A Novel Framework for Recognize Handwritten Character and Text Detection using Neural Network

Abstract

Real time Handwritten Character Recognition via using Template Matching is a system that's beneficial to recognize the person or alphabets in the given textual content through comparing two pictures of the alphabet. The objectives of this system prototype are to expand a software for the Optical Character Recognition (OCR) gadget by using using the Template Matching set of rules. Handwritten character popularity is a difficult challenge inside the field of research on picture processing, artificial intelligence as well as gadget vision because the handwriting varies from individual to man or woman. Moreover, the handwriting styles, sizes and its orientation make it even more complex to interpret the textual content. The several packages of handwritten textual content in analyzing bank cheques, Zip Code reputation and in casting off the hassle of managing documents manually has made it essential to accumulate digitally formatted facts. This paper presents the popularity of handwritten characters the use of both a scanned file, or direct acquisition of photograph the use of Matlab, followed with the aid of the implementation of diverse other tool boxes like Image Processing and Neural Network Toolbox to procedure the scanned or acquired photo. Experimental Results are given to offer the proposed version to be able to apprehend handwritten characters accurately.