Abstract
The recognition of Indian handwritten languages are attracting the attention of researchers due to wide variation and scope. This manuscript is focus on the Accuracy enhancement of handwritten Devanagari character recognition using background elimination and gray level normalization techniques. Devanagari is a famous and widely used script in India. In last few decades, the gradient based approaches have become a best choice to extract the features from handwritten characters. GLAC (Gradient Local Auto-Correlation) feature extraction technique is used for the experiment. All the experiments have done on standard handwritten Devanagari database (36172) and obtained (95.94%) higher recognition rate.
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