WO2018086233A1

WO2018086233A1 - Character segmentation method and device, and element detection method and device

Info

Publication number: WO2018086233A1
Application number: PCT/CN2016/113632
Authority: WO
Inventors: 李红匣
Original assignee: 广州视源电子科技股份有限公司
Priority date: 2016-11-08
Filing date: 2016-12-30
Publication date: 2018-05-17
Also published as: CN106599896A

Abstract

Provided is a character segmentation method, comprising: obtaining a character image (S11); segmenting to obtain respective rows of characters on the basis of the number of pixels having gray values within a preset range in each row of pixels in the character image, so as to obtain a plurality of character row images (S12); and segmenting to obtain respective single characters on the basis of a number of pixels having gray values within a preset range in each column of pixels in each of the character row images, so as to obtain a plurality of single character regions (S13). The character segmentation method features a simple algorithm, and high segmentation efficiency and accuracy. A character segmentation device is used to obtain a single character region by means of segmentation. An element detection method and device are used to perform segmentation to obtain a character by using the character segmentation method when detecting an element having a surface printed with the character, so as to realize identification of the printed character. The element detection method has high detection efficiency.

Description

Character segmentation method and device, and component detection method and device

Technical field

The present invention relates to the field of character recognition technology, and in particular, to a character segmentation method and apparatus, and a component detection method and apparatus.

Background technique

In the actual production process, each circuit board usually includes a variety of components, and each component, such as resistors, capacitors, etc., can have many different models. Sometimes, different types of components of the same type can be distinguished by the appearance characteristics of the components, such as shape, color, size, and the like. However, sometimes it is difficult to distinguish the different models of components by just the appearance information. Typically, the factory prints component information on the surface of the component to distinguish between different models. Therefore, the component can be detected by the character recognition system.

The character recognition system generally includes three parts: character extraction, character segmentation, and character recognition. Character segmentation is an important step in the character recognition system. The effect of character segmentation directly affects the accuracy of character recognition and is related to the feasibility of the entire character recognition system.

At present, the commonly used character segmentation methods follow image segmentation methods, such as threshold-based segmentation algorithms, edge-based segmentation methods, region-based segmentation methods, and the like.

However, the existing character segmentation method has the following drawbacks:

1. The noise removal effect is poor, that is, many of the divided character regions do not actually contain characters;

2. The calculation method is complicated and the character segmentation efficiency is low.

Summary of the invention

The purpose of the embodiment of the present invention is to provide a character segmentation method, which can realize effective segmentation of characters when input character images, and the calculation is simple.

To achieve the above object, an embodiment of the present invention provides a character segmentation method, including

Get a character image;

Dividing each line of characters based on the number of pixels in each row of pixels in the character image in a preset range, thereby obtaining segmented character line images;

Each of the single characters is divided based on the number of pixels in the column of each of the character line images in which the gray value is within a preset range, thereby obtaining the divided single character regions.

Compared with the prior art, a character segmentation method disclosed in the present invention separates the number of pixels in the preset range based on each row of pixels and each column of pixels in the image. Character line and single-character technical solution; this method is based on the characteristics of the character itself, using the characteristics of the character area in the character image that are different from the gray values of other areas, segmenting the line character image, and The single character is divided on the basis of the line character image, and the pair of characters can be effectively segmented; the algorithm for calculating the number of pixels is simple, and the problem of complicated calculation and low partitioning efficiency in the prior art is solved.

When used for component fault detection, it can quickly and effectively segment the printed characters on the components, improving the efficiency and accuracy of detection.

Further, the character segmentation method further includes:

The obtained sticky characters existing in each of the single-character regions are detected, and the sticky characters are divided to obtain a final single-character region.

As an improvement of the above solution, the character segmentation method further includes detecting and segmenting the sticky characters, reducing the influence of noise, and improving the segmentation accuracy.

Further, the segmentation of each line of characters based on the number of pixels in the grayscale value of each row of pixels in the character image in the preset range, thereby obtaining the segmented character line images includes:

Performing horizontal projection on the character image, respectively calculating the number of pixel points in the pixel range of each row of pixels in a preset range, and acquiring a row distribution of the pixel points whose gray value is within a preset range Histogram curve

Using a Gaussian function to fit the line distribution histogram curve to determine the position of each line of characters;

Each line of characters is divided based on the position of each line of characters, thereby obtaining a plurality of divided line image lines.

As an improvement of the above solution, the acquired input character image usually contains a part of noise. In order to initially determine the approximate position of the character line, firstly, the gray value in each line of pixels in the character image is obtained within a preset range. The line distribution histogram curve of the number of pixels, and then using the similarity of the curve of the region where each single character row in the line distribution histogram is similar to the Gaussian function, the Gaussian function is used to curve fit the line distribution histogram, and then obtain The bit of the character line. The improvement can effectively avoid the influence of noise, the algorithm is simple, and the segmentation accuracy is high.

Further, the segmentation is performed by dividing each single character based on the number of pixels in each column of each character line image in which the gray value is within a preset range, thereby obtaining the divided single characters. The area includes:

Performing vertical projection on the character line image, respectively calculating the number of pixels in the column of each of the pixels in the preset range, and acquiring the pixel points in the preset range Column distribution histogram curve;

The column distribution histogram curve is sequentially scanned according to a preset order, and each of the characters is divided by a pixel dot column whose number of pixels is zero in a preset range, thereby obtaining a plurality of divided characters. The single character area.

As an improvement of the above solution, when a certain pixel of the image does not have a pixel point whose gray value is within a preset range, the position can be regarded as a character division point, and the division efficiency is high.

Further, the method of dividing the glued characters is a drip algorithm.

As an improvement of the above scheme, the dripping algorithm simulates the process of water droplets dropping from a high point to a low point, and the trajectory of the water droplets constitutes a segmentation path of characters, and the segmentation effect of the drip algorithm is good, and the noise effect is effectively removed.

Further, the acquired character image is a binarized image, and the pixel value whose gray value is within a preset range is a gray value of 225. Pixels.

As an improvement of the above scheme, in order to improve the accuracy of line division, the number of character lines included in the character image may be input in advance.

In order to achieve the object of the present invention, the present invention provides a character segmentation apparatus, including:

a character image obtaining unit, configured to acquire a character image;

a character line dividing unit, configured to divide each line of characters based on the number of pixels of the gray level value in each row of pixel points in the character image, thereby obtaining the divided character line images;

a character dividing unit, configured to divide each single character based on the number of pixels in the column of each of the character line images in the preset grayscale value, thereby obtaining the divided Several single-character areas.

Compared with the prior art, a character segmentation apparatus disclosed by the present invention first calculates, by a character line segmentation unit, the number of pixel points in the pixel range of the input character image in the preset range. According to the characteristic structure of the character line, the segmentation obtains the character line image; and the character segmentation unit is used to calculate the number of the gray value in the column pixel of the character line image in the preset range of pixels, based on the characteristic structure of each single character, Segmentation acquires each single character; the device is simple to calculate and has high segmentation efficiency.

Further, the character segmentation device further includes:

The glue character segmentation unit is configured to detect the stuck characters existing in the acquired plurality of single character regions, and divide the glue characters to obtain a final single character region.

Further, the character line segmentation unit includes:

a first calculating module, configured to perform horizontal projection on the character image, respectively calculate a number of pixel points in the pixel range of each row of pixels in a preset range, and obtain the gray value in a preset a line distribution histogram curve of the pixel points in the range; fitting the line distribution histogram curve by a Gaussian function to determine the position of each line character;

The character line segmentation module divides each line of characters based on the position of each line of characters to obtain a plurality of character line images.

Further, the character segmentation unit includes:

a second calculating module, configured to vertically project the character line graph, and calculate a number of pixel points in the column of each column of pixels in a preset range, and obtain the gray value in the pre-predetermined range a column distribution histogram curve of the pixel points in the range; sequentially scanning a column distribution histogram curve of the pixel points whose gray value is within a preset range according to a preset order, thereby obtaining the gray value in a preset range a pixel dot column with zero pixel counts;

The single-character segmentation module is configured to divide each single character based on the obtained pixel point sequence in which the gray value is zero in the preset range, thereby obtaining the divided single-character regions.

Based on the character segmentation method disclosed by the present invention, the present invention further provides a component detection method, including:

Acquiring an image of the component to be detected, wherein the image of the component to be detected includes a character image of a printed character of the component to be detected;

Obtaining the character image;

And dividing each single character according to the number of pixels in each column of pixels in each of the character line images in the preset range, thereby obtaining the divided single character regions;

Character recognition is performed on the character image based on the acquired single character regions, thereby acquiring information of the printed characters of the component to be detected.

Compared with the prior art, a component detecting method disclosed by the present invention identifies a component based on printed character information on the component, including three steps of character extraction, character segmentation and character recognition; wherein, a method disclosed by the present invention is adopted. The character segmentation method improves the segmentation efficiency and accuracy of the printed characters on the components in component detection, and improves the accuracy of character recognition due to effective segmentation; ultimately, the efficiency and accuracy of component detection in the present technical solution are improved as a whole.

The invention also provides a component detecting device, comprising:

The image to be detected image acquiring unit is configured to acquire an image of the component to be detected, wherein the image of the component to be detected includes a character image of a printed character of the component to be detected;

a character image obtaining unit, configured to acquire a character image;

a character dividing unit, configured to divide each single character based on the number of pixels in the column of each of the character line images in the preset grayscale value, thereby obtaining the divided And a plurality of single-character regions; the component to be detected information acquiring unit is configured to perform character recognition on the printed character image based on the acquired plurality of the single-character regions, thereby acquiring information of the printed characters of the component to be detected.

Compared with the prior art, a component detecting device disclosed in the present invention acquires an image of a component to be detected by an image acquiring unit to be detected, and then acquires a character image in an image of the component to be detected through a character image acquiring unit, and then The character segmentation unit and the character segmentation unit are sequentially segmented and single-characterized to obtain a plurality of single-character regions, and finally, the component information acquisition unit performs component information identification based on the acquired single-character regions, wherein Since the character segmentation method can effectively segment the characters, the segmentation efficiency and accuracy of the printed characters on the components in the component detection are improved, and the accuracy of the character recognition is improved by the effective segmentation; Technical solution component efficiency and accuracy.

DRAWINGS

1 is a schematic flow chart of Embodiment 1 of a character segmentation method according to the present invention;

2 is a schematic flowchart of step S12 of Embodiment 1 of the character segmentation method provided by the present invention;

3 is a schematic flowchart of step S13 of Embodiment 1 of the character segmentation method provided by the present invention;

4 is a schematic flow chart of Embodiment 2 of a character segmentation method according to the present invention;

5 is a schematic flowchart of step S22 of Embodiment 2 of the character segmentation method provided by the present invention;

6 is a schematic flowchart of step S23 of Embodiment 2 of the character segmentation method provided by the present invention;

7 is an exemplary diagram of an acquired character image;

Figure 8 is a horizontal projection view of an exemplary image of the character image of Figure 7;

9 is a schematic diagram of fitting a line distribution histogram curve obtained by horizontal projection in FIG. 8 by using a Gaussian function;

Figure 10 is a vertical projection view of an example of a character line image obtained from the character image diagram of Figure 7;

11 is a schematic diagram of single character division of an example of a character line image in FIG. 10;

Figure 12 is a diagram showing an example of the presence of glue characters in a divided single-character area;

FIG. 13(a) is a diagram showing an example of a number of pixel positions of water droplets in a dripping algorithm used in step S24 of the second embodiment of the present invention;

FIG. 13(b) is a schematic diagram showing the rule of the drop position of the water drop in the dripping algorithm used in step S24 of the second embodiment provided by the character segmentation method of the present invention;

Figure 14 is a block diagram showing the structure of an embodiment of a character segmentation apparatus according to the present invention;

Figure 15 is a flow chart showing an embodiment of a component detecting method according to the present invention;

Figure 16 is a block diagram showing the construction of an embodiment of a component detecting device of the present invention.

detailed description

The technical solutions in the embodiments of the present invention are clearly and completely described in the following with reference to the accompanying drawings in the embodiments of the present invention. It is obvious that the described embodiments are only a part of the embodiments of the present invention, but not all embodiments. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative efforts are within the scope of the present invention.

FIG. 1 is a schematic flowchart of a first embodiment of a character segmentation method according to the present invention. The first embodiment includes the following steps:

S11. Acquire a character image.

Referring to FIG. 7, FIG. 7 is an exemplary diagram of acquired character images;

S12. Dividing each line of characters based on the number of pixels in the pixel in each line of the pixel image in the preset range, thereby obtaining the divided number of character line images;

S13. Divide each single character based on the number of pixels in each column of each character row image in which the gray value is within a preset range, thereby obtaining the divided single character regions.

The number of pixels in the grayscale value in the preset range in the step S12/step S13 is the number of pixels corresponding to the character region in each row/column of pixels, and the specific implementation time is The preset range of the set gradation value is specifically set according to the gradation value range of the pixel point representing the character in the character image.

Referring to FIG. 2, FIG. 2 is a schematic flowchart of step S12 of the first embodiment, and step S12 includes:

S121. Perform horizontal projection on the character image, calculate a number of pixel points in which the gray value in each row of pixels is within a preset range, and obtain a line distribution histogram curve of the pixel point whose gray value is within a preset range;

Referring to FIG. 8, FIG. 8 is a horizontal projection view of the acquired character image. Step S121 is described in detail with reference to FIG. 8. The character image in FIG. 8 contains a part of noise. In order to initially locate the approximate position of the character line image, the input is first required. The character image is horizontally projected, and the number of pixels in which the gray value in each row of pixels is within a preset range is calculated, thereby obtaining a line distribution histogram. The character image contains two character lines, and the horizontally projected line distribution histogram corresponding to the obtained line distribution histogram presents two peaks with larger peak values, and is similar to the Gaussian function.

S122. Perform a fitting process on the line distribution histogram curve by using a Gaussian function to determine the position of each line of characters;

Since the curve corresponding to each character line in the histogram is similar to the Gaussian function; therefore, the histogram curve can be fitted by a Gaussian function; see Fig. 9, which is obtained by horizontal projection in Fig. 8 using a Gaussian function. The line distribution histogram curve is fitted to the schematic diagram; the position of each line of the character image is determined according to the fitting result.

S123. Divide each line of characters based on the position of each line of characters, thereby obtaining the divided number of character line images.

Referring to FIG. 3, FIG. 3 is a schematic flowchart of step S13 in the first embodiment, and step S13 includes:

S131. Perform vertical projection on the character line image, respectively calculate the number of pixel points in the pixel range of each column of pixels in a preset range, and obtain a column distribution histogram of the pixel points whose gray value is within a preset range. curve;

S132. Scan the column distribution histogram curve in sequence according to a preset order to obtain a pixel point column in which the number of pixels of the gray value in the preset range is zero;

Referring to FIG. 10, FIG. 10 is a vertical projection view of an example of a character line image obtained from the character image example of FIG. 7. It can be seen from FIG. 10 that the boundary position between every two single characters is on the column distribution histogram curve. The number of pixels in which the gray value obtained at the corresponding position is within the preset range is zero. That is to say, when a column in the character line image does not have a pixel whose gray value is within a preset range, the column position can be considered as a single-character split column. Scanning the column distribution histogram curve in the preset order to scan the obtained pixel point column whose number of pixels in the preset range is zero;

S133. Divide each character with a pixel dot column whose gray value is zero in a preset range, thereby obtaining a plurality of divided single character regions.

Referring to FIG. 11, each single character is divided according to the acquired pixel columns, thereby acquiring a plurality of single character regions.

In the specific implementation, the histogram curve is firstly distributed by the line value of the pixel value of the character image in the preset range, and the position of the character line is determined by fitting with the Gaussian function, and the character image is segmented, thereby obtaining each a character line image; then, the histogram curve is distributed in a column of the number of pixels in the preset range by the gray value of each character line image, and the number of pixels in the preset range is zero. The pixel column performs a one-character split for each character line image.

In this embodiment, based on the feature of the number of pixels representing characters in the character image, the character line and the single character are sequentially divided, and the pair of characters can be effectively segmented; the algorithm for calculating the number of pixels is simple, and the calculation of the prior art is complicated, and the segmentation efficiency is low. The problem.

Referring to FIG. 4, it is a flow chart of the second embodiment of the present invention. The second embodiment includes the following steps:

S21. Acquire a character image.

Similarly, referring to FIG. 7, FIG. 7 is an exemplary diagram of the acquired character image;

Preferably, in the second embodiment, the input character image is obtained as a character image subjected to binarization processing. After the binarization process, if the gray point value of the pixel in the extracted character region is 225, that is, the black pixel point, the gray value of the pixel in the remaining region is 0; in the second embodiment, the number of black pixel points is used. The character area is recognized as an example.

S22. Dividing each line of characters based on the number of black pixel points in each row of pixels in the character image, thereby obtaining segmented character line images;

S23. Divide each single character according to the number of black pixel points in each column of pixels in each character line image, thereby obtaining a plurality of divided single character regions;

In the second embodiment, the input image is taken as a binarized image as an example. In step S22/step S23, the number of black pixel points in each row/column of pixels is obtained to obtain corresponding rows/ The number of pixels representing the character area in each column of pixels.

S24. Detect the adhesion characters existing in each single character area obtained, and divide the glue characters to obtain a final single character area.

Referring to FIG. 5, FIG. 5 is a schematic flowchart of step S22 of the second embodiment, where step S22 includes:

S221: Perform horizontal projection on the character image, respectively calculate the number of pixels in each row of pixels with the gray value within a preset range, and obtain a line distribution histogram curve of the pixel points whose gray value is within the preset range;

Similarly, referring to FIG. 8, FIG. 8 is a horizontal projection view of the acquired character image, and step S221 is described in detail with reference to FIG. 8. The character image in FIG. 8 includes a part of noise. In order to initially locate the approximate position of the character line image, firstly, The input character image needs to be horizontally projected, and the number of black pixel points in each row of pixels is calculated, thereby obtaining a line distribution histogram. The character image contains two character lines, and the horizontally projected line distribution histogram corresponding to the obtained line distribution histogram presents two peaks with larger peak values, and is similar to the Gaussian function.

S222. Perform a fitting process on the line distribution histogram curve by using a Gaussian function to determine the position of each line of characters;

S223. Divide each line of characters based on the position of each line of characters, thereby obtaining the divided number of character line images.

Referring to FIG. 6, FIG. 6 is a schematic flowchart of step S23 of the first embodiment, where step S23 includes:

S231, performing vertical projection on the character line image, respectively calculating the number of black pixel points in each column of pixels, and obtaining a column distribution histogram curve of the pixel points whose gray value is within a preset range;

S232. Scan the column distribution histogram curve in order according to a preset order to obtain a pixel point column with zero black pixel points;

Referring to FIG. 10, FIG. 10 is a vertical projection view of an example of a character line image obtained from the character image example of FIG. 7. It can be seen from FIG. 10 that the boundary position between every two single characters is on the column distribution histogram curve. Black pixels acquired at corresponding positions The number of points is zero. That is to say, when there is no black pixel in a column in the character line image, the column position can be considered as a single-character split column. Scanning the column distribution histogram curve in the preset order to scan the obtained pixel point column whose number of pixels in the preset range is zero;

S233, dividing each character by a pixel dot column having a black pixel number of zero, thereby obtaining a plurality of divided single character regions;

Preferably, the character image acquired in step S21 in the second embodiment may be a character image processed by a character extraction algorithm; the character extraction algorithm refers to an algorithm for extracting a character region, such as template matching, stroke width transformation (SWT), and MSER And other methods. The non-character area is removed from the character image processed by the character extraction algorithm, and the character area is reserved.

In addition, due to the presence of noise, there may be sticking between the two single characters, so that there may be two characters in the single-character area obtained in step S23. Referring to FIG. 12, FIG. 12 is a sticky character in the divided single-character area. For example, the character "J" and the character "X" are still stuck together after being divided by the step S23 due to noise, and are in the same single character region; the existence of the sticky character affects the segmentation validity of the embodiment. . In order to achieve effective segmentation, step S24 detects and segments the possible sticky characters. Preferably, in the second embodiment, the drip algorithm is used to obtain the segmentation path between the glued characters, and the glued characters are segmented based on the segmentation path.

Specifically, the drip algorithm divides the glue characters by simulating the process of water droplets dropping from a high point to a low point: when the water droplets from the top of the character due to gravity, they will descend downward or horizontally along the outline of the character; When the water droplets are trapped in the concave portion of the character outline, they will penetrate into the stroke of the character and continue to be low; the trajectory through which the water droplet passes constitutes the segmentation path of the character.

Referring to FIG. 13, FIG. 13(a) is a numbering example diagram of the pixel position of the dripping algorithm of the dripping algorithm, assuming that the position of the pixel where the water droplet is currently located is represented by n ₀ , and the position of the pixel where the water drop is dropped next time is The five droplets around the pixel are determined. Fig. 13(b) lists six cases in which five pixel points around the water drop may occur and the position where the water drop is next; wherein w represents a white pixel point, b represents a black pixel point, and * indicates that it may be white Pixels may also be black pixels, and arrows indicate the trajectory of water droplets. For example, when the neighboring five pixel points of the current pixel position of the water drop are all white dots or all black dots, the water drops downward. The dripping path of the water droplets can be obtained by the following calculation process:

For the stuck character image to be segmented, the coordinates of the pixel position where the water drop is currently located are represented as (x _i , y _i ), and the drop path of the water drop is T, then T(x _i+1 , y _i+1 )=f(x _i , y _i , W _i ), i=0, 1, ...; where (x _i+1 , y _i+1 ) represents the coordinates of the next drop of the droplet at the pixel position, and W _i represents the water droplet at the current position On the gravitational potential energy, the gravitational potential energy W _i is calculated by the following formula:

among them,

z _j represents the pixel value of n _j point, specifically, if n _j point is a black pixel point, z _j =0, if n _j point is a white pixel point, z _j =1; ω _j indicates that n _j point is Select the weight of the next drop point of the water drop, and ω _j =6-j. Then, the position where the water drops a little is:

The glued characters are divided according to the obtained water drop dripping path, thereby obtaining the final several single character regions.

In the specific implementation, firstly, the histogram curve is distributed through the line of the black pixel points of the character image, the position of the character line is determined at the fitting with the Gaussian function, and the character image is segmented to obtain the image of each character line; a histogram curve of a column of black pixel points of each character line image, and a single character segmentation of each character line image with a pixel column whose gray value is zero in a preset range of pixels Each single character region is obtained; and further, the glue character is used to divide the glue characters in the single character region to obtain the final single character region.

In this embodiment, based on the feature of the number of pixels representing characters in the character image, the character lines and the single characters are sequentially divided, and the pair of characters can be effectively segmented; and the glue characters are divided by the drip algorithm; the algorithm for calculating the number of pixels is simple, and the present solution is solved. There is a problem of complicated technical calculation and low partitioning efficiency; and the character line is obtained by fitting the Gaussian function, and the glue characters are divided by the dripping algorithm, the noise interference is greatly reduced, and the accuracy of the segmented characters is improved.

An embodiment of a character segmentation apparatus according to the present invention is shown in FIG. 14. FIG. 14 is a schematic structural diagram of an embodiment of a character segmentation apparatus according to the present invention. The apparatus of the embodiment includes a character image acquisition unit 11 and a character line division unit 12. And the character dividing unit 13, specifically:

a character image obtaining unit 11 configured to acquire a character image;

The character line dividing unit 12 is configured to divide each line of characters based on the number of pixels in the preset range of the gray value in each line of the pixel image, thereby obtaining the divided character line images;

The character dividing unit 13 is configured to divide each single character based on the number of pixels in each column of each character row image in which the gray value is within a preset range, thereby obtaining the divided single characters. region.

The character line dividing unit 12 includes a first calculating module 121 and a character line dividing module 122, specifically:

The first calculation module 121 is configured to perform horizontal projection on the character image, respectively calculate the number of pixel points in the pixel range of each row of pixels in the preset range, and obtain the pixel points whose gray value is within the preset range. The line distribution histogram curve; the Gaussian function is used to fit the line distribution histogram curve to determine the position of each line of characters;

The character line segmentation module 122 divides each line of characters based on the position of each line of characters, thereby acquiring a plurality of character line images.

The character dividing unit 13 includes a second calculating module 131 and a single character dividing module 132, specifically:

The second calculating module 131 is configured to vertically project the character line graph, and calculate the gray value in each column of pixels respectively in the preset range. The number of pixels surrounding the circle, obtains a histogram curve of the column distribution of the pixel points whose gray value is within the preset range; sequentially scans the column distribution histogram curve of the pixel points whose gray value is within the preset range according to the preset order , thereby obtaining a pixel point column in which the number of pixels of the gray value in the preset range is zero;

The single-character segmentation module 132 is configured to divide each single character according to the obtained pixel point sequence in which the acquired gray value is zero in the preset range, thereby obtaining the divided single-character regions.

Wherein, the number of pixels in the grayscale value set in the character segmentation device in the preset range is the number of pixels representing the character region in each row/column of pixels in the image, In the specific implementation, the preset range of the gradation value is set according to the gradation value range indicating the character pixel point. The process of acquiring the character image by the character image acquiring unit 11 in the present embodiment preferably includes obtaining the character image by the process of the character extraction algorithm and the image binarization, and the gray value of the pixel in the character region on the character image is 225, that is, When the black pixel is used, the character dividing device sets the pixel whose gray value is within the preset range as a black pixel.

The embodiment of the character segmentation apparatus provided by the present invention further includes an adhesion character segmentation unit 14 for detecting the adhesion characters existing in the acquired single character regions and dividing the adhesion characters to obtain the final single character region. .

The method for obtaining the segmentation path between the spliced characters is performed by using the drips algorithm. The specific calculation process may refer to the specific process of step S24 of the second embodiment provided by the character segmentation method of the present invention. Do not repeat them.

In a specific implementation, first, the character image acquiring unit 11 first acquires the input character image; then, the first calculating module 121 in the character line dividing unit 12 calculates the gray value in each row of the input character image in the preset range. The number of pixels within the line is obtained, the line distribution histogram curve is obtained, the line distribution histogram curve is curve-fitted, the character line position is determined, and the character line segmentation module 122 performs character line segmentation to obtain the character line image; then, the character segmentation The second calculating module 131 in the unit 13 calculates the number of pixels in the column pixel of the character line image in the preset range to determine that the number of pixels in the preset range is zero. The pixel sequence column; the single character segmentation module 132 obtains each single character according to the pixel column division; finally, the glue character segmentation unit 14 divides the glue characters in the single character region by the drip algorithm to obtain the final single character region.

The character segmentation device of the embodiment has a simple algorithm, does not require high hardware, and is beneficial to reduce cost; at the same time, the calculation is fast, and the segmentation efficiency is improved; and the segmentation accuracy is high, which is beneficial to the character recognition system when the character is recognized. Identify the effect.

According to the first embodiment/second embodiment provided by the character segmentation method of the present invention, the present invention further provides an embodiment of the component detecting method: obtaining the printed character information of the component by identifying the printed character on the component, including the component Information such as model and parameters to achieve the purpose of detecting the component. Referring to FIG. 15, FIG. 15 is a schematic flowchart of the embodiment, which specifically includes the following steps:

S31. Acquire an image of the component to be detected, where the image of the component to be detected includes a character image of a printed character of the component to be detected;

S32. Acquire a character image.

S33. Dividing each line of characters based on the number of pixels in the pixel in each line of the pixel image in the preset range, thereby obtaining the divided number of character line images;

S34. Segmentation based on the number of pixels in each column of pixels in each character row image in a preset range Each single character, thereby obtaining a plurality of single character regions after division;

S35. Perform character recognition on the printed character image based on the acquired single character regions, thereby acquiring information of the printed characters of the component to be detected.

The character image acquired in step S32 may refer to the character image example diagram shown in FIG. 7;

Preferably, the process of extracting the character image acquired from the image of the component to be detected in step S32 includes extracting characters, which may be processed by using a following character extraction algorithm: template matching, stroke width conversion (SWT), MSER, and the like. The non-character area is removed on the printed character image processed by the character extraction algorithm, and the printed character area on the component to be detected is retained;

Preferably, in step S32, in the process of acquiring the character image of the printed character of the image of the component to be detected, a binarization processing step of the image is further included, and the finally obtained character image is a binarized image.

Specifically, the process of segmenting the character image in the step S33 and the step S34 to obtain a plurality of single-character regions may refer to the specific implementation process of the first embodiment/second embodiment of the character segmentation method of the present invention, and details are not described herein.

Specifically, in step S35, based on the acquired single-character regions, each single-character region can be correspondingly identified, thereby reading the meaning of the character corresponding to the single character, thereby acquiring the printed character information of the component to be detected, thereby implementing the component. Detecting; or, in step S35, based on the acquired single-character regions, the template text corresponding matching is performed, and then the printed character information matched by the component to be detected is acquired, thereby realizing component detection.

In a specific implementation, the image of the to-be-detected component including the printed characters on the component to be detected is acquired, and the character image of the printed character is obtained therefrom; the character image is sequentially segmented and single-divided to obtain a plurality of single-character regions; and based on the acquisition Several single-character areas identify printed characters and acquire component information for component detection.

The embodiment of the component detecting method provided by the present invention recognizes that the detecting accuracy of the component is higher based on the printed character information on the component than detecting the identifying component from information such as the size, color, and appearance of the component; In the detection, the segmentation efficiency and accuracy of the printed characters on the component are improved, and the accuracy of the character recognition is improved due to the effective segmentation; finally, the efficiency and accuracy of the component detection in the embodiment are improved as a whole.

Correspondingly, the present invention further provides an embodiment of the component detecting device. Referring to FIG. 16, FIG. 16 is a schematic structural diagram of the embodiment.

The image to be detected image acquisition unit 10 is configured to acquire an image of the component to be detected, wherein the image of the component to be detected includes a character image of the printed character of the component to be detected;

a character image obtaining unit 11; configured to acquire a character image;

The character line dividing unit 12 is configured to divide each line character based on the number of pixels in the pixel value of each line of the character image in the preset range, thereby obtaining the divided character line images. ;

a character dividing unit 13 configured to calculate, according to a pixel point in a preset range, a gray value in each column of pixels in each character line image In the case of a number of cases, each single character is divided to obtain a plurality of single character regions after division;

The to-be-detected component information acquiring unit 15 is configured to perform character recognition on the printed character image based on the acquired plurality of single-character regions, thereby acquiring information of the printed characters of the to-be-detected component.

In a specific implementation, first, the image of the component to be detected is acquired by the image to be detected by the image to be detected 10; then, the image of the component to be detected is extracted by the printed character image extracting unit 11; then, the character row dividing unit 12 and the character dividing unit are sequentially passed through 13 to obtain a plurality of single-character regions; finally, the component information acquisition unit 15 performs component information identification based on the acquired plurality of single-character regions, thereby implementing detection of the component to be detected.

The embodiment improves the segmentation efficiency and accuracy of the printed characters on the component in the component detection, and improves the accuracy of the character recognition due to the effective segmentation; finally, the efficiency and accuracy of the component detection in the embodiment are improved as a whole.

The above is a preferred embodiment of the present invention, and it should be noted that those skilled in the art can also make several improvements and retouchings without departing from the principles of the present invention. It is the scope of protection of the present invention.

Claims

A character segmentation method, comprising:

Get a character image;

Dividing each line of characters based on the number of pixels in each row of pixels in the character image in a preset range, thereby obtaining segmented character line images;

Each of the single characters is divided based on the number of pixels in the column of each of the character line images in which the gray value is within a preset range, thereby obtaining the divided single character regions.
The character segmentation method according to claim 1, wherein the character segmentation method further comprises:

The obtained sticky characters existing in each of the single-character regions are detected, and the sticky characters are divided to obtain a final single-character region.
The character segmentation method according to claim 1, wherein the segmentation is performed based on the number of pixels in which the gray value in each row of pixels in the character image is within a preset range The characters, and thus the segmented character line images, include:

Performing horizontal projection on the character image, respectively calculating the number of pixel points in the pixel range of each row of pixels in a preset range, and acquiring a row distribution of the pixel points whose gray value is within a preset range Histogram curve

Using a Gaussian function to fit the line distribution histogram curve to determine the position of each line of characters;

Each line of characters is divided based on the position of each line of characters, thereby obtaining a plurality of divided line image lines.
The character segmentation method according to claim 1, wherein the number of pixels in each of the column of pixels in each of the character line images is within a preset range, Dividing each single character to obtain a number of divided single-character regions includes:

Performing vertical projection on the character line image, respectively calculating the number of pixel points in the pixel range of each column of pixels in a preset range, and obtaining a column distribution of the pixel points whose gray value is within a preset range Histogram curve

The column distribution histogram curve is sequentially scanned according to a preset order, and each of the characters is divided by a pixel dot column whose number of pixels is zero in a preset range, thereby obtaining a plurality of divided characters. The single character area.
A character segmentation method according to claim 2, wherein said method of dividing said glue characters is dripping algorithm.
The character segmentation method according to claim 1, wherein the acquired character image is a binarized image, and the pixel whose gray value is within a preset range is a pixel having a gray value of 225. point.
A character segmentation device, comprising:

a character image obtaining unit, configured to acquire a character image;

a character line dividing unit, configured to divide each line of characters based on the number of pixels of the gray level value in each row of pixel points in the character image, thereby obtaining the divided character line images;

a character dividing unit, configured to divide each single character based on the number of pixels in the column of each of the character line images in the preset grayscale value, thereby obtaining the divided Several single-character areas.
A character segmentation apparatus according to claim 7, wherein said character segmentation means further comprises:

The glue character segmentation unit is configured to detect the stuck characters existing in the acquired plurality of single character regions, and divide the glue characters to obtain a final single character region.
A character segmentation apparatus according to claim 7, wherein said character line division unit comprises:

a first calculating module, configured to perform horizontal projection on the character image, respectively calculate a number of pixel points in the pixel range of each row of pixels in a preset range, and obtain the gray value in a preset a line distribution histogram curve of the pixel points in the range; fitting the line distribution histogram curve by a Gaussian function to determine the position of each line character;

The character line segmentation module divides each line of characters based on the position of each line of characters to obtain a plurality of character line images.
A character segmentation apparatus according to claim 7, wherein said character segmentation unit comprises:

a second calculating module, configured to vertically project the character line graph, and calculate a number of pixel points in the column of each column of pixels in a preset range, and obtain the gray value in the pre-predetermined range a column distribution histogram curve of the pixel points in the range; sequentially scanning a column distribution histogram curve of the pixel points whose gray value is within a preset range according to a preset order, thereby obtaining the gray value in a preset range a pixel dot column with zero pixel counts;

The single-character segmentation module is configured to divide each single character based on the obtained pixel point sequence in which the gray value is zero in the preset range, thereby obtaining the divided single-character regions.
A component detecting method, comprising:

Acquiring an image of the component to be detected, wherein the image of the component to be detected includes a character image of a printed character of the component to be detected;

Obtaining the character image;

Dividing each line of characters based on the number of pixels in each row of pixels in the character image in a preset range, thereby obtaining segmented character line images;

And dividing each single character according to the number of pixels in each column of pixels in each of the character line images in the preset range, thereby obtaining the divided single character regions;

Character recognition is performed on the character image based on the acquired single character regions, thereby acquiring information of the printed characters of the component to be detected.
A component detecting device, comprising:

The image to be detected image acquiring unit is configured to acquire an image of the component to be detected, wherein the image of the component to be detected includes a character image of a printed character of the component to be detected;

a character image obtaining unit, configured to acquire a character image;

a character line dividing unit, configured to divide each line of characters based on the number of pixels of the gray level value in each row of pixel points in the character image, thereby obtaining the divided character line images;

a character dividing unit, configured to divide each single character based on the number of pixels in the column of each of the character line images in the preset grayscale value, thereby obtaining the divided a number of single character areas;

The to-be-detected component information acquiring unit is configured to perform character recognition on the printed character image based on the acquired plurality of single-character regions, thereby acquiring information of the printed characters of the to-be-detected component.