DocumentCode :
3019659
Title :
Segmentation of on-line handwritten Japanese text of arbitrary line direction by a neural network for improving text recognition
Author :
Zhu, Bilan ; Nakagawa, Masaki
Author_Institution :
Tokyo Univ. of Agric. & Technol., Japan
fYear :
2005
fDate :
29 Aug.-1 Sept. 2005
Firstpage :
157
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.
Keywords :
feature extraction; handwritten character recognition; image segmentation; neural nets; text analysis; Fisher linear discriminants; neural network; online handwritten Japanese text; spatial feature extraction; text recognition; text segmentation; Agriculture; Character generation; Character recognition; Feature extraction; Handwriting recognition; Multidimensional systems; Neural networks; Spatial databases; Text recognition; Writing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Document Analysis and Recognition, 2005. Proceedings. Eighth International Conference on
ISSN :
1520-5263
Print_ISBN :
0-7695-2420-6
Type :
conf
DOI :
10.1109/ICDAR.2005.211
Filename :
1575529
Link To Document :
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