition of Unconstrained Off-Line Bangla Handwritten Numerals
Auteurs : U. Pal [Inde] ; Bidyut Baran Chaudhuri [Inde]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2000.
Abstract
Abstract: This paper deals with an automatic recognition method for unconstrained off-line Bangla handwritten numerals. To take care of variability involved in the writing style of different individuals a robust scheme is presented here. The scheme is based on new features obtained from the concept of water overflow from the reservoir as well as topological and statistical features of the numerals. If we pour water from upper part of the character, the region where water will be stored in the character is imagined as a reservoir of the character. The direction of water overflow, height of water level when water overflows from the reservoir, position of the reservoir with respect to the character bounding box, shape of the reservoir etc. are used in the recognition scheme. The proposed scheme is tested on data collected from different individuals of various background and we obtained an overall recognition accuracy of about 91.98%.
Url:
DOI: 10.1007/3-540-40063-X_49
Affiliations:
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<front><div type="abstract" xml:lang="en">Abstract: This paper deals with an automatic recognition method for unconstrained off-line Bangla handwritten numerals. To take care of variability involved in the writing style of different individuals a robust scheme is presented here. The scheme is based on new features obtained from the concept of water overflow from the reservoir as well as topological and statistical features of the numerals. If we pour water from upper part of the character, the region where water will be stored in the character is imagined as a reservoir of the character. The direction of water overflow, height of water level when water overflows from the reservoir, position of the reservoir with respect to the character bounding box, shape of the reservoir etc. are used in the recognition scheme. The proposed scheme is tested on data collected from different individuals of various background and we obtained an overall recognition accuracy of about 91.98%.</div>
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