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

Joachim Schenk, Stefan Schwärzler, Günther Ruske, Gerhard Rigoll

Publikation: Beitrag in Buch/Bericht/KonferenzbandKonferenzbeitragBegutachtung

8 Zitate (Scopus)

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 %.

OriginalspracheEnglisch
TitelPattern Recognition - 30th DAGM Symposium, Proceedings
Seiten234-243
Seitenumfang10
DOIs
PublikationsstatusVeröffentlicht - 2008
Veranstaltung30th DAGM Symposium on Pattern Recognition - Munich, Deutschland
Dauer: 10 Juni 200813 Juni 2008

Publikationsreihe

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Band5096 LNCS
ISSN (Print)0302-9743
ISSN (elektronisch)1611-3349

Konferenz

Konferenz30th DAGM Symposium on Pattern Recognition
Land/GebietDeutschland
OrtMunich
Zeitraum10/06/0813/06/08

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