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Segmentation of On-line Handwritten Japanese Text of Arbitrary Line Direction by a Neural Network for Improving Text Recognition
Seoul, Korea August 31-September 01
DOI Bookmark: http://doi.ieeecomputersociety.org/10.1109/ICDAR.2005.211Eighth International Conference on Do ...
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Bilan Zhu, Masaki Nakagaw, "Segmentation of On-line Handwritten Japanese Text of Arbitrary Line Direction by a Neural Network for Improving Text Recognition," Document Analysis and Recognition, International Conference on, pp. 157-161, Eighth International Conference on Document Analysis and Recognition (ICDAR'05), 2005.
 
 
BibTex
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@article{ 10.1109/ICDAR.2005.211,
author = {Bilan Zhu and Masaki Nakagaw},
title = {Segmentation of On-line Handwritten Japanese Text of Arbitrary Line Direction by a Neural Network for Improving Text Recognition},
journal ={Document Analysis and Recognition, International Conference on},
volume = {0},
year = {2005},
issn = {1520-5263},
pages = {157-161},
doi = {http://doi.ieeecomputersociety.org/10.1109/ICDAR.2005.211},
publisher = {IEEE Computer Society},
address = {Los Alamitos, CA, USA},
}
 
 
RefWorks Procite/RefMan/Endnote
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TY - CONF
JO - Document Analysis and Recognition, International Conference on
TI - Segmentation of On-line Handwritten Japanese Text of Arbitrary Line Direction by a Neural Network for Improving Text Recognition
SN - 1520-5263
SP157
EP161
A1 - Bilan Zhu,
A1 - Masaki Nakagaw,
PY - 2005
KW - null
VL - 0
JA - Document Analysis and Recognition, International Conference on
ER -
 
Bilan Zhu, Tokyo University of Agriculture and Technology
Masaki Nakagaw, Tokyo University of Agriculture and Technology
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.
Citation:
Bilan Zhu, Masaki Nakagaw, "Segmentation of On-line Handwritten Japanese Text of Arbitrary Line Direction by a Neural Network for Improving Text Recognition," icdar, pp.157-161, Eighth International Conference on Document Analysis and Recognition (ICDAR'05), 2005
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