Segmentation of On-line Handwritten Japanese Text of Arbitrary Line Direction by a Neural Network for Improving Text Recognition

Authors:
Bilan Zhu;Masaki Nakagaw
Affiliations:
Tokyo University of Agriculture and Technology;Tokyo University of Agriculture and Technology
Venue:
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
Year:
2005

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Abstract

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.