This paper proposes a method for recognizing characters in gray-scale images with graphical designs and noise. Our previous method for binary images is robust against graphical designs, however, it is easily affected by binarization conditions or scanning conditions. Therefore, we improve the method to be tolerant for such conditions by handling gray-scaled images directly. First, a projection profile for text-line extraction and a similarity value for character recognition are expanded for gray-scaled images. Next, learning method of adaptive threshold values against the degree of degradation and variations of intensity levels are introduced to suppress spurious candidates during recognition process. Experimental results for fifty Japanese newspaper headlines show that the proposed method achieves higher recognition rates than our previous method in the wide range of scanning brightness conditions.