This paper presents entry for aura response for character recognition and the handwritten or printed text translation into editable text. The objective is to identify handwritten characters with the help of neural networks and facilitates the conversion of handwritten documents to editable text from document images. Handwritten contentedness boasts challenges that are seldom encountered in machine-printed text. The translation basis is either mechanical or electronic translation. This is not easy since different people have different handwriting styles. Assigning distinct templates to each and every alphabet and numbers is the approach described. This concept can be a trademark in data entry applications. The suggested method is simple, have promising discrimination accuracy and less time complexity.

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Handwriting Recognition Template Electronic Translation