Recognition-Based Method for Handwritten Numerical Strings Segmentation Trained with Negative Data
Zhang Jian · Journal of Wuhan University · 2007
This paper used the recognition-based Method to solve the segmentation problem of handwritten numerical strings.In the segmentation process,to get classifier with better performance,negative data must be the necessary trained samples.The experiment results show that this method with negative data can get better refuse rate,which reaches 19 percent.During to the increase of refuse rate,the recognition accuracy also increases,validate that the outputs of the classifier trained with negative data is reliable.