JP3046656B2 - How to correct the inclination of text documents - Google Patents

Description

DETAILED DESCRIPTION OF THE INVENTION

[0001]

BACKGROUND OF THE INVENTION 1. Field of the Invention The present invention relates to a method for correcting the inclination of a character document, and more particularly to a method for correcting the inclination of a character document, such as a copying machine, a facsimile, an OCR,
In a device that reads a set document as an image,
The present invention relates to a correction method for eliminating an unexpected inclination of a document.

[0002]

2. Description of the Related Art Due to the inclination of a character document, the data compression rate decreases in facsimile, and the recognition rate decreases in OCR. Further, in a copying machine, in order to stabilize the copy quality, it is necessary to automate the inclination correction of the original.

[0003] Conventionally, as the inclination detecting method, expansion, contraction,
Known methods using thinning (Nori Nakamura, Makoto Ujiie,
Noriyoshi Okamura and Minatoshi Minami, "Character Region Extraction Algorithm for Mixed Mode Communication", IEICE Transactions VO
L. J67-D, No. 11, pp1277-1284,
November 1984).

According to this method, a character document is binarized,
The letters are connected by expansion. Further, the character string is displayed as a relatively thin band by shrinking and filling, and this is thinned. Further, irregular portions at both ends of the thinned image are deleted by reduction. In such a thinned image, after labeling, the inclination of a group having a length equal to or more than a predetermined value is obtained,
This inclination is regarded as the inclination of the document. Here, the number of groups given by labeling is the maximum pixel density in one scan, and if the number of groups increases significantly, not only the processing time will increase but also the label counter will overflow, making it impossible to process.

[0005]

SUMMARY OF THE INVENTION The present invention has been made in order to solve such a conventional problem, and a character document which can surely execute skew correction and has a high processing speed regardless of the number of groups. It is an object of the present invention to provide a method for correcting the inclination.

[0006]

According to a method of correcting the inclination of a character document according to the present invention, an original image of a character document is sequentially expanded,
The optimal number of expansions is obtained by detecting a decrease in the amount of expansion of the graphic area before and after the expansion, and an image is generated by expanding the original image by the number of times of the optimal expansion. Calculate to determine which component is greater in the vertical and horizontal directions.
The original image is expanded a predetermined number of times based on the number of times.
The contour image of the image obtained by
Of the vertical and horizontal components of the obtained contour image, less components are removed, and only the more components, which are the main components ,
The inclination is calculated, and the original image is rotated so as to eliminate the inclination. According to the method of correcting the inclination of a character document according to the present invention, the inclination can be detected and corrected only by a so-called convolution operation without performing sequential processing such as labeling.

[0007]

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS Next, an embodiment of a method for correcting the inclination of a character document according to the present invention will be described with reference to the drawings. FIG. 1 is a flowchart showing the embodiment. First, a document is read from input means such as a scanner (step 1-1), and the size of an image is adjusted so as to conform to the subsequent processing (step 1).
-2) For example, if the output image of the scanner is 4096 × 40
96 pixels, and the processing target pixel of the image processing unit is 1024 pixels.
If it is × 1024, a reduction of １ ／ is required. In this reduction process, if the process of setting the average value of the predetermined convolution to the density of the specific pixel is performed, the maximum density information of the original image can be preserved. That is, in the 1/4 reduction, the average value of the 4 × 4 convolution is set as the density of the specific pixel (for example, the upper left pixel in the convolution). Assuming that the pixel value at the coordinates (x, y) of the digital image is f (x, y) and the image after the convolution processing is g (x, y), the “predetermined convolution” is (x, y). ), Which includes an m × n pixel area, for example, g (x, y) is f (x, y), f (x +
1, y),. . . , F (x + m-1, y) f (x, y + 1), f (x + 1, y + 1),. . . , F
(X + m-1, y +
1). . . . . . . . . . . . . . . . . . . . . . .
. . . . . . . . . . f (x, y + n-1), f (x +
1, y + n-1),. . . , F (x + m-1, y + n-
1) It is a function of. Here, assuming that 0 <= x <= M−1 and 0 <= y <= N−1 for f (x, y), 0 <= x <= M− for g (x, y) m, 0 <= y <= N−n. Then, g (x, y) becomes f (x, y).
When there is a linear relationship with y),

(Equation 1) G (x, y) is calculated by the following calculation. Where k
(I, j) is a coefficient of f (x + i, y + j). Note that the “4 × 4 convolution” means a process of m = n = 4, and when m = n = 3, is referred to as “3 × 3 convolution”.

The above-mentioned “process for setting the average value of a predetermined convolution to the density of a specific pixel” is expressed by the following equation (1).
(I, j) = 1 / (m × n) means “reduction processing”. When the pixel value of the processed image is h (x, y), h (x, y) ) = G (m × x, n × y). Here, the range of (x, y) of h (x, y) is as follows: 0 <= x <= M / (m−1), 0 <= y <= N / (n−
1) Further, g (m × x, n × y) is a pixel obtained as a result of the process of “the process of setting the average value of the predetermined convolution to the density of the specific pixel”.

Next, a density histogram of the image is obtained, binarized by an optimal threshold (step 1-3), and noise is further removed (step 1-4). As a method of calculating the optimum threshold, a method using the median of the density as a threshold, a mode method,
There are a discriminant analysis method, a P-tile method, and the like. As a noise removing method, an isolated point removal, a median filter, or the like is effective. At the time of detecting the inclination, the image of the character document is expanded a predetermined number of times in the main component direction, that is, the horizontal writing document in the horizontal direction, and the vertical writing document in the vertical direction based on the optimum number of times of expansion, and each line is converted into a striped figure. By thinning this figure, inclination detection becomes easy. Here, a method of calculating the optimum number of times of expansion Ndir will be described (steps 1-5 to 1-13).

In step 1-5, the expansion is repeated thereafter.
Loop count for counting the number of inflation
The parameter i is initialized (i = 1). Then, step 1-6
The number of non-background pixels (the number of white pixels) Nw1 after noise removal
(I = 1) is counted, and the initial value of the number of non-background pixels is obtained.
You. Next, in step 1-7, expansion is performed once, and
In step 1-8, the loop counter i is incremented (i
= 2), in step 1-9, the number of non-background pixels Nw2 (i =
2) is counted. Next, in step 1-10, the non-
The third derivative (Δ ³ Nwi) of the number of background pixels is obtained . The third derivative is calculated by the following equation.

The third derivative Δ ³ Nwi = △ ² Nw (i + 1)
− △ ² Nwi However, △ ² Nwi = △ Nw (i + 1) − △ Nwi ΔNwi = Nw (i + 1) −Nwi Therefore, △ ³ Nwi = Nw (i + 3) −3 · Nw
(I + 2) + 3 · Nw (i + 1) −Nwi

When the value of the ^third derivative Δ ³ NWi becomes negative, it means that the inter-line fusion has already occurred, and that the increase in the figure area due to the expansion has been detected (step 1-10).
Therefore, at this stage, the process exits the expansion loop, and the number of expansions up to the previous time, Ndil = i-1, is adopted as the optimum number of expansions (step 1-13). Then, the previous dilated image is used for the following main component detection (step 1-14). In this way, if the area change of the figure is traced for each expansion of one layer, the global start of fusion can be reliably detected, and the optimum number of expansions can be obtained.

The start of fusion may be unclear due to the influence of noise or the like that cannot be completely removed by the noise removal. Therefore, the upper limit I of the number of times of expansion is set in advance, and even before the third derivative reaches a negative value,
The expansion is terminated (step 1-11). In this case, the optimal number of times of expansion is (I-1). If the third derivative is positive,
First, the content of the loop counter i is less than the upper limit I of the number of times of expansion.
, The process returns to step 1-6 again, and
Repeat Step 1-7, Step 1-8, Step 1-9
And again, at step 1-10, the sign of the third derivative is determined.
Refuse. In the loop of steps 1-6 to 1-11,
Table 1 below.

[Table 1] Note that in order to determine whether or not ＜ ³ Nwi <0 is satisfied,
Requires that the current i be 4 or greater, i = 2 and
In the case of 3, “△ ³ Nwi <0” in step 1-10
Cannot be determined, so the same branch as when not satisfied (to N
Is performed). Next, the main component of the document, that is, whether the document is a horizontally written document or a vertically written document is determined. This determination is made by synthesizing the pattern inclination components of each part of the image (step 1-14).

When detecting the inclination component, in the 3 × 3 convolution, the patterns of FIGS. 2A to 2L are set as the horizontal component, and the patterns of FIGS. 3A to 3L are set as the vertical component. The main component of the entire image is obtained depending on whether the number is large. These patterns are the outlines of the figure. The inclination of the outline is defined for each two-pixel row, and those with the total inclination of the convolution near horizontal are considered as horizontal patterns, and those near vertical are considered as vertical patterns. It is a pattern of directions. Note that a pattern including one background pixel on the periphery of the convolution ((K) in FIGS. 2 and 3,
(L)) is a vertical pattern when the background is on the left and right peripheral edges, and a horizontal pattern when the background is on the upper and lower peripheral edges, and does not include any oblique 45-degree pattern.

Assuming that the number of vertical patterns is Nv and the number of horizontal patterns is Nh, if Nv <= Nh, the main component is determined to be horizontal, otherwise it is determined to be vertical (step 1-). 15-1-17). Here, the index of the main component is set to horv.
"v" is substituted for orv, and "h" is substituted for horv in the horizontal direction (steps 1-16 and 1-17).

Here, the original image before expansion is expanded a predetermined number of times in the direction of the main component, and the characters on each line are connected. The number of times of expansion should be the minimum number of times that characters are surely connected, and empirically, a value such as 9 · Ndir / 4 [times] is used based on the Ndir. FIG. 4A shows the result of executing the above-described number of horizontal expansions for the horizontally written original.
As shown in (B), most of the rows are connected integrally. The expanded image is subjected to contour extraction processing (step 1-20: FIG. 4C), and the vertical component is removed (step 1-21: FIG. 4D). Here, “removing the vertical component” means removing the vertical component of the contour image obtained by the contour extraction processing. Determined by pixel. For example, referring to the 3 × 3 pixel pattern shown in FIG. 7 for a specific pixel, when neighboring pixels match these patterns, it is determined to be a vertical pixel and the specific pixel is converted to a background pixel. Such processing is performed on all the pixels of the image from which the vertical component is to be removed, and the processing ends. As a result, only the main components of the image are extracted as the image, and the subsequent inclination calculation becomes easy. However, as is apparent from FIG. 4D, simply removing the vertical component often causes an oblique component affected by the vertical component at both ends of each row. The irregularities at both ends are removed (step 1-23). Here, “degeneration processing” means
This is a process of removing the end of the line figure. The determination as to whether or not to remove the line figure is made such that when the neighboring pixels match these patterns, it is determined that the central pixel should be removed and converted to a background pixel. Such processing is performed on all the pixels of the processing target image, and the processing ends. Note that the number of times of degeneration should be the minimum number of times that the irregular part can be completely erased, and empirically, based on the Ndil, 15Ndil + 1 + 1
(Times) and the like are adopted. This empirical formula is 1 to 1.5
This means deleting a line of a length corresponding to a character. The result of the degeneration processing is as shown in FIG. 4E, and each row is displayed as a substantially straight line. However, in FIG. 4E, minute noise is slightly included, and when detecting the inclination, processing is performed on an image (FIG. 4F) from which these noises have been eliminated.

FIGS. 6A, 6B, 6C, 6D, and 6D show the calculation of the inclination of an image mainly composed of the horizontal component.
The number of each of the patterns (E), (F), (G), (H), and (I) is counted, and the counted values are multiplied by a coefficient and totaled. Here, the shape of each pattern and the numerical value to be multiplied by the number of each pattern are as follows.

Horizontal pattern shape and coefficient (A) 3-pixel horizontal pattern coefficient = 0 (B) (downward right pattern of 2 pixels) + (horizontal pattern of 2 pixels) coefficient = 0.5 (C) (2 pixels (Horizontal pattern) + (two-pixel right-down pattern) coefficient = 0.5 (D) (two-pixel horizontal pattern) + (two-pixel upper-right pattern) coefficient = −0.5 (E) (two-pixel right (Up pattern) + (horizontal pattern of 2 pixels) coefficient = −0.5 (F) right down pattern of 3 pixels coefficient = 1 (G) upper right pattern of 3 pixels coefficient = −1 (H) (right of 2 pixels) Downward pattern) + (upper right pattern of 2 pixels) coefficient = 0 (I) (upper right pattern of 2 pixels) + (downward right pattern of 2 pixels) coefficient = 0

These coefficients are error components with respect to the horizontal component of each pattern, and give an absolute value "1" to a diagonal pattern of three pixels. These patterns (A) ~
The total number of the entire image of (I) is Na to Ni.
Then, the inclination angle θ is given by the following equation.

Θ = tan ⁻¹ [｛0.5 (Nb + Nc)
−0.5 (Nd + Ne) + Nf−Ng｝ / (Na + Nb)
+ Nc + Nd + Ne + Nf + Ng + Nh + Ni)] Here, the numerator in tan ⁻¹ () corresponds to the error component in the horizontal component, and the denominator corresponds to the sum of the main components.

The same applies to the processing for a vertically written character original. FIGS. 5A to 5I show the processing contents of a horizontally written original for FIGS. 4A to 4F. Then, FIG.
FIGS. 7A to 7I show vertical patterns corresponding to (I). The shapes and coefficients of these patterns are as follows.

Vertical pattern shape and coefficient (A) Vertical pixel coefficient of three pixels = 0 (B) (downward left pattern of two pixels) + (vertical pattern of two pixels) Coefficient = 0.5 (C) (two pixels (Vertical pattern) + (2 pixel left down pattern) coefficient = 0.5 (D) (2 pixel vertical pattern) + (2 pixel right down pattern) coefficient = −0.5 (E) (2 pixel right Downward pattern) + (Vertical pattern of 2 pixels) Coefficient = −0.5 (F) Downward pattern of 3 pixels Coefficient = −1 (G) Upper right pattern of 3 pixels Coefficient = 1 (H) (Left of 2 pixels) Downward pattern) + (2 pixel right down pattern) coefficient = 0 (I) (2 pixel right down pattern) + (2 pixel left down pattern) coefficient = 0

Assuming that the total number of these patterns (A) to (I) for the entire image is Na to Ni, the inclination angle θ is given by the following equation. θ = tan ⁻¹ [｛0.5 (Nb + Nc) −0.5 (N
d + Ne) -Nf + Ng｝ / (Na + Nb + Nc + Nd)
+ Ne + Nf + Ng + Nh + Ni)] Here, the numerator in tan ^-1 () corresponds to the error component in the vertical component, and the denominator corresponds to the sum of the main components.

After the above calculation of the tilt angle, the image is affine-transformed so as to eliminate the tilt. The affine transformation can be executed by a matrix operation by software or an address conversion process by a dedicated IC. As described above, the main components of each row can be extracted without performing thinning,
Accurate and high-speed tilt correction based on global direction determination can be reliably performed.

[0025]

As described above, the method of correcting the inclination of a character document according to the present invention sequentially expands the original image of the character document and detects the slowdown of the expansion amount of the figure area before and after expansion, thereby reducing the optimum number of expansions. determined, the optimal expanded number of times to generate an image obtained by expanding the original image, the generated image, determined whether there are many aspect either component by calculating the horizontal and vertical components of the graphic outline each part, in the direction of greater component Only the optimal expansion times
The original image is expanded a predetermined number of times based on the number
Extract the contour image of the obtained image, and by this extraction
Of the vertical and horizontal components of the obtained contour image, less components are removed, the main component, the inclination of which is calculated using only the more components, and the original image is rotated so as to eliminate this inclination, so that it is sure. There is an excellent effect that inclination correction can be performed and processing is performed at high speed regardless of the number of groups.

[Brief description of the drawings]

FIG. 1 shows a method of correcting the inclination of a character document according to the present invention.
It is a flowchart of Example 1-1 to Example 1-13 which shows an Example.

FIG. 2 is a conceptual diagram for explaining a horizontal component pattern. (A) is a conceptual diagram showing a horizontal component pattern including an error component of 0.5. (B) is a conceptual diagram showing another horizontal component pattern including a 0.5 error component. (C) is a conceptual diagram showing another horizontal component pattern including an error component of 0.5. (D) is a conceptual diagram showing another horizontal component pattern including an error component of 0.5. (E) is a conceptual diagram showing another horizontal component pattern including an error component of 0.5. (F) is a conceptual diagram showing another horizontal component pattern including an error component of 0.5. (G) is a conceptual diagram showing another horizontal component pattern including an error component of 0.5. (H) is a conceptual diagram showing another horizontal component pattern including an error component of 0.5. (I) is a conceptual diagram showing a horizontal horizontal component pattern. (J) is a conceptual diagram showing another horizontal horizontal component pattern. (K) A conceptual diagram showing a horizontal component pattern including one background pixel. (L) It is a key map showing other horizontal component patterns containing one background pixel.

FIG. 3 is a conceptual diagram for explaining a vertical component pattern. (A) is a conceptual diagram showing a vertical component pattern including an error component of 0.5. (B) is a conceptual diagram showing another vertical component pattern including an error component of 0.5. (C) It is a key map showing other longitudinal component patterns containing an error component of 0.5. (D) is a conceptual diagram showing another vertical component pattern including an error component of 0.5. (E) is a conceptual diagram showing another vertical component pattern including an error component of 0.5. (F) is a conceptual diagram showing another vertical component pattern including an error component of 0.5. (G) is a conceptual diagram showing another vertical component pattern including an error component of 0.5. (H) is a conceptual diagram showing another vertical component pattern including an error component of 0.5. (I) A conceptual diagram showing a vertical component pattern. (J) is a conceptual diagram showing another vertical component pattern. (K) A conceptual diagram showing a vertical component pattern including one background pixel. (L) It is a key map showing other longitudinal component patterns containing one background pixel.

FIG. 4 is a conceptual diagram for explaining a horizontally written original. (A) is a conceptual diagram showing an example of a horizontally written original. FIG. 3B is a conceptual diagram illustrating a horizontally expanded figure of a horizontally written document. (C) is a conceptual diagram showing an image in which the outline of an expanded figure of a horizontally written original is extracted. (D) is a conceptual diagram showing a state in which a vertical component of an outline of an expanded figure of a horizontally written original has been removed. (E) is a conceptual diagram showing a state in which a vertical component of an outline of an expanded figure of a horizontally written original has been removed and further reduced. (F) is a conceptual diagram showing a state in which a vertical component of an outline of an expanded figure of a horizontally written original has been removed, and minute noise has been removed from the reduced figure.

FIG. 5 is a conceptual diagram for explaining a vertically written original. (A) is a conceptual diagram showing an example of a vertically written original. (B) is a conceptual diagram showing a vertically expanded figure of a vertically written document. FIG. 3C is a conceptual diagram illustrating an image in which the contour of an expanded figure of a vertically written document is extracted. (D) is a conceptual diagram showing a state in which a horizontal component of an outline of an expanded figure of a vertically written document is removed. (E) is a conceptual diagram showing a state in which a horizontal component of an outline of an expanded figure of a vertically written original has been removed and further reduced. (F) is a conceptual diagram showing a state in which a horizontal component of an outline of an expanded figure of a vertically written original document is removed, and minute noise is removed from the reduced figure.

FIG. 6 is a conceptual diagram for explaining a horizontal pattern. (A) is a conceptual diagram showing an example of a horizontal pattern. (B) is a conceptual diagram showing a pattern including an error component of 0.5 with respect to a horizontal pattern. (C) is a conceptual diagram showing another pattern including an error component of 0.5 with respect to the horizontal pattern. (D) is a conceptual diagram showing still another pattern that includes an error component of 0.5 with respect to the horizontal pattern. (E) is a conceptual diagram showing still another pattern that includes an error component of 0.5 with respect to the horizontal pattern. (F) It is a key map showing the pattern of a right descent. (G) is a conceptual diagram showing an upward-sloping pattern. (H) It is a key map showing the pattern which changes from right descent to right descent. (I) is a conceptual diagram showing a pattern that changes from rising right to falling right.

FIG. 7 is a conceptual diagram for explaining a vertical pattern. (A) is a conceptual diagram showing an example of a vertical pattern. (B) is a conceptual diagram showing a pattern including an error component of 0.5 with respect to a vertical pattern. (C) is a conceptual diagram showing another pattern including an error component of 0.5 with respect to the vertical pattern. (D) is a conceptual diagram showing still another pattern that includes an error component of 0.5 with respect to the vertical pattern. (E) is a conceptual diagram showing still another pattern that includes an error component of 0.5 with respect to the vertical pattern. (F) It is a key map showing the pattern of a right descent. (G) is a conceptual diagram showing an upward-sloping pattern. (H) It is a key map showing the pattern which changes from left descent to right descent. (I) It is a conceptual diagram showing the pattern which changes from right descent to left descent.

FIG. 8 is a flowchart of steps 1-14 to 1-25.

FIG. 9 is a conceptual diagram showing a pattern for degeneration processing.

──────────────────────────────────────────────────続 き Continued on front page (58) Field surveyed (Int.Cl. ⁷ , DB name) G06K 9/32 H04N 1/387

Claims (6)

(57) [Claims] 1. An original image of a character document is sequentially expanded, an optimum expansion number is obtained by detecting a decrease in expansion amount of a graphic area before and after expansion, and an image is generated by expanding the original image by the optimum expansion number. Then, in the generated image, the vertical and horizontal components of each part of the figure contour are calculated to determine which of the vertical and horizontal components is larger, and only in the direction of the larger component, the original image is expanded a predetermined number of times based on the optimal expansion number. The contour image of the image obtained by the dilation is extracted, and a smaller component is removed from the vertical and horizontal components of the contour image obtained by the extraction, and the inclination is determined only by the larger component , which is the main component. A method for correcting the inclination of a character document, comprising calculating and rotating the original image so as to eliminate the inclination. 2. The slope is the slope of the main component.
Multiple convolution patterns to seek
To the number of matches,
Result of multiplying a preset error component coefficient
To obtain the slope of the main component.
Matches each of multiple convolution patterns
2. The method according to claim 1, wherein the inclination is obtained by dividing the total number of the originals. 3. The method according to claim 2, wherein the convolution has a size of 3 × 3 pixels. 4. The method according to claim 2, wherein the main component is horizontal or vertical. 5. A method for determining which of the vertical and horizontal components is larger,
When there are many vertical components, the pixels of the vertical component are deleted.
Wherein the pixel of the horizontal component is erased.
1. The method for correcting the inclination of a character document according to 1. 6. After erasing a small number of components, a predetermined number of times
6. The character source according to claim 5, wherein degeneration is performed.
Manuscript tilt correction method.