Design of handwritten numeral matching and recognition system base on Bayes theory
Abstract: The off-line recognition of handwritten numeral is one of the challenging items in the field of pattern recognition.It will paly a important role in many respects such as automatic inputting for letter classification,check recognition,statistical returns and handwritten ID.However,as a result of the lack of strict supervison and the complex relation of position between numbers.The present system is designed base on Bayesian theory and is mainly used to automatic inputting and automatic clustering for off-line handwritten numeral sample.The main work is as follows:
1,Pretreatment of handwritten numeral sample,having achieved basical smoothness of picture and generating different strategy for binaryzation based on different papery substrate,such as using iterating the best threshold for dividing against the background of plain paper ,using two-tier threshold for dividing against the background of draft paper.
2,This paper reviewed and summarize the main ways over the years in which thining numeral,On the basis of the present system can realize entering for numeral function,one modified arithmetic used to thining sample is proposed.
3,This paper introduced some primary ways for refining feature and statistical nature.In connection with the present design,I adopt the method of dividing picture pixels.
4,In the period of identifying,author use a few kinds of arithmetics to achieve image clustering base on Bayesian theory.
The set of all test samples for this present system is eight piece of pictures enlarged recording information about numeral.the set includes set of standard print digits and the same category but different size numeral and handwritten numeral and set of geometrical shapes etc.The accuracy rate of all clustering algorithms is depend on the certain sample.
Keywords: handwritten digits, clustering, bayes, feature extraction, fuzzy set, center of clustering