The historical documents are valuable cultural heritages and
sources for the study of history, social aspect and life at that time. The
digitalization of historical documents aims to provide instant access to
the archives for the researchers and the public, who had been endowed
with limited chance due to maintenance reasons. However, most of these
documents are not only written by hand in ancient Chinese characters,
but also have complex page layouts. As a result, it is not easy to utilize
conventional OCR(optical character recognition) system about historical
documents even if OCR has received the most attention for several years
as a key module in digitalization. We have been developing OCR-based
digitalization system of historical documents for years. In this paper,
we propose dedicated segmentation and rejection methods for OCR of
Korean historical documents. Proposed recognition-based segmentation
method uses geometric feature and context information with Viterbi
algorithm. Rejection method uses Mahalanobis distance and posterior
probability for solving out-of-class problem, especially. Some promising
experimental results are reported.