1.  Field of the Invention
The present invention relates to a character extracting method conducted in preparation for character recognition, and a character extracting apparatus for conducting the same.  More particularly, the present invention relates to a character extracting method, which is used in, for example, a pre-process of character recognition for managing the quality of industrial products in the field of factory automation in an automated manner on a production line, and a character extracting apparatus for conducting the same.  For example, the above-mentioned pre-process is a process for cutting out a character(s) on a wafer on a character-by-character basis by a character recognition apparatus for the purpose of quality management of the wafers produced. 
2.  Description of the Related Art
According to the character recognition technique in the field of office automation, a clear image can be obtained from a document of interest.  Therefore, the character recognition technique in the office automation has been reached a substantially high-level recognition ratio. 
According to the character recognition technique in the field of factory automation, however, a back-ground-light level varies depending upon the operation environment.  Therefore, the background of an object of interest is less distinct, making the difference between a character(s) and the background unclear.  Accordingly, the character recognition would be difficult without conducting a number of pre-processes prior to a character recognition process. 
A general character recognition apparatus for the factory automation conducts a character recognition process by correctly cutting out a character region, i.e., a portion where a character is drawn, using the image processing technology.  Provided that the character recognition process for the factory automation is the same as that for the office automation, whether the character recognition succeeds or not is determined by the character extracting process which is conducted as a pre-process of the character recognition process.  When the character region can be correctly extracted, a character(s) can be recognized with a high recognition ratio, using a character recognition method similar to that used in the office automation. 
A "method using region information" and a "method using edge information" are becoming popular as methods for extracting (cutting) a character region from an image. 
One example of the "method using region information" is to set a threshold reflecting a local characteristic of an image I(i, j) (e.g., "Image Data Processing for Scientific Measurement" by Kawata et al.,  1994, published by CQ Inc.). 
The key to this threshold method is to properly select a threshold.  A threshold T is given by, for example, the following expression (1): ##EQU1##
where P: a region;
N: the number of pixels in the region P; PA1 I(i, j): a two-dimensional function representing an image; and PA1 (i, j): a pixel position in coordinates.  PA1 f: a two-dimensional function of x and y; and PA1 (x, y): a pixel position in coordinates.  PA1 .sigma.:  a spatial constant of the Gaussian function. 
According to the above expression (1), a region P centered around an pixel (i, j) is provided on a pixel-by-pixel basis, and a mean concentration value of each region P is set as a threshold T.
A Marr's zero-cross method ("Vision" by D. Marr, 1982, published by W. H. Freeman Inc.) is well known as a method using edge information.  According to the zero-cross method, a Laplacian operation is applied to an original image according to the following expression (2), and the point where the operation result changes from positive to negative (i.e., zero-cross) is extracted as an edge of a character: ##EQU2##
where .gradient..sup.2 : a Laplacian operator;
Alternatively, a method for first reducing the sharpness of the original image using a Gaussian function and then applying a Laplacian operation to the resultant image according to the following expression (3) is often used: EQU G(x, y)=1/(2.pi..sigma..sup.2)exp(-(x.sup.2 +y.sup.2)/(2.sigma..sup.2));.sigma.&gt;0(3)
where G(x, y): a two-dimensional Gaussian function; and
However, in the above-mentioned threshold method using region information, an importance level of the pixels in the region P is not considered on a pixel-by-pixel basis.  In short, every pixel in the region P is regarded as being of the same importance.  Moreover, every region P in the entire screen has the same size.  Therefore, such a threshold T is not preferable.  For such reasons as described above, a character(s) can not be precisely cut out by this threshold method.  Consequently, such a high recognition ratio as obtained by the character recognition apparatus for office automation can not be expected.