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Multilevel correction of OCR of medical texts

Piasecki M.

Journal of Medical Informatics & Technologies

2007

Vol. 11

263--273

In the paper the idea of the multilevel correction of the results handwriting OCR of medical texts is investigated. The correction is performed according to different levels of linguistic knowledge. Three types of models, namely: the n-gram Language Models of word form and base form sequences, the morpho-syntactic model based on a tagger and the model of correction by parsing are presented and their results are compared. The parsing model is based on the combination of a deterministic Czech parser adapted for Polish and the Structured Language Model based on lexicalised, binary parsing trees produced in the left-to-right manner. Contrary to the initial expectations, the best result of correction from 82% of the word level classifier to 92.98% of the overall accuracy was achieved with the help of a n-gram Language Models. The more rich description of language expressions in a model, the worse results were obtained. This result is in large extent caused by the specific characteristics of the processed medical documents.