JPH0916713A - Image area dividing method - Google Patents

Abstract

PURPOSE: To extract a character area out of various input images without performing any complicated processing and to exactly divide the area correspond ing to the attribute of an image by dividing the image area while defining proximate pixels, for which density level difference is less than a threshold value, as the same area. CONSTITUTION: As the image information of the image read by a scanner 1, a position coordinate and a density level for each picture-element (pixel) are stored in an image buffer 2, for example. A relation discriminating part 3 performs labelling processing for applying a common label to two pixels, for which the position coordinates are mutually proximate and the difference of density levels is smaller than the threshold from the image information stored in the image buffer 2. A divided result output part 5 divides the pixels in the image, for which the value of an element in a correspondent label buffer 4 is common, into the area composed of pixels having the same label. An area attribute discriminating part 7 discriminates the attributes of respective divided areas based on the geometrical features of respective areas, density distribution and mutual inclusive relationship.

Description

Detailed Description of the Invention

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to an image area dividing method which is applied to a character recognition apparatus and an image recognition apparatus and divides a grayscale image according to attributes such as a character area and other areas.

[0002]

2. Description of the Related Art Conventionally, when a grayscale image is divided according to attributes such as a character region and other background regions, it is known that the image is divided into two by binarizing the image density. Examples of the method include "P-tile method", "division method based on maximal variance of bimodal histogram" (Makoto Nagao, "Image recognition theory", Corona Publishing, pp. 38-45).

In the P tile method, for example, as shown in FIG. 9A, the ratio of pixels having a density difference threshold value θ or more (or less) of the density level difference of each pixel in the image is predetermined. The density difference threshold θ
It determines the value of.

Further, the bisection method based on the maximum variance of the bimodal histogram is, for example, as shown in FIG. 9B, when the density value histogram is bisected by the density difference threshold value θ, the density between two pixels is classified between classes. The value of the density difference threshold θ is determined so that the variance is maximized.

In order to recognize a character area in an image, the P tile method requires the following assumptions. (1) The number of pixels occupied by a character area in an image is within a certain range.

In the P-tile method and the bisection method based on the maximum variance of bimodal histograms, the following assumptions are necessary. (2) The density band forming the character area and the density band forming the non-character area are continuous in the image.

That is, according to the above-mentioned both-binarization method, as shown in FIG.
As shown in FIG. 10A, if the density bands of the character area and the non-character area are continuous, the image and the character area can be recognized, but as shown in FIG. If any of the density bands in the non-text area is discontinuous,
The image and text area cannot be recognized.

Moreover, the above assumptions (1) and (2) are effective in many cases when inputting a paper surface in which characters are written in a different color on a single color paper, but the range of the input image is limited. If it is expanded to an image containing a color document or various objects, it generally does not hold. This is because in a color document, characters may be written in various different colors, and in such a case, the density level of the character area is not constant, and in a general image including various objects, the characters are imaged. This is because how much is included in each image varies greatly with each image.

The above-mentioned various objects are objects included in the image but unrelated to the original processing symmetry. For example, if a character string indicating a speed limit is extracted from a landscape and a system for recognizing the speed limit is mounted on a vehicle to drive, the object that exists in addition to the character string indicating the speed limit that is the processing symmetry in the image is present. For example, other vehicles, people, signs, traffic lights, etc. are various objects. Further, since the license plate of the automobile is recognized, even in a system in which a moving vehicle is photographed from a fixed point, various objects other than the license plate of the automobile, which is the processing symmetry in the image, are various objects.

Therefore, with the above-described conventional binarization method, it was impossible to automatically and accurately binarize an image including a color document or the like to extract a character portion.

Therefore, a method for extracting a character portion from various images including color documents is disclosed in, for example, (A) Japanese Patent Application Laid-Open No. 4-273788 (B) Japanese Patent Application Laid-Open No. 3-108078. It is disclosed.

In the method disclosed in the above publication (A), the density band of an image is divided into a plurality of pixels, and the pixels belonging to each of them are subjected to labeling and the like, so that various images including a color document and the like are processed. The character part is extracted.

In the method disclosed in the above publication (B), attention is paid to a specific density level of a target image, pixels of the density level and a density level close thereto are extracted, and a process such as labeling is performed. The character portion is extracted from various images including color documents.

[0014]

However, in the method of the above publication (A), the pixels belonging to both sides of the break in the density band of the image are separated into different regions, and therefore, they should originally belong to the same region. Two adjacent pixels of density level may be separated into separate regions. Therefore, for example, when a region where the density in the image changes gently is divided into a plurality of regions at the density band breaks,
A region including adjacent pixels having close density levels, that is, a region that should originally belong to the same region needs to be reintegrated, which causes a problem that a process for extracting a character region becomes complicated.

Further, in the method of the above publication (B), it is necessary to know at least the central density band of the density band of interest of the target image in advance. Therefore, if the density band of the character area is not known at the start of the process, such as when extracting the character part from the paper surface where different characters are printed in different colors, the density of interest will be repeated many times. There is a problem that a complicated process such as performing the process again by changing the band, comparing and combining the process results, and the process for extracting the character region becomes complicated.

The present invention has been made in view of the above problems, and an object thereof is to perform various input images such as images including color documents without performing complicated processing for extracting a character area. An object of the present invention is to provide an image area dividing method capable of extracting a character area and accurately dividing an area according to attributes such as a character area and a non-character area of an image.

[0017]

According to a first aspect of the present invention, there is provided an image area dividing method for dividing a grayscale image according to attributes such as characters and non-characters, wherein pixels in the grayscale image are close to each other. In addition, labeling is performed based on the relationship that the difference in density level is smaller than the threshold value, the region is divided into regions having pixels having the same label, and the attribute of each region after division is determined.

An image area dividing method according to a second aspect is the method according to the first aspect.
In the image area dividing method, the attribute of each area after the division is determined based on the geometrical characteristics of each area, the density distribution, and the mutual inclusion relation.

[0019]

According to the method of claim 1, the image area is divided into pixels which are close to each other and whose density level difference is less than or equal to the threshold value, and the divided image area is divided into areas. Since the attributes (text, non-text, etc.) of each area are determined by the geometrical characteristics of each area, the density distribution, and the mutual inclusion relationship, no matter how many character areas have different density levels in the image, If the density level difference between the adjacent non-character area is sufficient, the pixels forming the character area can be accurately extracted.

That is, the image area is divided without making any assumption about the density level of the area or the density distribution in the image, other than the assumption that the adjacent pixels which should belong to the same area have the close density level. Therefore, in order to recognize the character area from the image as in the conventional art, "the number of pixels occupied by the character area in the image is within a certain range", "the density band forming the character area in the image" ,
The density bands forming the non-character area are continuous. There is no need to make assumptions such as.

Further, when it is determined whether or not adjacent pixels belong to the same area, a small difference in density level is tolerated, and since the density bands are not divided and processed in advance, they should originally belong to the same area. , Two adjacent pixels with close density levels will not be divided into different regions.

Therefore, for example, a region where the density changes gently will not be divided into a plurality of regions at the density band breaks, and there will be no need to re-integrate a region including adjacent pixels having similar density levels. The extraction process of the character area can be easily performed.

Furthermore, as described above, no matter how many character areas have different density levels in the image, if the density difference between them and the adjacent non-character areas is sufficient, the character areas are accurately constructed. It becomes possible to extract pixels.

Thus, even if the characters written in different colors are in the same image, even if the density level of the area to be processed is unknown, the character area can be extracted without any problem.

Therefore, without performing complicated processing,
Character areas can be extracted from a wide variety of input images including color documents in which areas with different density levels exist, and as a result, images can be accurately displayed according to attributes such as character areas and non-character areas. It can be divided.

[0026]

DESCRIPTION OF THE PREFERRED EMBODIMENTS One embodiment of the present invention will be described below with reference to FIGS. In this embodiment, a case where the image area dividing method of the present invention is applied to a character recognition system equipped with an optical character reading device will be described.

The character recognition system according to this embodiment is shown in FIG.
1, the scanner 1, the image buffer 2, the relationship determination unit 3, the label buffer 4, the division result output unit 5, the division result buffer 6, the region attribute determination unit 7, the attribute determination rule base 8, the region attribute buffer 9, Value image generation unit 10, binary image buffer 11, character cutout unit 12, character coordinate buffer 13, character recognition unit 14, recognition result buffer 15, language processing unit 16, language dictionary 17, result correction unit 18, display 19, keyboard. 20, a result output unit 21, and a printer 22.

The scanner 1 is adapted to optically read an image composed of a character area and a non-character area such as paper. The image buffer 2 stores, as image information of an image read by the scanner 1, for example, a position coordinate and a density level of each pixel (hereinafter referred to as a pixel).

From the image information stored in the image buffer 2, the relation determining unit 3 performs a labeling process for giving a common label to two pixels "position coordinates are close to each other and density levels are close to each other", and the label is labeled. The contents of the buffer 4 are rewritten. The image division processing in the relationship determination unit 3, that is, the rewriting of the contents of the label buffer 4 will be described in detail later.

The label buffer 4 is an array for storing integers corresponding to the pixels in the image on a one-to-one basis.

It is represented as [0], L [1], L [2], .... The label buffer 4 is initialized so that all the elements L have different numerical values before rewriting by the relationship determining unit 3. For example, L [i] = i (i =
0,1, ...) is initialized.

The division result output unit 5 refers to the contents of the image buffer 2 and the label buffer 4, divides the pixels in the image into those in which the corresponding element values of the label buffer 4 are common, and performs the subsequent processing. The information required for is extracted and stored in the division result buffer 6. Here, pixels in which corresponding element values of the label buffer 4 are common belong to the same area.

The following information is stored in the division result buffer 6, for example. I. Coordinates of vertices of circumscribing rectangle of each area II. Average value of pixel density value for each area III. Number of pixels forming each area In the division result buffer 6, if necessary for the subsequent processing, the above-mentioned I to III obtained in the processing up to here are obtained. Any information other than the information may be stored.

Of the above information, I can be obtained by calculating the minimum and maximum coordinates in the horizontal and vertical directions of the pixels forming the area. II is obtained by collecting pixels having common values of the elements of the label buffer 4 and calculating the density average value thereof. III is obtained by calculating the number of pixels in which the values of the elements of the label buffer are common.

The area attribute judging section 7 judges the attribute of each area after division based on the geometrical characteristics of each area, the density distribution, and the mutual inclusion relationship. That is, the area attribute determination unit 7 refers to the information in the division result buffer 6 and the content of the attribute determination rule base 8 to determine the attribute of each area (for example, whether it is a character or a non-character), and The determination result is stored in the area attribute buffer 9.

The following rules are used for the attribute determination rule base 8. A character attribute is given to an area having a smaller number of pixels than the area of the circumscribed rectangle of the area. A non-character attribute is given to a circumscribed rectangle with a size larger than a certain size. A non-character attribute is given to an area of a certain size or larger that has another area having a character attribute inside. For a non-character area having a small area of the circumscribing rectangle, when there is a character area in which both the density average value and the coordinates of the circumscribing rectangle are close, a character attribute is given.

Among the above rules, since the majority of characters are composed of lines, the number of pixels forming a character is
This is because it is smaller than the area of the circumscribed rectangle. Uses the fact that the size of most characters falls within a certain range. Takes advantage of the fact that a character may not be a commonly used character. Takes advantage of the fact that punctuation is small in size and can be an exception to the rule, but that there are usually other characters in its vicinity. The attribute determination rule base 8 may use a known rule other than the above.

The binary image generation unit 10 refers to the image buffer 2, the label buffer 4, the division result buffer 6 and the area attribute buffer 9 described above, and sets 1 to the pixel corresponding to the character and to the other pixels. An image in which 0 is given as a density level is created, and the binary image is stored in the binary image buffer 11.

Incidentally, if it is desired to simply take out the pixels forming a character, so-called binarization processing is not necessary at all, and the contents of the image buffer 2, the division result buffer 6 and the area attribute buffer 9 are appropriately combined and output. However, in the present embodiment, as described above, the character area and the non-character area may be applied to the characters written in different colors so that the same recognition method applied to the conventional monochrome image can be applied. A binary image is created by giving different constant density levels to and.

The character cutout unit 12 refers to the information in the binary image buffer 11 to extract the character position in the image and stores the character coordinates in the character coordinate buffer 13. At this time, in addition to the information of the binary image buffer 11, the division result buffer 6 and the area attribute buffer 9 are added.
The information included in and is used to exclude the non-character area from the processing target.

The character recognizing unit 14 uses the binary image buffer 11
The character recognition is performed by referring to the information in the character coordinate buffer 13 and the result is stored in the recognition result buffer 15. The recognition result buffer 15 stores a character string including the recognition result of each character.

The language processing unit 16 uses the recognition result buffer 15
The word is extracted from the character string stored in
If there is a word included in the character string that is not in the language dictionary 17, a mark indicating that the recognition result of the word is doubtful is added to the word and the recognition result buffer 1
5 is stored.

The result correction section 18 includes a recognition result buffer 15
Is output to the display 19. At this time, the result correction unit 18 reversely displays the word, which is given by the language processing unit 16 and has a mark whose suspicion is recognized, so that the user is urged to change the erroneously recognized word. The user operates the keyboard 20 to change the word with the mark to the correct word, and then rewrites the misrecognized word before the change into the word after the change in the recognition result buffer 15 again. It is supposed to be stored.

The result output unit 21 includes a recognition result buffer 15
Is output to the printer 22.

Here, the image division processing in the relation determining unit 3 will be described below with reference to FIGS. 4 to 8. The position of the pixel shown in each drawing is expressed as the number of row counting from the top and the number of column counting from the left (hereinafter referred to as the row and column in the present embodiment) in the figure.

First, the relation S with respect to any two pixels in the image stored in the image buffer 2 by the scanner 1
Is defined by the following procedure. In the following description, "close to" means "adjacent" in the sense of so-called 4-connection or 8-connection, or may be "condition that the mutual distance is small". However, being adjacent in the sense of four connections means being located one above, one below, one left, or one right of the pixel of interest, and being adjacent in the sense of eight connections. Means any of the above or 1
One upper right, one lower right, one upper left, one lower left. Here, the above-mentioned 4-connection and 8-connection will be described with reference to, for example, FIG. 8, and when attention is paid to a pixel E among the nine pixels A to I arranged in a grid pattern, D, B, and F in the drawing are used. , H are regarded as adjacent points of E, and the pixel connection relationship is considered.
In the figure, A, B, C, D, F, G, H, and I are all pixel E
It is 8 to consider the connection relationship of pixels by considering them as adjacent points of
It is a connection.

Next, regarding any two pixels X ₀ and X ₁ , "two pixels X ₀ and X ₁ are close to each other,
When the difference in the density level is less than or equal to the density difference threshold α, ”
It is defined as “relationship R (X ₀ , X ₁ ) holds”. However, the above α may be set to the same value over the entire image, or the function α of the density levels of two pixels may be set in advance and the value may be set. Further, the coordinates of the two pixels and the like may be included in the parameter that defines α.

The above "relationship R (X ₀ , X ₁ ) holds"
This will be described below with reference to FIG. However,
The density difference threshold α is set to 2 for the entire image, and “proximity” means that they are adjacent in the sense of eight connections. The small circles in the figure represent pixels, and the numbers in the circles represent density levels.

In FIG. 4, for example, paying attention to the pixel at the density level 3 in the 3rd row and the 3rd column, what is connected to this pixel by a straight line is a pixel for which the relationship R is established between the pixel and the pixel. For example, the pixel in the third row and third column is X
₀ , assuming that the pixel at the 3rd row and the 2nd column is X ₁ , the density level of X ₀ is 3, the density level of X ₁ is 4, and the density level difference is 1, that is, the density difference threshold α ≦ There is a relationship of 2. Therefore, the relationship R (X ₀ , X ₁ ) is established between the pixel X ₀ in the 3rd row and the 3rd column and the pixel X ₁ in the 3rd row and the 2nd column.

For two pixels a and b,
"Pixel column {Xi} (i = 0, 1, ..., N-1; n
Is a positive integer, X ₀ = a, X _n-1 = b), and
When R (X _i , X _{i + 1} ) (where 0 ≦ i ≦ n−2) holds, the relation “S (a,
b) holds. ”

That is, the relationship S (a, b) holds that the pixels a are close to each other so that the relationship R is established with the currently existing pixel (from the definition of the relationship R, the relationship R is Repeatedly move to one of the two pixels that hold true)
It shows that pixel b can be reached.

The above "relationship S (a, b) holds" will be described below with reference to FIG. However, the density difference threshold α is set to 2 with respect to the entire image, and “close”
And 8 are adjacent to each other in the sense of 8 connections. The small circles in the figure represent pixels, and the numbers in the circles represent density levels.

In FIG. 5, what is connected to each other by a straight line is a pixel having a relationship S between this pixel and the third row and third column. That is,
Pixels that are connected by a straight line have a relationship R with each other, and by tracing the pixels with which this relationship R holds, all pixels that have a relationship S with the pixel in the third row and third column can be reached. It is like this.

However, for convenience of description, in FIG. 4 and FIG. 5, a pixel which is adjacent to the pixel in the third row and the third column but does not have the relation R, for example, the pixel in the second row and the fourth column (density level 18) Even if a relationship R is established between this pixel and another pixel adjacent to this pixel, for example, the pixel in the 3rd row and the 4th column or the pixel in the 1st row and the 4th column, the straight line indicating the relationship R is omitted. There is.

Further, the purpose of the relation determining unit 3 is to take out all combinations of two pixels (hereinafter, Xi, Xj) in the image, and actually obtain the relation S (Xi, Xj).
j) is determined, and if so, X is
Two elements L of the label buffer 4 corresponding to i and Xj
Labeling processing for giving the same label value to [i] and L [j] is performed.

However, in the relationship determination unit 3, S
Instead of determining whether (Xi, Xj) holds, it is determined whether R (Xi, Xj) holds, and if so, two elements L [i], L [j], And a value common to all the elements stored in the label buffer 4 having a value equal to any of the elements at that time (hereinafter,
V ₀ ) may be substituted. in this case,
V ₀ may be the smaller value of either L [i] or L [j] at that time.

In this way, the result similar to the case of using the relation S can be obtained by using the relation R either directly through the relation R or indirectly through another one or a plurality of pixels. This is because the same label is given to two pixels that are physically connected, and from this, the same label is given to two pixels for which the relation S holds.

A case in which the background portion in which the density level changes gently and the character portion are cut out at the same time by using the character recognition system having the above configuration will be described below with reference to FIG. The above-described relationships R and S are used to separate each region of the image, and the density difference threshold α used at this time is used.
Is 2 with respect to the entire image, and “adjacent” means that they are adjacent in the sense of 8 connections. The small circles in the figure represent pixels, and the numbers in the circles represent density levels.

As shown in FIG. 6A, the image information read by the scanner 1 is provided with a density level for each pixel. From this image information, a pixel group corresponding to a character portion, that is, close to each other is provided. And the density difference threshold α
A pixel group having a value of 2 or less is cut out. That is, FIG.
In the image information shown in (a), the background portion shown in FIG. 6 (b) and the character portion shown in FIG. 6 (c) are cut out at the same time. The above-mentioned character portions are connected by the relationship S from the pixels of the density level 3 in the 5th row and 9th column shown in FIG. 6A.

Further, in the same image as the input image described in FIG. 6, the elements L [i], L of the label buffer 4 are
The extraction process of the character portion in [j] will be described below with reference to FIG. 7. In the figure, each grid represents an element of the label buffer 4, and the numeral in each grid represents the value of the element of the label buffer.

FIG. 7A shows the numerical values of the label buffer of the grid before the content of the label buffer 4 is rewritten by the relationship determining unit 3, and FIG. 7B shows the values of the label buffer 4 of the label buffer 4 by the relationship determining unit 3. It shows the number in the grid's label buffer after the contents have been rewritten. That is, in FIG. 7B, the value of the label buffer corresponding to the pixels forming the character area is rewritten to 22, and the value of the label buffer corresponding to the pixels forming the non-character area is rewritten to 0. From this, it can be seen that the value of the label buffer determines which region the corresponding pixel belongs to. However, the relationship of each pixel is the same as the relationship R and S shown in FIG.

Here, regarding the rewriting of the element contents of the label buffer 4 in the relation judging section 3 using the relation R,
This will be described below with reference to the flowchart shown in FIG.

First, two unprocessed pixels X in the image
i, Xj (i ≠ j) are taken out (S21).

Next, the two extracted pixels Xi, X
It is determined whether j satisfies the relation R (Xi, Xj) (S22). Here, if the relation R does not hold, S2
Move to 4.

If the relation R is established in S22, the element L of the label buffer 4 corresponding to the two pixels Xi and Xj.
[I], L [j], and all the elements of the label buffer 4 having the same value as any one of them are assigned a common value (S).
23).

Then all the pixels in the image Xi, X
It is determined whether or not the process has been performed for the combination of j (S
24). Here, if it is determined that the process is performed for all the combinations of the pixels Xi and Xj, the process in the relationship determination unit 3 ends. On the other hand, all pixels Xi, X
If it is determined that processing is not performed for the combination of j,
The process moves to S21 again and is processed.

An image area dividing method in the character recognition system having the above configuration will be described below with reference to the flowchart shown in FIG.

First, a sheet on which a grayscale image is formed is optically scanned by the scanner 1 and image information is input to the image buffer 2 (S1). At this time, the label buffer 4 is initialized so that all different values are entered (S2). For example, initialization is performed so that L [i] = i (i = 0, 1, ...).

Next, in the relationship determining unit 3, the image buffer 2
The content of the label buffer 4 is rewritten according to the procedure shown in the flow chart shown in FIG. 2 based on the image information stored in the image division processing (S3).

Next, the division result output unit 5 refers to the contents of the image buffer 2 and the contents of the rewritten label buffer 4 to refer to the pixel in the image as the value of the corresponding element of the label buffer L [i]. Are shared and stored in the division result buffer 6 (S4).

Then, the area attribute judgment unit 7 judges the attribute (character or non-character) of each area by referring to the contents of the division result buffer 6 and the attribute judgment rule base 8.
The attribute is stored in the area attribute buffer 9 (S5).

Next, the binary image generator 10 refers to the contents of the image buffer 2, the label buffer 4, the division result buffer 6 and the area attribute buffer 9 to binarize the pixel, It is stored in the binary image buffer 11 (S6).

Next, the character cut-out unit 12 refers to the contents of the binary image buffer 11 to extract the character position in the image and makes it available in the character coordinate buffer 13 (S7).

Then, the character recognition unit 14 refers to the contents of the binary image buffer 11 and the character coordinate buffer 13 to perform character recognition, and stores the result in the recognition result buffer 15 (S8).

After that, the language processing unit 16 extracts words from the character string stored in the recognition result buffer 15, adds a mark indicating suspiciousness to a word whose recognition is suspicious, and again stores it in the recognition result buffer 15. Store (S
9).

Next, the result correction section 18 outputs the contents of the recognition result buffer 15 to the display 19, the user corrects the erroneously recognized word using the keyboard 20, and stores it in the recognition result buffer 15 again. (S10).

Finally, in the result output section 21, the contents of the recognition result buffer 15 storing the corrected word are read by the printer 2
It outputs from 2 (S11).

As described above, according to the present image area dividing method, the image area is divided into pixels which are close to each other and whose density level difference is less than or equal to the threshold value, and are divided into the respective areas. Since its attributes (text, non-text, etc.) are determined based on the geometrical characteristics, density distribution, and mutual inclusion relations, no matter how many character areas have different density levels in the image, If the density level difference between the adjacent non-character area is sufficient, the pixels forming the character area can be accurately extracted.

That is, the image area is divided without making any assumption about the density level of the area and the density distribution in the image, other than the assumption that the adjacent pixels which should belong to the same area have the close density level. Therefore, in order to recognize the character area from the image as in the conventional art, "the number of pixels occupied by the character area in the image is within a certain range", "the density band forming the character area in the image" ,
The density bands forming the non-character area are continuous. There is no need to make assumptions such as.

Therefore, in a wide variety of images including color documents and the like, it is impossible with the conventional binarization method.
It is possible to automate the digitization and to extract the character part accurately.

Further, in this image area dividing method, when it is determined whether or not adjacent pixels belong to the same area, a small difference in density level is allowed, and the density band is not divided in advance for processing. Two adjacent pixels with close density levels, which should originally belong to the same region, are not divided into different regions.

Therefore, for example, an area where the density changes gently is not divided into a plurality of areas at the density band breaks, and it becomes unnecessary to reintegrate the area including adjacent pixels having similar density levels. The extraction processing of the character area can be performed easily and accurately.

Further, in the image area dividing method, as described above, no matter how many character areas have different density levels in the image, if the density difference between them and the adjacent non-character area is sufficient, it is accurate. It is possible to extract the pixels that form the character area.

As a result, even when the characters written in different colors are in the same image, even if the density level of the region to be processed is unknown, the process can be performed without any problem.

Further, in the present image area dividing method, since it can be known to which area each pixel in the image belongs, the shape of the area can be accurately determined.

In the image area dividing method of the present invention, the character area is cut out at almost the level of the connected components. Therefore, the characters or pixels forming a part of the character are always taken as a block and extracted from the image, and these are integrated. When the method is combined with a method of extracting a character string or recognizing the character string (a method disclosed in Japanese Patent Application Laid-Open No. 5-81474 filed by the applicant of the present application), omitting a part of the character string extracting or character recognizing process. You can

In this embodiment, the scanner 1 is used as the image reading means, but a camera or the like may be used.

[0087]

According to the image area dividing method of the first aspect of the present invention,
As described above, the pixels in the grayscale image are labeled based on the relationship that they are close to each other and the difference in their density levels is smaller than the threshold value, and are divided into regions consisting of pixels having the same label. The configuration is such that the attribute of each area is determined.

As a result, the image area is divided into pixels which are close to each other and whose density level difference is less than or equal to the threshold value as the same area, and the attribute such as character or non-character of each area after the division is determined.
Since it is determined based on the geometrical characteristics of each area, the density distribution, and the mutual inclusion relation as described in claim 2, no matter how many character areas have different density levels in the image, they are close to each other. If the density level difference between the non-character areas is sufficient, the pixels forming the character area can be accurately extracted.

Therefore, without performing complicated processing,
Character areas can be extracted from a wide variety of input images including color documents in which areas with different density levels exist, and as a result, images can be accurately displayed according to attributes such as character areas and non-character areas. The effect that it can be divided is achieved.

[Brief description of the drawings]

FIG. 1 is a flowchart showing an image area dividing method according to an embodiment of the present invention.

FIG. 2 is a flowchart showing an example of image division processing of the image area division method shown in FIG.

3 is a block diagram showing an outline of a character recognition system to which the image region dividing method shown in FIG. 1 is applied.

FIG. 4 is an explanatory diagram showing the relationship of each pixel in the image division processing of the image area division method shown in FIG.

5 is an explanatory diagram showing a relationship of each pixel in the image division processing of the image area division method shown in FIG.

6 is an explanatory diagram of image division processing in the image area division method shown in FIG.

FIG. 7 is another explanatory diagram of the image division processing in the image area division method shown in FIG.

FIG. 8 is an explanatory diagram showing a pixel connection relationship.

FIG. 9 is an explanatory diagram showing a conventional image area dividing method.

FIG. 10 is an explanatory diagram showing a conventional image area dividing method.

[Explanation of symbols]

 1 Scanner 3 Relationship Determining Section 5 Division Result Output Section 4 Label Buffer 7 Area Attribute Determining Section

Claims (2)

[Claims] 1. An image area dividing method for dividing a grayscale image according to attributes such as characters and non-characters, wherein pixels in the grayscale image are close to each other and the difference in their density levels is smaller than a threshold value. A method for dividing an image area, which comprises labeling based on a relationship, dividing the area into pixels having the same label, and determining an attribute of each area after the division. 2. The image area dividing method according to claim 1, wherein the attribute of each area after the division is determined based on the geometrical feature of each area, the density distribution, and the mutual inclusion relation.