1. Field of the Invention
The present invention relates to a multilevel image processing technology.
2. Description of the Related Art
Recently, a slip recognition technology using a non-contact type image input device, such as an over-head reader (OHR), has become a key for winning financial OCR (optical character reader) business.
An OHR is a stand type image input device provided with a line or area CCD (charge couple diode) as an image element, as shown in FIG. 1A. Compared with a conventional contact type image input device, such as an image scanner, etc., by using an OHR, entry to a slip can be made possible while a user is inputting an image and an image can be inputted while viewing a list of slips. Therefore, work can be performed comfortably.
Compared with an image obtained by a scanner (hereinafter called “a scanner image”), an image obtained by the OHR (hereinafter called “an OHR image”) suffers from degradation, such as that caused by uneven gradation, reflection, image distortion, etc.
FIG. 1B shows an example of a scanner image, and FIG. 1C shows an example of an OHR image. The OHR image shown in FIG. 1C does not include the reflections of desks, walls, human beings, etc., and it is of fairly good quality for an OHR image. However, compared with the scanner image shown in FIG. 1B, the OHR image has a large degree of uneven gradation and character lines that are more blurred. If an OHR is used, there is also a case where an OHR image with reflections, as shown in FIG. 1D must be handled, since there is a possibility that the reflections of desks, walls, human beings, etc., may be included in an image. The OHR image shown in FIG. 1D is blurred from the right to the left of the image due to reflections and as if the image were gradated. If an OHR is used, the development of a base technology for overcoming such image degradation becomes a major problem.
In order to configure a high-precision binarizing system for an OHR image, it is necessary to obtain a character outline which is resistant against reflection and uneven gradation. Therefore, constant threshold value binarization is not sufficient and Niblack's local binarization (see Reference 1: IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 17, No. 12, p. 1191–1202, 1995), etc., must be introduced.
Niblack's local binarization is a system of performing binarization for each pixel assuming that the threshold value of each pixel T=E+Kσ (E: average gray level of pixels in the vicinity of a target pixel, σ: standard deviation of gray level of the pixels in the vicinity of the target pixel, K: prescribed constant). A rectangular area of N×N (N is a constant) with the target pixel located at the center is used as the vicinity of the target pixel.
However, if a conventional system, such as Niblack's binarization, etc., is used without modification, a black-white flickering noise occurs since all pixels in the vicinity of the pixel have an even gray level inside a background or a thick line.
FIG. 1F shows a binary image obtained by performing Niblack's local binarization (N=7, K=−0.1) for the OHR image shown in FIG. 1E. According to the conventional binarization system, a black-white flickering noise occurs, as shown in FIG. 1F. Such a noise in which “black” and “white” are flickering must be eliminated.
Although a method of eliminating the black-white flickering noise which occurs in the case where Niblack's local binarization is applied is described in the previous reference, the method is complex, the process requires many steps and the calculation cost is high, which is a problem.