The fast evolution of scanning and computing technologies in recent years has led to the creation of large collections of scanned historical documents. It is almost always the case that these scanned documents suffer from some form of degradation. Large degradations make documents hard to read and substantially deteriorate the performance of automated document processing systems. Enhancement of degraded document images is normally performed assuming global degradation models. When the degradation is large, global degradation models do not perform well. In contrast, we propose to learn local degradation models and use them in enhancing degraded document images. Using a semi-automated enhancement system, we have labeled a subset of the Frieder diaries collection (The diaries of Rabbi Dr. Avraham Abba Frieder. http://ir.iit.edu/collections/). This labeled subset was then used to train classifiers based on lookup tables in conjunction with the approximated nearest neighbor algorithm. The resulting algorithm is highly efficient and effective. Experimental evaluation results are provided using the Frieder diaries collection (The diaries of Rabbi Dr. Avraham Abba Frieder. http://ir.iit.edu/collections/). © Springer-Verlag 2009.
T. Obafemi-Ajayi et al., "Historical Document Enhancement Using LUT Classification," International Journal on Document Analysis and Recognition, vol. 13, no. 1, pp. 1 - 17, Springer, Mar 2010.
The definitive version is available at https://doi.org/10.1007/s10032-009-0099-3
Electrical and Computer Engineering
International Standard Serial Number (ISSN)
Article - Journal
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01 Mar 2010