Character extracting method and its apparatus

A character extracting method includes the steps of: obtaining an image of a specific region; applying a Laplacian operation to the obtained image; binarizing an image resulting from the Laplacian operation; and separating and cutting a character on a character-by-character basis based on the binarized image.

BACKGROUND OF THE INVENTION
 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;
 I(i, j): a two-dimensional function representing an image; and
 (i, j): a pixel position in coordinates.
 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;
 f: a two-dimensional function of x and y; and
 (x, y): a pixel position in coordinates.
 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
 .sigma.: a spatial constant of the Gaussian function.
 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.
 SUMMARY OF THE INVENTION
 According to one aspect of the present invention, a character extracting
 method includes the steps of: obtaining an image of a specific region;
 applying a Laplacian operation to the obtained image; binarizing the image
 resulting from the Laplacian operation; and separating and cutting a
 character on a character-by-character basis based on the binarized image.
 In one example, in the binarizing step, in a case where a portion where a
 character to be cut out is drawn has a convex property, I.sub.b [i, j] is
 a first value when .gradient..sup.2 I.sub.0 [i, j].gtoreq.0, and I.sub.b
 [i, j] is a second value when .gradient..sup.2 I.sub.0 [i, j]&lt;0; and in a
 case where the portion where the character to be cut out has a concave
 property, I.sub.b [i, j] is the first value when .gradient..sup.2 I.sub.0
 [i, j].ltoreq.0, and I.sub.b [i, j] is the second value when
 .gradient..sup.2 I.sub.0 [i, j]&gt;0, where .gradient..sup.2 represents a
 Laplacian operator; [i, j] represents a pixel position in the image;
 I.sub.0 [i, j] represents a pixel value corresponding to the pixel
 position [i, j]; and I.sub.b [i, j] represents a binarized pixel-value
 corresponding to a pixel value I.sub.0 [i, j].
 In one example, the first value corresponds to a level 0, and the second
 value corresponds to a level 255.
 In one example, the convex property indicates such a property that the
 portion where the character to be cut out is drawn becomes brighter toward
 the center thereof, and the concave property indicates such a property
 that the portion where the character to be cut out is drawn becomes darker
 toward the center thereof.
 In one example, a character extracting method further includes, after the
 binarizing step, the step of: filtering noise from the binarized image.
 In one example, the step of filtering the noise from the binarized image
 uses an arbitrary pixel and a pixel adjacent to the arbitrary pixel in the
 binarized image to filter the noise from the arbitrary pixel.
 In one example, the step of filtering the noise from the binarized image
 filters the noise by applying a minimum filter to a region having the
 arbitrary pixel and the adjacent pixel.
 In one example, the step of filtering the noise from the binarized image
 filters the noise by applying an AND operation to a region having the
 arbitrary pixel and the adjacent pixel.
 In one example, the step of filtering the noise from the binarized image
 filters the noise by applying a mean-value operation to a region having
 the arbitrary pixel and the adjacent pixel.
 In one example, a character extracting method further includes, after the
 step of filtering the noise from the binarized image, the step of
 conducting an expansion process and/or a contraction process in order to
 shape a deformed character and/or to fill a hole which is present in the
 portion where the character to be cut out is drawn.
 In one example, a character extracting method further includes, prior to
 the step of conducting the expansion process and/or the contraction
 process, the step of determining an outer frame of the character to be cut
 out.
 In one example, a character extracting method further includes the step of
 calculating a logic product of an image resulting from the step of
 conducting the expansion process and/or the contraction process and an
 image resulting from the step of filtering the noise from the binarized
 image.
 According to another aspect of the present invention, a character
 extracting apparatus includes: an imaging section for obtaining an image
 of a specific region; a Laplacian operation section for applying a
 Laplacian operation to the obtained image; a binarizing section for
 binarizing the image resulting from the Laplacian operation; and a cutting
 section for separating and cutting a character on a character-by-character
 basis based on the binarized image.
 In one example, the binarizing section binarizes the image resulting from
 the Laplacian operation as follows: in a case where a portion where a
 character to be cut out is drawn has a convex property, I.sub.b [i, j] is
 a first value when .gradient..sup.2 I.sub.0 [i, j].gtoreq.0, and I.sub.b
 [i, j] is a second value when .gradient..sup.2 I.sub.0 [i, j]&lt;0; and in a
 case where the portion where the character to be cut out has a concave
 property, I.sub.b [i, j] is the first value when .gradient..sup.2 I.sub.0
 [i, j].ltoreq.0, and I.sub.b [i, j] is the second value when
 .gradient..sup.2 I.sub.0 [i, j]&gt;0, where .gradient..sup.2 represents a
 Laplacian operator; [i, j] represents a pixel position in the image;
 I.sub.0 [i, j] represents a pixel value corresponding to the pixel
 position [i, j]; and I.sub.b [i, j] represents a binarized pixel-value
 corresponding to a pixel value I.sub.0 [i, j].
 In one example, the first value corresponds to a level 0, and the second
 value corresponds to a level 255.
 In one example, the convex property indicates such a property that the
 portion where the character to be cut out is drawn becomes brighter toward
 the center thereof, and the concave property indicates such a property
 that the portion where the character to be cut out is drawn becomes darker
 toward the center thereof.
 In one example, a character extracting apparatus further includes a filter
 for filtering noise from the binarized image.
 In one example, the filter uses an arbitrary pixel and a pixel adjacent to
 the arbitrary pixel in the binarized image to filter the noise from the
 arbitrary pixel.
 In one example, the filter filters the noise by applying a minimum filter
 to a region having the arbitrary pixel and the adjacent pixel.
 In one example, the filter filters the noise by applying an AND operation
 to a region having the arbitrary pixel and the adjacent pixel.
 In one example, the filter filters the noise by applying a mean-value
 operation to a region having the arbitrary pixel and the adjacent pixel.
 In one example a character extracting apparatus further includes an image
 processing section for calculating a logic product of an image provided by
 an expansion process and/or a contraction process and an image output from
 the filter.
 Thus, the invention described herein makes possible the advantages of (1)
 providing a character extracting method capable of substantially correctly
 cutting out a character region including a character(s) which is likely to
 contain a great amount of noise in the field of, for example, factory
 automation, and capable of ultimately achieving such a high recognition
 ratio as obtained by the character recognition process for office
 automation, and (2) a character segmentation apparatus for conducting the
 same.
 These and other advantages of the present invention will become apparent to
 those skilled in the art upon reading and understanding the following
 detailed description with reference to the accompanying figures.

DESCRIPTION OF THE PREFERRED EMBODIMENTS
 Hereinafter, the present invention will be described by way of illustrative
 examples with reference to the accompanying drawings. The same reference
 numerals designate the same components.
 The present invention will now be described with reference to FIGS. 1 to
 10.
 FIG. 1 is a graph showing a gray level of an image including characters
 with respect to a one-dimensional pixel position. In FIG. 1, the abscissa
 indicates a one-dimensional pixel position, and the ordinate indicates a
 gray level of a pixel. Each of two large convex-shaped portions 101 and
 102 represents a portion where a character(s) to be extracted is drawn
 (hereinafter, this position is referred to as a character-drawn portion)
 and the other portions represent a background. It is herein assumed that
 the character(s) is white and the background is black. It should be noted
 that this character image of FIG. 1, which represents a character of the
 factory automation, has a digital gray-level image of 256 gradation
 levels. This character image is the darkest where a gray level (i.e., a
 pixel value) is zero, and is the brightest where a gray level is 255.
 FIG. 2A is an enlarged graph showing only the character-drawn portion 102
 of FIG. 1. As shown in FIG. 2A, one of the features of a character of the
 factory automation is that the character becomes brighter toward the
 center of the character-drawn portion. That is, the character has a
 convex-shaped gray-level profile (hereinafter, such a feature of the
 character is referred to as a convex property). The gray level of the
 character-drawn portion 102 reaches a peak at the center of the
 character-drawn portion 102, and gradually decreases away from the center
 toward the right and the left.
 It should be noted that a character of the factory automation may have a
 property opposite to the convex property. More specifically, a character
 image may become darker toward the center of a character-drawn portion
 (hereinafter, such a property is referred to as a concave property).
 Regarding the gray level; the character-drawn portion in an image of the
 factory automation has a regular profile of the concave or convex
 property. However, a background (the other portions) has a mixed profile
 of the concave and convex properties.
 FIG. 2B shows a slope of the gray level of the character-drawn portion 102.
 More specifically, FIG. 2B shows the gray level of the character-drawn
 portion 102 differentiated with a position of the character-drawn portion
 102. FIG. 2C shows the result of applying a Laplacian operation to the
 character-drawn portion 102 (see the following expression (4)) in order to
 use a zero-cross method. Each of the intersections X.sub.1 and X.sub.2 of
 the straight line representing a gray level of zero and the curve
 representing the Laplacian operation result corresponds to an edge region
 of the character-drawn portion:
 ##EQU3##
 where I(i, j): a two-dimensional function representing an image; and
 (i, j): a pixel position in coordinates.
 FIG. 2D is a graph showing the result of binarizing the Laplacian operation
 result. According to this binarizing process, the Laplacian operation
 result is regarded as 255 when the Laplacian operation result is negative.
 The Laplacian operation result is regarded as zero when the Laplacian
 operation result is positive or zero.
 The character region including the character-drawn portion is extracted by
 the above-mentioned binarizing process. This binarizing process is given
 by the following expression (5):
 ##EQU4##
 where (i, j): a pixel position;
 I.sub.0 : an original image; and
 I.sub.b : a binarized image.
 It should be noted that in the case of an image having a concave property,
 the binarizing process given by the following expression (6) is conducted:
 ##EQU5##
 where (i, j): a pixel position;
 I.sub.0 : an original image; and
 I.sub.b : a binarized image.
 By extracting a character region by using an inner region of a
 character-drawn portion, a phenomenon that the character region overlaps
 with the other unnecessary regions will be suppressed. This is because a
 character image having a convex property becomes brighter toward the
 center of the character-drawn portion 102, whereby the character region
 including a character-drawn portion can be easily distinguished from the
 other unnecessary regions.
 According to the above-mentioned method, the character-drawn portion can be
 extracted. Since the Laplacian operation is directly applied to the
 original image, a high-frequency noise region having a convex property
 remains in the binarized image. However, the size of the remaining
 high-frequency noise region in the binarized image is smaller than that of
 the character region.
 According to the present example, a minimum filter with a size of 2.times.2
 pixels, for example, is applied to the binarized image in order to remove
 the noise region.
 In the case of such a 2.times.2 mask size, four mask patterns are possible
 for a pixel of interest (i, j), as shown in FIGS. 4A to 4D. Any one of the
 four mask patterns is selected to define the filter. The filter (minimum
 filter) of the mask pattern shown in FIG. 4A is herein defined according
 to the following expression (7):
EQU I.sub.f [i][j]=min{Ib[i-1,j-1], Ib[i,j-1],Ib[i-1,j],Ib[i,j]} (7)
 where I.sub.f is a filtered binarized-image.
 In a filtering process described below in Example 2, a 2.times.2 mask is
 used. In a contraction process of contracting eight or four pixels, which
 is described below, a 3.times.3 mask is used. The eight pixels are those
 pixels which are located in the vicinity of a pixel of interest. The four
 pixels are those pixels which are located in the vicinity of a pixel of
 interest, for example, on upper, lower, right and left sides of the pixel
 of interest.
 This filtering process is effective not only in reducing a small noise
 region, but also in cutting out a large unnecessary region from a
 character region. As described below, the other unnecessary regions can be
 removed by, for example, performing a logic product operation between
 images or calculating the respective areas of the unnecessary regions,
 based on the respective features of the unnecessary regions.
 A character can be shaped by an expansion process and/or the contraction
 process, so that the character is formed and a hole in the character-drawn
 portion is eliminated. For illustrating the expansion process and the
 contraction process, "Primer for Computer Image Processing", pp. 76-77,
 edited by Tamura and published by Soken Shuppan is herein incorporated by
 reference.
 When the expansion process and/or the contraction process are conducted,
 the characters are more likely to be in contact with each other.
 Therefore, frames circumscribing the respective characters are determined
 prior to the expansion process and/or the contraction process. Then, the
 determined frames are used after shaping the characters.
 In the examples of the present invention, a character region is extracted
 from an image through the processes shown in FIGS. 2A, 2C and 2D, based on
 the above-mentioned properties.
 EXAMPLE 1
 Hereinafter, a character extracting apparatus according to Example 1 of the
 present invention will be described with reference to FIG. 5.
 The character extracting apparatus 50 in FIG. 5 includes an imaging section
 1 for obtaining an image of a specific region including a character(s), a
 Laplacian operation section 2 for applying an Laplacian operation to the
 obtained image, a binarizing section 3 for binarizing the Laplacian
 operation result, and a cutting section 6 for separating and cutting the
 character(s) on a character-by-character basis based on the binarized
 image.
 The imaging section 1 has an imaging camera and an illumination apparatus.
 The imaging camera obtains an image of a region including a character(s).
 The character(s) is written on, for example, a wafer.
 The Laplacian operation section 2 performs an operation denoted by the
 expression: .gradient..sup.2 I.sub.0 [i, j]=.differential..sup.2
 I/.differential.i.sup.2 +.differential..sup.2 I/.differential.j.sup.2.
 The binarizing section 3 binarizes the result of the operation under the
 following conditions:
 In the case where a portion where a character to be cut out is drawn, that
 is, a character-drawn portion has a convex property, I.sub.b [i, j] is a
 first value when .gradient..sup.2 I.sub.0 [i, j].gtoreq.0, and I.sub.b [i,
 j] is a second value when .gradient..sup.2 I.sub.0 [i, j]&lt;0; and in the
 case where the character-drawn portion has a concave property, I.sub.b [i,
 j] is a first value when .gradient..sup.2 I.sub.0 [i, j].ltoreq.0, and
 I.sub.b [i, j] is a second value when .gradient..sup.2 I.sub.0 [i, j]&gt;0.
 In the above expression, .gradient..sup.2 represents a Laplacian operator,
 [i, j] represents a pixel position in the image, I.sub.0 [i, j] represents
 a pixel value corresponding to the pixel position [i, j], and I.sub.b [i,
 j] represents a binarized pixel value corresponding to the pixel position
 [i, j].
 As described above, the convex property herein indicates such a property
 that the character-drawn portion becomes brighter toward the center
 thereof, and the concave property herein indicates such a property that
 the character-drawn portion becomes darker toward the center thereof.
 Hereinafter, a method for cutting out a character by the character
 extracting apparatus 50 according to Example 1 will now be described with
 reference to FIGS. 6 and 7.
 In Step 201, an image of a specific region is obtained. The image of the
 specific region includes a background and a character-drawn portion. In
 Step 202, a Laplacian operation is applied to the image of the specific
 region. As described above, this operation is performed according to the
 expression: .gradient..sup.2 I.sub.0 [i, j]=.differential..sup.2
 I/.differential.i.sup.2 +.differential..sup.2 I/.differential.j.sup.2.
 In Step 203, the Laplacian operation result is binarized. Then, a
 character(s) is extracted on a character-by-character basis, based on the
 binarized value, in Step 204.
 Hereinafter, Binarizing Step 203 will be described in detail with reference
 to FIG. 7.
 In Step 301, whether the character-drawn portion has a convex property or a
 concave property is determined.
 In the case where the character-drawn portion has a convex property, the
 process proceeds to Step 302. In the case where the character-drawn
 portion has a concave property, the process proceeds to Step 303.
 In Step 302, the process proceeds to Step 304 when .gradient..sup.2 I.sub.0
 [i, j].gtoreq.0, and proceeds to Step 305 when .gradient..sup.2 I.sub.0
 [i, j]&lt;0.
 In Step 304, I.sub.b [i, j] is a first value of, for example, zero. In Step
 305, I.sub.b [i, j] is a second value of, for example, 255.
 In the case where the character-drawn portion has a concave property, the
 process proceeds to Step 303.
 In Step 303, the process proceeds to Step 304 when .gradient..sup.2 I.sub.0
 [i, j].ltoreq.0, and proceeds to Step 305 when .gradient..sup.2 I.sub.0
 [i, j]&lt;0.
 In Step 304, I.sub.b [i, j] is a first value of, for example, zero. In Step
 305, I.sub.b [i, j] is a second value of, for example, 255.
 EXAMPLE 2
 A character extracting apparatus according to Example 2 of the present
 invention will now be described with reference to FIG. 8.
 The character extracting apparatus 80 in FIG. 8 includes a filtering
 section 4, an image processing section 5, an output section 7 and a
 control section 8, in addition to the components of the character
 extracting apparatus 50. More specifically, the filtering section 4
 calculates a binarized image I.sub.f filtered by a minimum filter. The
 image processing section 5 performs a process such as the process of
 calculating a logic product, an expansion process and/or a contraction
 process of the image. The output section 7 outputs the resultant cut-out
 image as image data. The control section 8 controls the operation of the
 character extracting apparatus 80.
 It should be noted that the Laplacian operation section 2, the binarizing
 section 3, the filtering section 4, the image processing section 5 and the
 cutting section 6 are implemented by, for example, a general purpose image
 processing board GPB made by Sharp Kabushiki Kaisha. This general-purpose
 image processing board GPB includes a high-speed image processing chip
 SALA (trade name of Sharp Kabushiki Kaisha) as a main component. As shown
 in FIG. 9A, the size of an operation region is limited to 3.times.3 in
 SALA in order to achieve a high-speed operation.
 FIG. 9A shows an operation region of SALA. The letters A, B, . . . , H, I
 in FIG. 9A represent pixel values (e.g. gray level) corresponding to the
 respective pixel positions. FIG. 9B shows values of a 3.times.3 Laplacian
 operator.
 This Laplacian operator, that is, the Laplacian operation section 2 in FIG.
 8 applies a Laplacian operation to the original image, i.e., to the
 character image provided from the imaging section 1. The Laplacian
 operation result I.sub.1 is given by the following expression (8):
EQU I.sub.1 [i][j]=B+D+F+H-4E (8)
 where (i, j) is a pixel position at E in FIG. 9A.
 The Laplacian operation result I.sub.1 is provided to the binarizing
 section 3. Then, the binarized section 3 binarizes the result I.sub.1
 according to the following expression (9) to obtain a binarized image
 I.sub.b :
 ##EQU6##
 where (i, J) is a pixel position at E in FIG. 9A.
 The obtained binarized image I.sub.b is provided to the filtering section
 4. Then, the filtering section 4 applies a filtering process to the
 binarized image I.sub.b according to the following expression (10). As a
 result, an image I.sub.f in which a noise region has been filtered is
 obtained.
EQU I.sub.f [i][j]=min{A,B,D,E} (10)
 In the above expression (10), (i, j) is a pixel position at E in FIG. 9A.
 Thereafter, the filtering section 4 applies such a filtering process to all
 of the pixels in the binarized image, and provides the resultant image
 data in which noise has been filtered to the image processing section 5.
 Then, the image processing section 5 performs a process such as a process
 of calculating a logic product, an expansion process and/or a contraction
 process of the image in order to improve the character recognition ratio.
 Thereafter, the image processing section 5 provides the resultant image
 data to the cutting section 6. The cutting section 6 separates and cuts
 the image data provided by the image processing section 5 on a
 character-by-character basis. The resultant character(s) is output from
 the output section 7 to a character recognition section (not shown).
 Hereinafter, a method for cutting out a character by the character
 extracting apparatus 80 according to Example 2 will be described with
 reference to FIGS. 3A through 3I and FIG. 10. More specifically, an
 example in which the character extracting apparatus 80 cuts out a
 character(s) on a wafer will be described.
 FIG. 3A shows an original image of a character region on a wafer. In Step
 201 of FIG. 10, the original image is provided to the character extracting
 apparatus 80. The image data has 256 gradation levels (a level 0
 corresponds to black, and a level 255 corresponds to white). In order to
 reduce the size of an unnecessary region as much as possible, the
 illumination apparatus and the imaging camera of the imaging section 1 are
 set so that a screen frame of the imaging camera corresponds to a region
 including a character(s).
 In Step 202, the Laplacian operation section 2 performs a Laplacian
 operation. In Step 203, the binarizing section 3 binarizes the Laplacian
 operation result. FIG. 3B shows the binarized image, that is, the image
 resulting from the Laplacian operation of the Laplacian operation section
 2 and the binarizing process of the binarizing section 3. Since Step 203
 of Example 2 is the same as Step 203 of Example 1, detailed description
 thereof will be omitted.
 In Step 205, the filtering section 4 filters an image having been subjected
 to the Laplacian operation and the binarized process, such as the image
 shown in FIG. 3B. The filtering section 4 applies the above-mentioned
 minimum-filtering process to a small noise-region present in the image
 shown in FIG. 3B, thereby filtering or cutting the noise from the
 character region. FIG. 3C shows an image resulting from such a filtering
 process.
 In Step 206, the image processing section 5 applies an expansion process
 and/or a contraction process to the filtered image. The image processing
 section 5 may calculate a logic product of the expanded and/or contracted
 image and the filtered image. FIG. 3D shows an image resulting from
 expanding the binarized image of FIG. 3C by the image processing section 5
 so that each of the remaining unnecessary regions are connected to each
 other. Such an expansion process may disadvantageously bring the
 characters into contact with each other or may disadvantageously bring the
 character region and the noise region into contact with each other.
 However, such a disadvantage can be easily eliminated by calculating a
 logic product of the above-mentioned two images.
 In Step 204, the cutting section 6 cuts out a character(s) output from the
 image processing section 5 on a character-by-character basis. Thereafter,
 the resultant character(s) is sent to the output section 7.
 The character extracting apparatus 80 according to Example 2 may
 additionally perform the following process.
 As shown in FIG. 3E, an unnecessary region which is in contact with a frame
 having a prescribed size is removed from the image shown in FIG. 3D.
 Moreover, a logic product of the images shown in FIGS. 3C and 3E may be
 calculated in order to eliminate the above-mentioned disadvantage caused
 by the expansion process. FIG. 3F shows an image resulting from such a
 process.
 Furthermore, in order to remove the noise remaining in the image shown in
 FIG. 3F, the image processing section 5 may remove a region having an area
 smaller than a prescribed threshold. FIG. 3G shows an image resulting from
 such a process.
 The prescribed threshold can be easily determined from the font of a
 character(s) of interest and a character set. A character set in the
 present example consists of capital letters, numerals, and a hyphen ("-").
 According to the font of these characters, a character having the smallest
 area is the hyphen. Therefore, the number of pixels in the area of the
 character "-" is used as the threshold. However, there may be a case where
 a character of interest is incomplete or deformed. Therefore, the
 threshold is preferably set to a value corresponding to two-thirds of the
 area of the character "-".
 The cutting section 6 may separate the image shown in FIG. 3G on a
 character-by-character basis, using, for example, a projection histogram.
 The projection histogram is described in the above-cited reference "Primer
 for Computer Image Processing", pp. 110-113, which is herein incorporated
 by reference for illustrating the projection histogram. FIG. 3H shows an
 image resulting from such a process. As shown in FIG. 3H, each character
 is located in a corresponding one of the separated character regions
 (defined by the broken line in the figure). It should be noted that there
 exist deformed characters in the image shown in FIG. 3H. It should be
 noted that the separated character regions are temporarily obtained by,
 for example, the projection histogram.
 In order to improve the recognition ratio of the deformed characters in the
 image shown in FIG. 3H, the image processing section 5 may apply an
 expansion process and/or a contraction process to shape the deformed
 characters, that is, to repair the image. FIG. 3I shows the repaired
 image. It should be noted that those characters that are not deformed can
 also be shaped by the expansion and/or the contraction process.
 The expansion process and/or the contraction process may disadvantageously
 bring the characters into contact with each other. Therefore, the image
 will not be newly separated on a character-by-character basis, but the
 separated regions shown in FIG. 3H are used. Then, the final image, that
 is, the image shown in FIG. 3I is output to the character recognition
 section.
 It should be noted that, although noise is filtered by the
 minimum-filtering operation performed by the filtering section 4, an AND
 operation or a mean-value operation can alternatively be used as the
 filtering process.
 According to a conventional method for separating an image based on edge
 information, only an edge is extracted or the sharpness of an original
 image is reduced by a Gaussian function. As a result, the following
 problems will occur:
 (1) In the field of factory automation, character edges and noise edges are
 mixed in most images to be recognized, and moreover, most character edges
 are not clear. When only an edge region is to be extracted, a character
 edge is not completely extracted. Therefore, it is very difficult to
 distinguish a character edge from a noise edge; and
 (2) It is difficult to estimate an optimal value .sigma. of the Gaussian
 function. Therefore, when the sharpness of the original image is reduced
 by the Gaussian function, the difference between the background and the
 character(s) will become less clear. Moreover, the characters will be
 brought into contact with each other, or the character region and the
 noise region will be brought into contact with each other. As a result,
 the character deformation or the like will occur when the noise is picked
 up, depending upon a width .sigma. of the Gaussian function, that is, a
 spatial constant of the Gaussian function.
 On the other hand, a character extracting method according to the present
 invention includes the steps of applying a Laplacian operation to the
 original image, and binarizing the Laplacian operation result. More
 specifically, the character extracting method of the present invention
 includes not only the Laplacian operation step of applying partial
 differentiation to the original image twice, but also the step of
 binarizing the Laplacian operation result. Therefore, noise included in
 the original image will not be emphasized.
 As a result, a character region can be precisely cut out from an image
 having a large amount of noise in the production environment such as
 factory automation.
 Accordingly, the character extracting method of the present invention can
 significantly reduce misrecognition resulting from an inaccurately
 extracted character region, as compared to the conventional methods. As a
 result, the character recognition ratio can be improved. Moreover, the
 present invention can be implemented by a highly simple algorithm as shown
 in the above-described examples. Therefore, the present invention can be
 used in, for example, a production line in which a higher-speed process is
 required.
 Moreover, noise can be filtered by performing a filtering process.
 Accordingly, the character region can be cut out more precisely.
 Furthermore, any of the various image processing techniques such as an
 expansion process, a contraction process and a logic product operation of
 images may additionally be used, whereby the character region can be much
 more precisely cut out.
 Furthermore, a Laplacian operation may be performed utilizing a variety of
 concave and convex properties rather than utilizing the edges.
 Various other modifications will be apparent to and can be readily made by
 those skilled in the art without departing from the scope and spirit of
 this invention. Accordingly, it is not intended that the scope of the
 claims appended hereto be limited to the description as set forth herein,
 but rather that the claims be broadly construed.