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This paper proposes a new technique of segmentation and recognition of characters with a wide variety of image degradations and complex backgrounds in natural scenes. The key ideas are twofold. One is segmentation of character and background by local/adaptive binarization of one of Cyan/Magenta/Yellow (CMY) color planes with the maximum breadth of histogram. The other is affine-invariant grayscale character recognition using global affine transformation (GAT) correlation. In experiments, we use a total of 698 test images extracted from the public ICDAR 2003 robust OCR dataset containing images of single characters in natural scenes. In advance, we classify those images into seven groups according to the degree of image degradations and/or background complexity. On the other hand, we prepare a single-font set of 62 alphanumerics for templates. Experimental results show an average recognition rate of 70.3%, ranging from 95.5% for clear images to 24.3% for little-contrast images.