@inproceedings{2007-IUPR-11Sep_1129,
author = {Faisal Shafait and Daniel Keysers and Thomas M. Breuel},
title = {Efficient Implementation of Local Adaptive Thresholding Techniques Using Integral Images},
booktitle = {Document Recognition and Retrieval XV},
year = {2008},
address = {San Jose, CA},
month = {Jan},
pdf = {2007-IUPR-11Sep_1129.pdf},
__utma = {43439421.91645987.1202213052.1202213052.1202213052.1},
__utmc = {43439421},
__utmz = {43439421.1202213052.1.1.utmccn=(direct)|utmcsr=(direct)|utmcmd=(none)},
abstract = {Adaptive binarization is an important first step in many doc- ument analysis and OCR processes. This paper describes a
fast adaptive binarization algorithm that yields the same quality of binarization as the Sauvola method [1], but runs in
time close to that of global thresholding methods (like Otsu's method [2]), independent of the window size. The
algorithm combines the statistical constraints of Sauvola's method with integral images [3]. Testing on the UW-1 dataset
demonstrates a 20-fold speedup compared to the original Sauvola algorithm.},
category = {IPET}
}