On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Lexical Post-Processing Optimization for Handwritten Word Recognition
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Rapid on-line temporal sequence prediction by an adaptive agent
Proceedings of the fourth international joint conference on Autonomous agents and multiagent systems
OCR post-processing for low density languages
HLT '05 Proceedings of the conference on Human Language Technology and Empirical Methods in Natural Language Processing
A real-time hand tracker using variable-length Markov models of behaviour
Computer Vision and Image Understanding
Real-time 3-D human body tracking using learnt models of behaviour
Computer Vision and Image Understanding
Non-stationary policy learning in 2-player zero sum games
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 2
Learning atomic human actions using variable-length Markov models
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on human computing
International Conference on Multimodal Interfaces and the Workshop on Machine Learning for Multimodal Interaction
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We describe a linguistic postprocessor for character recognizers. The central module of our system is a trainable variable memory length Markov model (VLMM) that predicts the next character given a variable length window of past characters. The overall system is composed of several finite state automata, including the main VLMM and a proper noun VLMM. The best model reported in the literature (Brown et al., 1992) achieves 1.75 bits per character on the Brown corpus. On that same corpus, our model, trained on 10 times less data, reaches 2.19 bits per character and is 200 times smaller (/spl sime/160,000 parameters). The model was designed for handwriting recognition applications but could also be used for other OCR problems and speech recognition.