Information Services banner Edinburgh Research Archive The University of Edinburgh crest

Edinburgh Research Archive >
Centre for Speech Technology Research >
CSTR publications >

Please use this identifier to cite or link to this item: http://hdl.handle.net/1842/963

This item has been viewed 10 times in the last year. View Statistics

Files in This Item:

File Description SizeFormat
Takuno.pdf313.62 kBAdobe PDFView/Open
Title: Context-dependent substroke model for HMM-based on-line handwriting recognition
Authors: Tokuno, Junko
Inami, Nobuhito
Matsuda, Shigeki
Nakai, Mitsuru
Shimodaira, Hiroshi
Sagayama, Shigeki
Issue Date: Aug-2002
Citation: In Frontiers in Handwriting Recognition, 2002. Proceedings. Eighth International Workshop on, 6-8 Aug. 2002 Page(s):78 - 83
Publisher: IEEE Computer Society
Abstract: Describes context-dependent substroke hidden Markov models (HMMs)for on-line handwritten recognition of cursive Kanji and Hiragana characters. In order to tackle this problem, we have proposed the substroke HMM approach where a modeling unit "substroke" that is much smaller than a whole character is employed and each character is modeled as a concatenation of only 25 kinds of substroke HMMs. One of the drawbacks of this approach is that the recognition accuracy deteriorates in the case of scribbled characters, and characters where the shape of the substrokes varies a lot. We show that the context-dependent substroke modeling which depends on how the substroke connects to the adjacent substrokes is effective for achieving robust recognition of low quality characters, The successive state splitting algorithm which was mainly developed for speech recognition is employed to construct the context dependent substroke HMMs. Experimental results show that the correct recognition rate improved from 88% to 92% for cursive Kanji handwriting and from 90% to 98% for Hiragana handwriting.
URI: http://ieeexplore.ieee.org/servlet/opac?punumber=8011
http://hdl.handle.net/1842/963
Appears in Collections:CSTR publications

Items in ERA are protected by copyright, with all rights reserved, unless otherwise indicated.

 

Valid XHTML 1.0! Unless explicitly stated otherwise, all material is copyright © The University of Edinburgh 2013, and/or the original authors. Privacy and Cookies Policy