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Ensemble Neural Network in Classifying Handwritten Arabic Numerals
Department of Computer Science, V. H. N. S. N. College, Virudhunagar, India
Department of Computer Science, V. H. N. S. N. College, Virudhunagar, India
- 1 Department of Computer Science, V. H. N. S. N. College, Virudhunagar, India
- 2 Department of Computer Science, V. H. N. S. N. College, Virudhunagar, India
Journal of Intelligent Learning Systems and Applications·Volume 08 (2015)·Pages 1–8·Published 28 December 2015·DOI10.4236/jilsa.2016.81001
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Abstract
A method has been proposed to classify handwritten Arabic numerals in its compressed form using partitioning approach, Leader algorithm and Neural network. Handwritten numerals are represented in a matrix form. Compressing the matrix representation by merging adjacent pair of rows using logical OR operation reduces its size in half. Considering each row as a partitioned portion, clusters are formed for same partition of same digit separately. Leaders of clusters of partitions are used to recognize the patterns by Divide and Conquer approach using proposed ensemble neural network. Experimental results show that the proposed method recognize the patterns accurately.
KeywordsHandwritten NumeralsDivide and ConquerClusterLeader AlgorithmNeural NetworkEnsembleClassification
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