DIRECT GRAY-SCALE EXTRACTION OF FEATURES FOR CHARACTER-RECOGNITION

Most current OCR systems perform a binarization of the input before attempting recognition. Though this might appear to be a rather innocuous operation since the input is supposed to be binary to start with, a significant amount of information is lost during the process. To avoid this problem, other systems perform recognition without binarization by using techniques related to matched filters. However, this approach is limited to applications where there is only a limited variation on the form of the characters (such as in the case of a single font). In this paper, a new approach is explored; this appraoch eliminates binarization by extracting features directly from gray-scale images. In this method, a digitized gray-scale image is treated as a noisy sampling of the underlying continuous surface, and desired features are obtained by extracting and assembling topographic characteristics of this surface. The advantages and effectiveness of the new approach are both shown theoretically and demonstrated through preliminary experiments of the proposed method.