Optical Character Recognition (OCR) from document photos taken by cell phones is a challenging task. Most OCR methods require prior binarization of the image, which can be difficult to achieve when documents are captured with various mobile devices in unknown lighting conditions. For example, shadows cast by the camera or the camera holder on a hard copy can jeopardize the binarization process and hinder the next OCR step. In the case of highly uneven illumination, binarization methods using global thresholding simply fail, and state-of-the-art adaptive algorithms often deliver unsatisfactory results. In this paper, we present a new binarization algorithm using two complementary local adaptive passes and taking advantage of the color components to improve results over current image binarization methods. The proposed approach gave remarkable results at the DocEng'22 competition on the binarization of photographed documents.