We present a novel adaptive method to improve the binarization quality of degraded word color images. The objective of this work is to solve a nonlinear problem concerning the binarization quality, that is, to achieve edge enhancement and noise reduction in images. The digitized data used in this work were extracted automatically from real world photos. The motion of objects with reference to static camera and bad environmental conditions provoked serious quality problems on those images. Conventional methods, such as the nonlinear adaptive filter method proposed by Mo, or Otsu's method cannot produce satisfactory binarization results for those types of degraded images. Among other problems, we note mainly that contrast (between shapes and backgrounds) varies greatly within every degraded image due to non-uniform illumination. The proposed method is based on the automatic extraction of background information, such as luminance distribution to adaptively control the intensity levels, that is, without the need for any manual fine-tuning of parameters. Consequently, the new method can avoid noise or inappropriate shapes in the output binary images. Otsu's method is also applied to automatic threshold selection for classifying the pixels into background and shape pixels. To demonstrate the efficiency and the feasibility of the new adaptive method, we present results obtained by the binarization system. The results were satisfactory as we expected, and we have concluded that they can be used successfully as data in further processing such as segmentation or extraction of characters. Furthermore, the method helps to increase the eventual efficiency of a recognition system for poor-quality word images, such as number plate photos with non-uniform illumination and low contrast.