The State of the Art in Online Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Fundamentals of speech recognition
Fundamentals of speech recognition
A Fast Statistical Mixture Algorithm for On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
HMM Based On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Fuzzy-Syntactic Approach to Allograph Modeling for Cursive Script Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
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This paper presents a hidden Markov model (HMM) based approach to on-line handwritten digit recognition using stroke sequences. In this approach, a character instance is represented by a sequence of symbolic strokes, and the representation is obtained by component segmentation and stroke classification. The component segmentation is based on the delta lognormal model of handwriting generation. The symbolic strokes are used for HMM multiple observation training or recognition. A training and recognition experiment has been conducted using the above techniques.