Recognition of Non-Compound Handwritten Devnagari Characters using a Combination of MLP and Minimum Edit Distance

International Journal of Computer Science and Security

D. K. Basu¹  M.Kundu¹  Sandhya Arora¹  Mita Nasipuri¹  Debotosh Bhattacharjee¹ 

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关键词: Harris corner detector;    Classification;    Multilayer Perceptron;    Minimum Edit Distance method.;    feature extraction;   

DOI  :  

来源: Computer Science and Security

【 摘 要 】

This paper deals with a new method for recognition of offline Handwritten Devnagari Character. It uses two well known and established pattern recognition techniques: one using neural networks and the other one using minimum edit distance. Each of these techniques is applied on different sets of characters for recognition. Here two sets of features are computed and two classifiers are applied to get higher recognition accuracy. Two MLP’s are used separately to recognize the characters. For one of the MLP’s the characters are represented with their shadow features and for the other chain code histogram feature is used. The decision of both MLP’s is combined using weighted majority scheme. Top three results produced by combined MLP’s is used to calculate the relative difference value. Based on this relative difference character set is divided into two. First set consists of the characters with distinct shapes and second set consists of confused characters, which appear very similar in shapes. Characters of distinct shapes of first set are classified using MLP. Confused characters in second set are classified using minimum edit distance method. Method of minimum edit distance makes use of corner detected in a character image using modified Harris corner detection technique. Experiment on this method is carried out on a database of 7154 samples. The overall recognition is found to be 90.74%.

【 授权许可】

Unknown