Segmentation of on-line handwritten Japanese text of arbitrary line direction by a neural network for improving text recognition

This paper describes a segmentation method of online handwritten Japanese text of arbitrary line direction by a neural network to improve text recognition performance. This method extracts multidimensional features from strokes of handwritten text and input them into a neural network to preliminarily determine segmentation points. Then, it modifies segmentation candidates using some spatial features. We compare the method with the previous method and that by Fisher´s linear discriminant using the database HANDS-Kondate_t_bf-2001-11. This paper also shows how to generate character segmentation candidates in order to achieve high discrimination rate by investigating the relationship between recall, precision and the f measure.