Recognition of Handprinted Bangla Numerals Using Neural Network Models
Identifieur interne : 001819 ( Main/Exploration ); précédent : 001818; suivant : 001820Recognition of Handprinted Bangla Numerals Using Neural Network Models
Auteurs : U. Bhattacharya [Inde] ; K. Das [Inde] ; A. Datta [Inde] ; K. Parui [Inde] ; Bidyut Baran Chaudhuri [Inde]Source :
- Lecture Notes in Computer Science [ 0302-9743 ] ; 2002.
Abstract
Abstract: This paper proposes an automatic recognition scheme for handprinted Bangla (an Indian script) numerals using neural network models. A Topology Adaptive Self Organizing Neural Network is first used to extract from a numeral pattern a skeletal shape that is represented as a graph. Certain features like loops, junctions etc. present in the graph are considered to classify a numeral into a smaller group. If the group is a singleton, the recognition is done. Otherwise, multilayer perceptron networks are used to classify different numerals uniquely. The system is trained using a sample data set of 1880 numerals and we obtained 90.56% correct recognition rate on a test set of another 3440 samples. The proposed scheme is sufficiently robust with respect to considerable object noise.
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DOI: 10.1007/3-540-45631-7_31
Affiliations:
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