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Dhandra, B.V., Benne, R.G., Hangarge, M., Telugu, K. and Handwritten, D. (2011) Numeral Recognition with Probabilistic Neural Network: A Script Independent Approach. International Journal of Computer Applications, 26, 11-16.

has been cited by the following article:

• TITLE: Ensemble Neural Network in Classifying Handwritten Arabic Numerals

  AUTHORS: Kathirvalavakumar Thangairulappan, Palaniappan Rathinasamy

  KEYWORDS: Handwritten Numerals, Divide and Conquer, Cluster, Leader Algorithm, Neural Network, Ensemble, Classification

  JOURNAL NAME: Journal of Intelligent Learning Systems and Applications, Vol.8 No.1, December 28, 2015

  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.