Novel VQ designs for discrete HMM on-line handwritten whiteboard note recognition

Joachim Schenk, Stefan Schwärzler, Günther Ruske, Gerhard Rigoll

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

8 Scopus citations

Abstract

In this work we propose two novel vector quantization (VQ) designs for discrete HMM-based on-line handwriting recognition of whiteboard notes. Both VQ designs represent the binary pressure information without any loss. The new designs are necessary because standard k-means VQ systems cannot quantize this binary feature adequately, as is shown in this paper. Our experiments show that the new systems provide a relative improvement of r = 1.8 % in recognition accuracy on a character- and r = 3.3 % on a word-level benchmark compared to a standard k-means VQ system. Additionally, our system is compared and proven to be competitive to a state-of-the-art continuous HMM-based system yielding a slight relative improvement of r = 0.6 %.

Original languageEnglish
Title of host publicationPattern Recognition - 30th DAGM Symposium, Proceedings
Pages234-243
Number of pages10
DOIs
StatePublished - 2008
Event30th DAGM Symposium on Pattern Recognition - Munich, Germany
Duration: 10 Jun 200813 Jun 2008

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume5096 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference30th DAGM Symposium on Pattern Recognition
Country/TerritoryGermany
CityMunich
Period10/06/0813/06/08

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