Abstract
A stochastic model of stroke order variation is proposed and applied to the stroke-order free on-line Kanji character recognition. The proposed model is a hidden Markov model (HMM) with a special topology to represent all stroke order variations. A sequence of state transitions from the initial state to the final state of the model represents one stroke order and provides a probability of the stroke order. The distribution of the stroke order probability can be trained automatically by using an EM algorithm from a training set of on-line character patterns. Experimental results on large-scale test patterns showed that the proposed model could represent actual stroke order variations appropriately and improve recognition accuracy by penalizing incorrect stroke orders.
Original language | English |
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Title of host publication | ICDAR2009 - 10th International Conference on Document Analysis and Recognition |
Pages | 803-807 |
Number of pages | 5 |
DOIs | |
Publication status | Published - 2009 |
Event | ICDAR2009 - 10th International Conference on Document Analysis and Recognition - Barcelona, Spain Duration: Jul 26 2009 → Jul 29 2009 |
Other
Other | ICDAR2009 - 10th International Conference on Document Analysis and Recognition |
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Country | Spain |
City | Barcelona |
Period | 7/26/09 → 7/29/09 |
All Science Journal Classification (ASJC) codes
- Computer Vision and Pattern Recognition