A Fast Statistical Mixture Algorithm for On-Line Handwriting Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Continuous HMM applied to quantization of on-line Korean character spaces
Pattern Recognition Letters
Substroke Approach to HMM-Based On-line Kanji Handwriting Recognition
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Online Recognition of Chinese Characters: The State-of-the-Art
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Collection of on-line handwritten Japanese character pattern databases and their analyses
International Journal on Document Analysis and Recognition
Online slant signature algorithm analysis
WSEAS Transactions on Computers
Baseline extraction algorithm for online signature recognition
WSEAS TRANSACTIONS on SYSTEMS
Slant algorithm for online signature recognition
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Online baseline identification algorithm using vector rules
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Recent results of online Japanese handwriting recognition and its applications
SACH'06 Proceedings of the 2006 conference on Arabic and Chinese handwriting recognition
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An on-line handwritten character recognition technique based on a new HMM is proposed. In the proposed HMM, not only pen-direction feature but also pen-coordinate feature are separately utilized for describing the shape variation of on-line characters accurately. Specijcally speaking, the proposed HMM outputs a pen-coordinate feature at each inter-state transition and outputs a pen-direction feature at each intra-state transition, i.e., self-transition. Thus, each state of the proposed HMM can specify the starting position and the direction of a line segment by its incoming inter-state transition and intra-state transition, respectively. The results of recognition experiments on 10-stroke Chinese characters show that the proposed HMM outperforms the conventional HMM which does not use the pen-coordinate feature because of its non-stationarity.