C4.5: programs for machine learning
C4.5: programs for machine learning
Combining Character Classifiers Using Member Classifiers Assessment
ISDA '05 Proceedings of the 5th International Conference on Intelligent Systems Design and Applications
Hand-Written and automatically extracted rules for polish tagger
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Effective architecture of the polish tagger
TSD'06 Proceedings of the 9th international conference on Text, Speech and Dialogue
Correction of medical handwriting OCR based on semantic similarity
IDEAL'07 Proceedings of the 8th international conference on Intelligent data engineering and automated learning
Language modelling for the needs of OCR of medical texts
ISBMDA'06 Proceedings of the 7th international conference on Biological and Medical Data Analysis
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In the paper, three-level hand-writing recognition using language syntactic properties on the upper level is presented. Isolated characters are recognized on the lowest level. The character classification from the lowest level is used in words recognition. Words are recognized using a combined classifier based on possibly incomplete unigram lexicon. Word classifier builds a rank of the most likely words. Ranks created for subsequent words are input to the syntactic classifier, which recognizes the whole sentences. Here the local syntactic constraints are used to build a syntactically consistent sentence. The method has been applied to recognition of hand-written medical texts describing fixed aspects of patient treatment. Due to narrow area of topics explained in the texts and peculiarity of style characteristic for physicians writing texts, the syntax of expected sentences is relatively simple, what makes the problem of checking the syntactic consistency simpler.