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Real Time Handwritten Character Recognition Using Ann

Authors: Ishan Gulati*1, Gautam Vig2 & Vijay Khare3;

Real Time Handwritten Character Recognition Using Ann

Abstract

-Real time Handwritten Character Recognition by using Template Matching is a system which is useful to recognize the character or alphabets in the given text by comparing two images of the alphabet. The objectives of this system prototype are to develop a program for the Optical Character Recognition (OCR) system by using the Template Matching algorithm . Handwritten character recognition is a challenging task in the field of research on image processing, artificial intelligence as well as machine vision since the handwriting varies from person to person. Moreover, the handwriting styles, sizes and its orientation make it even more complex to interpret the text. The numerous applications of handwritten text in reading bank cheques, Zip Code recognition and in removing the problem of handling documents manually has made it necessary to acquire digitally formatted data. This paper presents the recognition of handwritten characters using either a scanned document, or direct acquisition of image using Matlab, followed by the implementation of various other Matlab toolboxes like Image Processing and Neural Network Toolbox to process the scanned or acquired image. Experimental Results are given to present the proposed model in order to recognize handwritten characters accurately.

Keywords

Image Rendering, Character Extraction, Image Processing, Edge Detection, Neural Network, Back Propagation Network, Multi Layer Perceptron Network.

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
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