Investigation of the use of trigraphs for large vocabulary cursive handwriting recognition

Andreas Kosmala, Joerg Rottland, Gerhard Rigoll

Research output: Contribution to journalConference articlepeer-review

7 Scopus citations

Abstract

This paper presents an extensive investigation of the use of trigraphs for on-line cursive handwriting recognition based on Hidden Markov Models (HMMs). Trigraphs are context dependent HMMs representing a single written character in its left and right context, similar to triphones in speech recognition. Looking at the great success of triphones in continuous speech recognition, it was always a challenging and open question, if the introduction of trigraphs could lead to substantially improved handwriting recognition systems. The results of this investigation are indeed extremely encouraging: The introduction of suitable trigraphs led to a 50% relative error reduction for a writer dependent 1000 word handwriting recognition system, and to a 35% relative error reduction for the same system with an extended 30000 word vocabulary for cursive handwriting recognition.

Original languageEnglish
Pages (from-to)3373-3376
Number of pages4
JournalICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
Volume4
StatePublished - 1997
Externally publishedYes
EventProceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP. Part 1 (of 5) - Munich, Ger
Duration: 21 Apr 199724 Apr 1997

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