[b] [/b]Hi am Mohamed i would like to get details on source code for offline handwriting recognition in matlab for genetic algorithm ..My friend Justin said source code for offline handwriting recognition in matlab for genetic algorithm will be available here and now i am living at ......... and i last studied in the college/school ......... and now am doing ....i need help on ......etc
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Handwriting recognition allows a person to scribble something on a piece of paper and then convert it into text. If you look at practical reality there are enumerable styles in which you can write a character. These styles can be combined to generate more styles. Even if a small child knows the basic styles a character can write, he would be able to recognize characters written in intermediate styles between them or formed by their mix. This motivates the use of Genetic Algorithms for the problem. To test this, we did a pool of character images. We turn them into graphs. The graph of each character was intermixed to generate intermediate styles between the styles of the main character. Character recognition implied the matching of the graph generated from the image of unknown character with the graphics generated by the mixture. Using this method we received an accuracy of 98.44%.
Handwriting recognition has always been a special problem, especially in an offline mode. Handwriting recognition is a famous problem that involves the recognition of any contribution given in the form of image, scanned paper, text, etc. There is a lot of work has been done in this area in recent years. In other forms, handwriting recognition refers to the identification of written characters. Here character recognition is online character recognition, as well as character recognition offline.
The identification of written characters can be done using handwriting recognition. The problem can be seen in such a way as to identify the most appropriate character to which the given figure coincides. The recognition technique used for offline character recognition refers to where the final figure is given to us. Online character recognition systems are contrary to where data can be sampled while writing the character. Operating in offline mode gives the input the full picture character that we need to recognize. Recognition is usually in complexity that is related to the size of the language being considered. If the language contains more number of characters; The difficulty arises in identifying the case when the language contains fewer characters. They always have an effect on the handwriting recognition system. In this article we propose the use of column vectors to solve this problem. The basic idea of the column vector comes from the fact that it can be used as an excellent means of combining various styles of writing a character and giving way to new styles. Looking closely at the ability of the human mind to recognize handwriting, we find that humans are able to recognize the characters even though they might be seeing that style for the first time.
