Handwriting Recognition Using Position Sensitive Letter N-Gram Matching
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Offline Recognition of Unconstrained Handwritten Texts Using HMMs and Statistical Language Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Combining Character Classifiers Using Member Classifiers Assessment
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Comparison of Feature Reduction Methods in the Text Recognition Task
ICAISC '08 Proceedings of the 9th international conference on Artificial Intelligence and Soft Computing
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This paper presents a concept of bidirectional probabilistic character language model and its application to handwriting recognition. Character language model describes probability distribution of adjacent character combinations in words. Bidirectional model applies word analysis from left to right and in reversed order, i.e. it uses conditional probabilities of character succession and character precedence. Character model is used for HMM creation, which is applied as a soft word classifier. Two HMMs are created for left-to-right and right-to-left analysis. Final word classification is obtained as a combination of unidirectional recognitions. Experiments carried out with medical texts recognition revealed the superiority of combined classifier over its components.