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Book 2 Advanced Research in Artificial Intelligence >

Please use this identifier to cite or link to this item: http://hdl.handle.net/10525/1026

Title: Offline Handwriting Recognition Using Genetic Algorithm
Authors: Mathur, Shashank
Aggarwal, Vaibhav
Joshi, Himanshu
Ahlawat, Anil
Keywords: Handwriting Recognition
Segementation
Artificial Neural Networks
Genetic Algorithm
Issue Date: 2008
Publisher: Institute of Information Theories and Applications FOI ITHEA
Abstract: In this paper, a new method for offline handwriting recognition is presented. A robust algorithm for handwriting segmentation has been described here with the help of which individual characters can be segmented from a word selected from a paragraph of handwritten text image which is given as input to the module. Then each of the segmented characters are converted into column vectors of 625 values that are later fed into the advanced neural network setup that has been designed in the form of text files. The networks has been designed with quadruple layered neural network with 625 input and 26 output neurons each corresponding to a character from a-z, the outputs of all the four networks is fed into the genetic algorithm which has been developed using the concepts of correlation, with the help of this the overall network is optimized with the help of genetic algorithm thus providing us with recognized outputs with great efficiency of 71%.
URI: http://hdl.handle.net/10525/1026
ISSN: 1313-0455
Appears in Collections:Book 2 Advanced Research in Artificial Intelligence

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