Tree-Based State Clustering Using Self-Organizing Principles for Large Vocabulary On-Line Handwriting Recognition

Authors:
Affiliations:
Venue:
ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2
Year:
1998

Citing 4
Cited 1

Improved On-Line Handwriting Recognition Using Context Dependent Hidden Markov Models

ICDAR '97 Proceedings of the 4th International Conference on Document Analysis and Recognition
An Investigation of the Use of Trigraphs for Large Vocabulary Cursive Handwriting Recognition

ICASSP '97 Proceedings of the 1997 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '97) -Volume 4 - Volume 4
Tree-based state tying for high accuracy acoustic modelling

HLT '94 Proceedings of the workshop on Human Language Technology
On-line cursive handwriting recognition using hidden Markov models and statistical grammars

HLT '94 Proceedings of the workshop on Human Language Technology

State Sharing in a Hybrid Neuro-Markovian On-Line Handwriting Recognition System through a Simple Hierarchical Clustering Algorithm

ICMI '02 Proceedings of the 4th IEEE International Conference on Multimodal Interfaces

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Abstract

The introduction of trigraphs offers a powerful method for the accuracy enhancement of handwriting modeling. A trigraph is a Hidden Markov Model (HMM) for a special character with defined adjacent characters. Especially in large vocabulary systems, as they are investigated here, the number of unseen trigraphs for which no training samples are available, exceeds the number of seen trigraphs by far. This paper presents a novel approach, which allows a synthesis of unseen trigraphs from seen trigraphs. With the method proposed here, a mean relative ermr reduction of 42% was obtained on a writer dependent system, resulting in an overall word recognition rate of 94.1 %.