Modified Quadratic Discriminant Functions and the Application to Chinese Character Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
On speeding candidate selection in handprinted Chinese character recognition
Pattern Recognition
Precise Candidate Selection for Large Character Set Recognition by Confidence Evaluation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Accelerating Large Character Set Recognition using Pivots
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Collection of on-line handwritten Japanese character pattern databases and their analyses
International Journal on Document Analysis and Recognition
Improving the Structuring Search Space Method for Accelerating Large Set Character Recognition
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 02
Structuring Search Space for Accelerating Large Set Character Recognition
IEICE - Transactions on Information and Systems
A robust model for on-line handwritten japanese text recognition
International Journal on Document Analysis and Recognition - Special Issue DRR09
Research on Machine Recognition of Handprinted Characters
IEEE Transactions on Pattern Analysis and Machine Intelligence
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
ICDAR '11 Proceedings of the 2011 International Conference on Document Analysis and Recognition
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In this paper, a systematic method is described that constructs an efficient and a robust coarse classifier from a large number of basic recognizers obtained by different parameters of feature extraction, different discriminant methods or functions, etc. The architecture of the coarse classification is a sequential cascade of basic recognizers that reduces the candidates after each basic recognizer. A genetic algorithm determines the best cascade with the best speed and highest performance. The method was applied for on-line handwritten Chinese and Japanese character recognitions. We produced hundreds of basic recognizers with different classification costs and different classification accuracies by changing parameters of feature extraction and discriminant functions. From these basic recognizers, we obtained a rather simple two-stage cascade, resulting in the whole recognition time being reduced largely while maintaining classification and recognition rates.