One of the most researched topics with wide implementations in different fields is Handwritten Character Recognition
(HCR). To utilize this technology in automated data-entry applications, this is one of the fields on which past and recent works
have been done to focus on a variety of languages. Individual characters in a word are recognized through a set of images,
which are then studied by the Deep Neural Network. It is with the help of this recognition that results are ranked. This ranking
is performed based on the client’s request. A Convolution Deep Neural Network Model is considered to acknowledge the
handwritten characters in this paper. It learns, using local receptive areas and heavily connected neural network layers, a useful
set of ordination which are required to generate an optimum result.
