Study of Different Features on Handwritten Devnagari Character

In this paper a scheme for offline handwritten Devnagari character recognition is proposed, which uses different feature extraction and recognition algorithms. The proposed system assumes no constraints in writing style, size or variations. First the character is preprocessed and features namely : chain code histogram, four side views, shadow based are extracted and fed to multilayer perceptrons as a preliminary recognition step. Finally the results of all MLP´s are combined using weighted majority scheme. The proposed system is tested on 1500 handwritten devnagari character database collected from different people. It is observed that the proposed system achieves 98.16% recognition rates as top 5 results and 89.58% as top 1 results.