An Integrated System for the Analysis and the Recognition of Characters in Ancient Documents
This paper describes an integrated system for processing and analyzing highly degraded ancient printed documents. For each page, the system reduces noise by wavelet-based filtering, extracts and segments the text lines into characters by a fast adaptive thresholding, and performs OCR by a feed-forward back-propagation multilayer neural network. The probability recognition is used as a discriminant parameter for determining the automatic activation of a feed-back process, leading back to a block for refining segmentation. This block acts only on the small portions of the text where the recognition was not trustable, and makes use of blind deconvolution and MRF-based segmentation techniques. The experimental results highlight the good performance of the whole system in the analysis of even strongly degraded texts.
KeywordsMarkov Random Fields Text Line Optical Character Recognition Gray Level Image Character Segmentation
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