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%.
