Abstract
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.
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© 2002 Springer-Verlag Berlin Heidelberg
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Vezzosi, S., Bedini, L., Tonazzini, A. (2002). An Integrated System for the Analysis and the Recognition of Characters in Ancient Documents. In: Lopresti, D., Hu, J., Kashi, R. (eds) Document Analysis Systems V. DAS 2002. Lecture Notes in Computer Science, vol 2423. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45869-7_5
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DOI: https://doi.org/10.1007/3-540-45869-7_5
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