In OCR systems the character segmentation algorithm may generate mis-segmented blocks. Feedback information from character classifier is indispensable to achieve higher character segmentation accuracy. In this paper a novel rejection algorithm is proposed to identify these mis-segmented characters more accurately. First, based on confidence evaluation of distance-based classifiers, the usual generalized confidence mapping function is modified to fit this specific purpose. Second, a novel adaptive thresholding rejection rule is proposed, which is more accurate and flexible. Experiments on Chinese, Japanese and Korean document recognition showed that new rejection algorithm evidently improved the system performance, especially for low-quality printed document recognition.