On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
IAM-OnDB - an On-Line English Sentence Database Acquired from Handwritten Text on a Whiteboard
ICDAR '05 Proceedings of the Eighth International Conference on Document Analysis and Recognition
ICPR '96 Proceedings of the 13th International Conference on Pattern Recognition - Volume 2
Combining On-Line and Off-Line Systems for Handwriting Recognition
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
Novel VQ with constraints on the quantization error distribution
ICME'09 Proceedings of the 2009 IEEE international conference on Multimedia and Expo
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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 %.