Research Of Handwritten Digit Recognition Based On Structural And Statistical Method

Off-line handwritten digit recognition is a problem not solved perfect so far, so it is very challenging. There are so many potential applications such as bank check processing, postal mail sorting, automatic reading of tax forms and various handwritten and printed materials that a lot of researcher persistent to research it.Previous researches and tendency of handwritten digit recognition are introduced. Pre-processing such as image binaryzation, image segmentation, normalization and thinning are discussed. Feature combination do a good contribution to recognition rate but classifier combination not. I research a statistical method use Support vector classifier which has excellent classified ability to classify combined features of gradient and curvature and wavelet features. The method of extract features and using tool of support vector classifier are illustrate in detail.A structural method is researched. More specifically, based on three kinds of feature points, first we extract ten kinds of primitive segments for each image. And then, a tree-like classifier based on the extracted feature points and primitives is applied to classify the numerals. This method needn't a study procedure and run fast. Finally, to get a good reliability rate, using the two methods to recognize a batch of handwritten digit samples at the same time to get two recognition results and get the final result if two digits are same.The result of experiments shows that our methods are simple and convenient, at the same time, the error rate can accept. The combined recognition even lost some recognition rate but obtain a high reliability rate.