CN107688807A - Image processing method and image processing apparatus - Google Patents
Image processing method and image processing apparatus Download PDFInfo
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- G—PHYSICS
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- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
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- G06V20/60—Type of objects
- G06V20/62—Text, e.g. of license plates, overlay texts or captions on TV images
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Abstract
The present invention provides a kind of image processing method, and it includes：Based on the variable quantity of pixel grey scale in image, multiple identification regions are divided an image into；According to the gray value of all pixels of identification region, the integral gradient amplitude of all pixels in identification region is calculated；The occurrence number of the integral gradient amplitude of all pixels in identification region, obtain at least one occurrence number extremal region of the integral gradient amplitude of all pixels of each identification region；According to the occurrence number extremal region of identification region, the character area border of image is determined, to carry out Text region processing to image.The present invention also provides a kind of image processing apparatus, the image processing method and image processing apparatus of the present invention determines the character area border of image by the occurrence number extremal region of identification region, accurately the word in image can be identified, and the calculating process of occurrence number extremal region is simple.
Description
Technical field
The present invention relates to image processing field, more particularly to a kind of image processing method and image processing apparatus.
Background technology
In present image process field, character recognition technology and method are more ripe, are such as entered using gradient image information
Row identification is identified by the way of deep learning, above-mentioned identification technology can be used fine the accurate character block of cutting
Be identified.But become increasingly complex with the background information of application scenarios, the character block of application scenarios can not have been entered
The accurate slicing operation of row, so as to cause to identify that the difficulty of the word in image is larger.
In face of the above situation, MSER (Maximally Stable Extremal Regions, maximum can be usually used
Stability region) method or SWT (stroke width transform, stroke width conversion) method based on character duration carry out
Text region in image.
Wherein MSER methods carry out binaryzation to image by using different gray thresholds and obtain gray scale stability region, but
It is when it is large stretch of stability region that character is mutually nested with background, the word in image can not be accurately distinguished.SWT methods
The character duration information in background can be calculated, but calculating process is complex, and the false alarm rate in complex background picture
It is higher, bring certain pressure to identification.
The content of the invention
The embodiment of the present invention provides a kind of can be accurately distinguished to the word in image and calculating process is relatively simple
Image processing method and image processing apparatus；With solve existing image processing method and image processing apparatus in image
Word can not be accurately distinguished or be identified the complex technical problem of calculating process.
The embodiment of the present invention provides a kind of image processing method, and it includes：
Based on the variable quantity of pixel grey scale in image, described image is divided into multiple identification regions；
According to the gray value of all pixels of the identification region, the entirety for calculating all pixels in the identification region is terraced
Spend amplitude；
The occurrence number of the integral gradient amplitude of all pixels in the identification region, obtains the identification region
All pixels integral gradient amplitude at least one occurrence number extremal region；And
According to the occurrence number extremal region of the identification region, the character area border of described image is determined, with
Just Text region processing is carried out to described image.
The embodiment of the present invention also provides a kind of image processing apparatus, and it includes：
Division module, for the variable quantity based on pixel grey scale in image, described image is divided into multiple identification regions；
Computing module, for the gray value of all pixels according to the identification region, calculate institute in the identification region
There is the integral gradient amplitude of pixel；
Extremal region acquisition module, the appearance for the integral gradient amplitude of all pixels in the identification region
Number, obtain at least one occurrence number extremal region of the integral gradient amplitude of all pixels of the identification region；And
Identification module, for the occurrence number extremal region according to the identification region, determine the text of described image
Word zone boundary, to carry out Text region processing to described image.
Compared to prior art, image processing method of the invention and image processing apparatus go out occurrence by identification region
Extremal region is counted to determine the character area border of image, accurately the word in image can be identified, and occurrence number
The calculating process of extremal region is simple；Solve existing image processing method and image processing apparatus to the word in image
It can not be accurately distinguished or be identified the complex technical problem of calculating process.
Brief description of the drawings
Fig. 1 is the flow chart of the first preferred embodiment of the image processing method of the present invention；
Fig. 2 is the flow chart of the second preferred embodiment of the image processing method of the present invention；
Fig. 3 is the step S204 of the second preferred embodiment of the image processing method of present invention flow chart；
Fig. 4 is the signal of the pixel gradient amplitude coordinate system in the second preferred embodiment of the image processing method of the present invention
Figure；
Fig. 5 is that the occurrence number of the integral gradient amplitude in the second preferred embodiment of the image processing method of the present invention is bent
The schematic diagram of line；
Fig. 6 is the structural representation of the first preferred embodiment of the image processing apparatus of the present invention；
Fig. 7 is the structural representation of the second preferred embodiment of the image processing apparatus of the present invention；
Fig. 8 is the structural representation of the computing module of the second preferred embodiment of the image processing apparatus of the present invention；
Fig. 9 is the structural representation of the extremal region acquisition module of the second preferred embodiment of the image processing apparatus of the present invention
Figure；
Figure 10 is the maximum of the extremal region acquisition module of the second preferred embodiment of the image processing apparatus of the present invention
The structural representation of area acquisition unit；
Figure 11 be the present invention image processing method and image processing apparatus specific embodiment processing image schematic diagram；
Figure 12 is turned for the specific embodiment of image processing method and image processing apparatus of the invention using MSER algorithms
Processing image schematic diagram after changing；
Figure 13 is the character area border of the specific embodiment of image processing method and image processing apparatus of the invention
Image schematic diagram；
Figure 14 be the present invention image processing apparatus where electronic equipment working environment structural representation.
Embodiment
Schema is refer to, wherein identical element numbers represent identical component, and principle of the invention is to implement one
Illustrated in appropriate computing environment.The following description is based on the illustrated specific embodiment of the invention, and it should not be by
It is considered as the limitation present invention other specific embodiments not detailed herein.
In the following description, specific embodiment of the invention will be referred to as the operation performed by one or multi-section computer
The step of and symbol illustrate, unless otherwise stating clearly.Therefore, it will appreciate that these steps and operation, be carried for several times wherein having
To be performed by computer, include by representing with the computer disposal list of the electronic signal of the data in a structuring pattern
Member is manipulated.This manipulation transforms data or the opening position being maintained in the memory system of the computer, it can match somebody with somebody again
Put or change the running of the computer in a manner familiar to those skilled in the art in addition.The data knot that the data are maintained
Structure is the provider location of the internal memory, and it has the particular characteristics as defined in the data format.But the principle of the invention is with above-mentioned
Word illustrates, it is not represented as a kind of limitation, those skilled in the art will appreciate that plurality of step as described below and
Operation also may be implemented among hardware.
The image processing method and image processing apparatus of the present invention can be used for the movement of various progress pictograph identifications
Electronic equipment, stationary electronic devices, wearable device, helmet or medical treatment ＆ health platform.User is set using the mobile electron
Standby, stationary electronic devices, wearable device, helmet or medical treatment ＆ health platform can accurately distinguish to the word in image
And Text region process is relatively simple.
Fig. 1 is refer to, Fig. 1 is the flow chart of the first preferred embodiment of the image processing method of the present invention.This is preferred real
Above-mentioned mobile electronic device or stationary electronic devices can be used to be implemented for the image processing method for applying example, this preferred embodiment
Image processing method include：
Step S101, based on the variable quantity of pixel grey scale in image, divide an image into multiple identification regions；
Step S102, according to the gray value of all pixels of identification region, calculate the entirety of all pixels in identification region
Gradient magnitude；
Step S103, the occurrence number of the integral gradient amplitude of all pixels in identification region, obtain cog region
At least one occurrence number extremal region of the integral gradient amplitude of all pixels in domain；
Step S104, according to the occurrence number extremal region of identification region, the character area border of image is determined, so as to right
Image carries out Text region processing.
The following detailed description of the idiographic flow of each step of the image processing method of this preferred embodiment.
In step S101, variable quantity of the image processing apparatus based on pixel grey scale in image, divide an image into multiple
Identification region.Here image refers to the image for carrying out Text region, and the image includes background and word, due to background
Color and text color have certain display gray difference, i.e. the variable quantity of the pixel grey scale of word and background intersection can be compared with
Greatly, the variable quantity of the pixel grey scale of simple background area is smaller.Therefore can be divided an image into by above-mentioned variable quantity more
The individual identification region with word, and the pure background area of no word is removed, it is effective to be carried out to each identification region
Text region.Then pass to step S102.
In step s 102, image processing apparatus is according to the ashes of all pixels of the step S101 each identification regions obtained
Angle value, calculate the integral gradient amplitude of all pixels in each identification region.Due in identification region also include background area with
And character area, therefore the grey scale change amount of each pixel in identification region with respect to adjacent pixel is obtained in this step, as
The integral gradient amplitude of the pixel grey scale, so as to demarcate the character area in identification region and background area.With
After go to step S103.
In step s 103, as background area pixel grey scale and character area pixel grey scale difference be it is fixed,
Then the integral gradient amplitude of the pixel grey scale of the intersection of background area and character area is also fixed, the picture of these intersections
The occurrence number of the integral gradient amplitude of plain gray scale is directly proportional to the size of character area, i.e., character area is bigger, intersection
The occurrence number of the integral gradient amplitude of pixel grey scale is bigger.
In this step, it is necessary to which main character area is identified, and main character area is in identification region
Area is larger, the occurrence number of the integral gradient amplitude of the junction in main character area and neighboring background region also just compared with
It is more, therefore image processing apparatus is dry to some by the occurrence number of the integral gradient amplitude of all pixels in identification region here
The background area grey scale change disturbed is filtered, the region as corresponding to will appear from the pixel of the less integral gradient amplitude of number
Without Text region.
The occurrence number of the integral gradient amplitude of all pixels so in the identification region after filtering, obtain identification
At least one occurrence number extremal region of the integral gradient amplitude of all pixels in region.Here occurrence number extremal region
Refer to the more region of the occurrence number of integral gradient amplitude in identification region, the overall ladder of all pixels should be included but is not limited to
Spend the most region of the occurrence number of amplitude.Then pass to step S104.
In step S104, image processing apparatus is according to the occurrence number extreme value areas of the step S103 identification regions obtained
Domain, determine the character area border of image.Subsequent picture processing unit enters style of writing according to the character area border of image to image
Word identifying processing.
Text region process in the image for the image processing method for so completing this preferred embodiment.
The image processing method of this preferred embodiment determines image by the occurrence number extremal region of identification region
Character area border, accurately the word in image can be identified, and the calculating process of occurrence number extremal region is simple.
Fig. 2 is refer to, Fig. 2 is the flow chart of the second preferred embodiment of the image processing method of the present invention.This is preferred real
Above-mentioned mobile electronic device or stationary electronic devices can be used to be implemented for the image processing method for applying example, this preferred embodiment
Image processing method include：
Step S201, based on the changing value of the pixel grey scale in image, image is drawn using maximum stability region algorithm
It is divided into multiple identification regions；
Step S202, according to the gray value of all pixels of identification region, calculate the entirety of all pixels in identification region
Gradient magnitude；
Step S203, based on default normalization setting, the integral gradient amplitude of all pixels in identification region is carried out
Normalized；
Step S204, the occurrence number of the integral gradient amplitude of all pixels in identification region, obtain cog region
At least one occurrence number extremal region of the integral gradient amplitude of all pixels in domain；
Step S205, according to the occurrence number extremal region of identification region, determine the character area border of image；
Step S206, operation is filled to the region in character area border, to enter to the image after padding
Style of writing word identifying processing.
The following detailed description of the idiographic flow of each step of the image processing method of this preferred embodiment.
In step s 201, variable quantity of the image processing apparatus based on pixel grey scale in image, uses MSER
(Maximally Stable Extremal Regions, maximum stability region algorithm) divides an image into multiple cog regions
Domain.Here image refers to the image for carrying out Text region, and the image includes background and word.
MSER algorithms carry out the setting of multiple gray thresholds to the gray-scale map of image, and to each gray threshold in image district
Pixel quantity in domain is counted, and gray threshold is changed into minimum region is set as maximum stability region.Due to background
Color and text color have certain display gray difference, i.e. the variable quantity of the pixel grey scale of word and background intersection can be compared with
Greatly, the variable quantity of the pixel grey scale of simple background area is smaller.Therefore above-mentioned maximum stability region can divide image
, here can be by maximum stability region (pure background area) for multiple identification regions (non-maximum stability region) with word
Remove, to carry out effective Text region to each identification region.
Here the available identification region information of the pixel in each identification region and zone position information represent, know
Other area information refers to the information of the identification region where pixel, and zone position information refers to pixel in corresponding identification region
In positional information.Then pass to step S202.
In step S202, image processing apparatus is according to the ashes of all pixels of the step S101 each identification regions obtained
Angle value, calculate the integral gradient amplitude of all pixels in each identification region.
Here each horizontal shade of gray amplitude of pixel in the horizontal direction in identification region is calculated using sobel operators
GradientXImage, and it is terraced in the vertical gray scale of vertical direction using each pixel in sobel operators calculating identification region
Spend amplitude GradientYImage.
For each pixel image_n_gray (x, y) in identification region, wherein n is that the identification region of pixel is believed
Breath, x, y are position coordinates of the pixel in corresponding identification region.Pixel image_n_gray (x, y) horizontal gray scale ladder
Degree amplitude is GradientXImage_n (x, y), and pixel image_n_gray (x, y) vertical shade of gray amplitude is
GradientYImage_n(x,y)。
Pixel image_n_gray (x, y) integral gradient amplitude is GradientAllImage_n (x, y),
Then pass to step S203.
In step S203, image processing apparatus sets a designated area [0, m] and is used as normalized setting regions, and m can
For 255 etc..
Image processing apparatus traversal GradientAllImage_n (x, y) all values, obtain all
Maximum GradientAllImage_n_max in GradientAllImage_n (x, y).
It is normalized using below equation.The entirety of pixel image_n_gray (x, y) after normalization
Gradient magnitude is：GradientAllImageNormalize_n(x,y).
GradientAllImageNormalize_n (x, y)=
m*GradientAllImage_n(x,y)/GradientAllImage_n_max。
The integral gradient amplitude of each pixel so after normalized is respectively positioned on 0 between m.Then pass to step
S204。
In step S204, the entirety of all pixels of the image processing apparatus in the identification region after normalized
The occurrence number of gradient magnitude, obtain at least one occurrence number extreme value of the integral gradient amplitude of all pixels of identification region
Region.
If the pixel grey scale of background area and the difference of pixel grey scale of character area are fixed, then background area and text
The integral gradient amplitude of the pixel grey scale of the intersection in block domain is also fixed, the overall ladder of the pixel grey scale of these intersections
The occurrence number of degree amplitude is directly proportional to the size of character area, i.e., character area is bigger, the entirety of the pixel grey scale of intersection
The occurrence number of gradient magnitude is bigger.
In this step, it is necessary to which main character area is identified, and main character area is in identification region
Area is larger, the occurrence number of the integral gradient amplitude of the junction in main character area and neighboring background region also just compared with
It is more, therefore image processing apparatus is dry to some by the occurrence number of the integral gradient amplitude of all pixels in identification region here
The background area grey scale change disturbed is filtered, the region as corresponding to will appear from the pixel of the less integral gradient amplitude of number
Filter out.
Specific to refer to Fig. 3, Fig. 3 is the step S204 of the second preferred embodiment of the image processing method of present invention stream
Cheng Tu.Step S204 includes：
Step S2041, image processing apparatus is using the integral gradient amplitude of all pixels of identification region as abscissa, identification
The occurrence number of the integral gradient amplitude of all pixels in region is ordinate, establishes the pixel gradient amplitude coordinate of identification region
System.Specifically as shown in figure 4, Fig. 4 is the pixel gradient amplitude seat in the second preferred embodiment of the image processing method of the present invention
Mark the schematic diagram of system.
Step S2042, image processing apparatus then mark institute on the pixel gradient amplitude coordinate system that step S2041 is established
There is the occurrence number point of integral gradient amplitude.Here it is that the integral gradient amplitude of all pixels of identification region is counted,
So that occurrence number of each integral gradient amplitude obtained in identification region, then by above-mentioned integral gradient amplitude and correspondingly
Occurrence number be labeled on pixel gradient amplitude coordinate system.If the mark point in Fig. 4 can be that integral gradient amplitude A occurs 40
Secondary, integral gradient amplitude B occurs 50 times, and integral gradient amplitude C occurs 80 times, and integral gradient amplitude D occurs 90 times, integral gradient
Amplitude E occurs 100 times, and integral gradient amplitude F occurs 120 times, and integral gradient amplitude G occurs 95 times, and integral gradient amplitude H occurs
85 times, integral gradient amplitude I occurs 50 times, and integral gradient amplitude J occurs 55 times, and integral gradient amplitude K occurs 50 times.Wherein A>
B>C>D>E>F>G>H>I>J>K.Here the integral gradient amplitude of too small (essentially 0) is deleted.
Step S2043, the occurrence number point for the integral gradient amplitude that image processing apparatus marks to step S2042 carry out height
This smoothing processing, the occurrence number curve for obtaining integral gradient amplitude (are counted with Gaussian function to these occurrence number points
Calculate and obtain one group of new point distribution).Specifically as shown in figure 5, Fig. 5 is the second preferred embodiment of the image processing method of the present invention
In integral gradient amplitude occurrence number curve schematic diagram.
Step S2044, image processing apparatus carry out a derivation and secondary derivation to the point in occurrence number curve, can
To obtain the maximum point of occurrence number curve and minimum point.Such as the integral gradient amplitude F in Fig. 4, integral gradient amplitude B
And integral gradient amplitude J is maximum point, between integral gradient amplitude B and integral gradient amplitude C, integral gradient amplitude H with
And there is minimum point between integral gradient amplitude I.
By analyzing the maximum point between two minimum points, the non-zero obtained around the maximum goes out occurrence
It is several integral gradient amplitudes, i.e. integral gradient amplitude C, integral gradient amplitude D, integral gradient amplitude E, integral gradient amplitude F, whole
Body gradient magnitude G and integral gradient amplitude H.
The region of the integral gradient amplitude of non-zero occurrence number around the maximum of above-mentioned acquisition is arranged to maximum F
Region corresponding to corresponding maximum region, i.e. integral gradient amplitude H to integral gradient amplitude C.Certainly through the above way may be used
To obtain multiple maximum points of occurrence number curve and corresponding maximum region.
Step S2045, will be greater than setting value maximum point and corresponding maximum region corresponding to integral gradient width
It is worth region, is arranged to occurrence number extremal region.
The integral gradient amplitude of all pixels so in the identification region after being filtered using setting value goes out occurrence
Number, obtain at least one occurrence number extremal region of the integral gradient amplitude of all pixels of identification region.Here appearance
Number extremal region refers to the more region of the occurrence number of integral gradient amplitude in identification region, should include but is not limited to all
The most region of the occurrence number of the integral gradient amplitude of pixel.Then pass to step S205.
In step S205, image processing apparatus is according to the occurrence number extreme value areas of the step S204 identification regions obtained
Domain, determine the character area border of image.Then pass to step S206.
In step S206, the region in character area border that image processing apparatus obtains to step S205 is filled
Operation, so can preferably distinguish character area and background area, so as to accurately obtain the position of the character area of image
Put, background area can be removed well by Text region.
Text region process in the image for the image processing method for so completing this preferred embodiment.
On the basis of first preferred embodiment, the image processing method of this preferred embodiment is carried out to integral gradient amplitude
Normalized, further increase the degree of accuracy of Text region operation；Occur by establishing occurrence number curve to obtain
Number extremal region, simplify the calculating process of occurrence number extremal region acquisition；Region in character area border is carried out
Padding so that the difference of character area and background area is bigger, is more easy to background area being removed operation.
The present invention also provides a kind of image processing apparatus, refer to Fig. 6, and Fig. 6 is the of the image processing apparatus of the present invention
The structural representation of one preferred embodiment.Above-mentioned image processing method can be used in the image processing apparatus of this preferred embodiment
First preferred embodiment is implemented, the image processing apparatus 60 of this preferred embodiment include division module 61, computing module 62,
Extremal region acquisition module 63 and identification module 64.
Division module 61 is used for the variable quantity based on pixel grey scale in image, divides an image into multiple identification regions；Meter
The gray value that module 62 is used for all pixels according to identification region is calculated, calculates the integral gradient width of all pixels in identification region
Value；The occurrence number of the integral gradient amplitude for all pixels that extremal region acquisition module 63 is used in identification region, is obtained
Take at least one occurrence number extremal region of the integral gradient amplitude of all pixels of identification region；Identification module 64 is used for root
According to the occurrence number extremal region of identification region, the character area border of image is determined, to carry out word knowledge to image
Manage in other places.
The image processing apparatus 60 of this preferred embodiment is in use, division module 61 is based on pixel grey scale in image first
Variable quantity, divide an image into multiple identification regions.Here image refers to the image for carrying out Text region, the image bag
Background and word are included, because background color and text color have certain display gray difference, i.e. word and background has a common boundary
The variable quantity of the pixel grey scale at place can be larger, and the variable quantity of the pixel grey scale of simple background area is smaller.Therefore by upper
Multiple identification regions with word can be divided an image into by stating variable quantity, and the pure background area of no word is removed, with
Just effective Text region is carried out to each identification region.
The gray value of all pixels for each identification region that subsequent computing module 62 obtains according to division module 61, is calculated
The integral gradient amplitude of all pixels in each identification region.Due to also including background area and literal field in identification region
Domain, therefore computing module 62 obtains the grey scale change amount of each pixel in identification region with respect to adjacent pixel, it is grey as the pixel
The integral gradient amplitude of degree, so as to demarcate the character area in identification region and background area.
If the pixel grey scale of background area and the difference of pixel grey scale of character area are fixed, then background area and text
The integral gradient amplitude of the pixel grey scale of the intersection in block domain is also fixed, the overall ladder of the pixel grey scale of these intersections
The occurrence number of degree amplitude is directly proportional to the size of character area, i.e., character area is bigger, the entirety of the pixel grey scale of intersection
The occurrence number of gradient magnitude is bigger.
Due to need main character area is identified, and main character area identification region area compared with
Greatly, main character area and the occurrence number of the integral gradient amplitude of the junction in neighboring background region are also just more, therefore
Here extremal region acquisition module 63 is dry to some by the occurrence number of the integral gradient amplitude of all pixels in identification region
The background area grey scale change disturbed is filtered, the region as corresponding to will appear from the pixel of the less integral gradient amplitude of number
Without Text region,
So integral gradient amplitude of all pixels of the extremal region acquisition module 63 in the identification region after filtering
Occurrence number, obtain at least one occurrence number extremal region of the integral gradient amplitude of all pixels of identification region.This
In occurrence number extremal region refer to the more region of the occurrence number of integral gradient amplitude in identification region, should include but not
It is limited to the most region of the occurrence number of the integral gradient amplitude of all pixels.
The occurrence number extremal region for the identification region that last identification module 64 obtains according to extremal region acquisition module 63,
Determine the character area border of image.Subsequent identification module 64 carries out Text region according to the character area border of image to image
Processing.
Text region process in the image for the image processing apparatus 60 for so completing this preferred embodiment.
The image processing apparatus of this preferred embodiment determines image by the occurrence number extremal region of identification region
Character area border, accurately the word in image can be identified, and the calculating process of occurrence number extremal region is simple.
Fig. 7 is refer to, Fig. 7 is the structural representation of the second preferred embodiment of the image processing apparatus of the present invention.This is excellent
The image processing apparatus of embodiment is selected the second preferred embodiment of above-mentioned image processing method to can be used to be implemented, this is preferably
The image processing apparatus 70 of embodiment includes division module 71, computing module 72, normalization module 73, extremal region acquisition module
74th, identification module 75 and filling module 76.
Division module 71 is used for the variable quantity based on pixel grey scale in image, divides an image into multiple identification regions；Meter
The gray value that module 72 is used for all pixels according to identification region is calculated, calculates the integral gradient width of all pixels in identification region
Value；Normalize module 73 to be used to set based on default normalization, the integral gradient amplitude of all pixels in identification region is entered
Row normalized；The integral gradient amplitude for all pixels that extremal region acquisition module 74 is used in identification region goes out
Occurrence number, obtain at least one occurrence number extremal region of the integral gradient amplitude of all pixels of identification region；Identify mould
Block 75 is used for the occurrence number extremal region according to identification region, determines the character area border of image；Filling module 76 is used for
Operation is filled to the region in character area border, to carry out Text region processing to the image after padding.
Fig. 8 is refer to, Fig. 8 is that the structure of the computing module of the second preferred embodiment of the image processing apparatus of the present invention is shown
It is intended to.The computing module 72 includes horizontal vertical shade of gray amplitude computing unit 81 and integral gradient amplitude computing unit
82。
Horizontal vertical shade of gray amplitude computing unit 81 is used for the gray value of all pixels according to identification region, calculates
The horizontal shade of gray amplitude and vertical shade of gray amplitude of all pixels of identification region；Integral gradient amplitude computing unit
82 are used for the horizontal shade of gray amplitude of all pixels using identification region and vertical shade of gray amplitude, calculate cog region
The integral gradient amplitude of all pixels in domain.
Fig. 9 is refer to, Fig. 9 is the extremal region acquisition module of the second preferred embodiment of the image processing apparatus of the present invention
Structural representation.Extremal region acquisition module 74 includes establishment of coordinate system unit 91, mark unit 92, curve acquisition unit
93rd, maximum region acquiring unit 94 and extremal region acquiring unit 95.
Establishment of coordinate system unit 91 is used for using the integral gradient amplitude of all pixels of identification region as abscissa, cog region
The occurrence number of the integral gradient amplitude of all pixels in domain is ordinate, establishes the pixel gradient amplitude coordinate of identification region
System；Mark unit 92 is used for the occurrence number point that all integral gradient amplitudes are marked on pixel gradient amplitude coordinate system；Curve
Acquiring unit 93 is used to carry out Gaussian smoothing to the occurrence number point of integral gradient amplitude, obtains going out for integral gradient amplitude
Occurrence number curve；Maximum region acquiring unit 94 is used at least one maximum point and correspondingly for obtaining occurrence number curve
Maximum region；Extremal region acquiring unit 95 is used to will be greater than the maximum point of setting value and corresponding maximum region
Corresponding integral gradient amplitude region, is arranged to occurrence number extremal region.
Figure 10 is refer to, Figure 10 is that the extremal region of the second preferred embodiment of the image processing apparatus of the present invention obtains mould
The structural representation of the maximum region acquiring unit of block.The maximum acquiring unit 94 include maximum obtain subelement 941,
Integral gradient amplitude obtains subelement 942 and maximum region obtains subelement 943.
Maximum obtains at least one maximum that subelement 941 is used to obtain the occurrence number curve；Integral gradient
Amplitude obtains the integral gradient amplitude that subelement 942 is used to obtain the non-zero occurrence number around maximum；Maximum region obtains
Take subelement 943 corresponding for the region of the integral gradient amplitude of the non-zero occurrence number around maximum to be arranged into maximum
Maximum region.
The image processing apparatus 70 of this preferred embodiment is in use, division module 71 is based on pixel grey scale in image first
Variable quantity, image is drawn using MSER (Maximally Stable Extremal Regions, maximum stability region algorithm)
It is divided into multiple identification regions.Here image refers to the image for carrying out Text region, and the image includes background and word.
MSER algorithms carry out the setting of multiple gray thresholds to the gray-scale map of image, and to each gray threshold in image district
Pixel quantity in domain is counted, and gray threshold is changed into minimum region is set as maximum stability region.Due to background
Color and text color have certain display gray difference, i.e. the variable quantity of the pixel grey scale of word and background intersection can be compared with
Greatly, the variable quantity of the pixel grey scale of simple background area is smaller.Therefore above-mentioned maximum stability region can divide image
, here can be by maximum stability region (pure background area) for multiple identification regions (non-maximum stability region) with word
Remove, to carry out effective Text region to each identification region.
Here the available identification region information of the pixel in each identification region and zone position information represent, know
Other area information refers to the information of the identification region where pixel, and zone position information refers to pixel in corresponding identification region
In positional information.
The gray value of all pixels for each identification region that subsequent computing module 72 obtains according to division module, calculate every
The integral gradient amplitude of all pixels in individual identification region.
Here the horizontal vertical shade of gray amplitude computing unit 81 of computing module 72 can utilize sobel operators to calculate identification
The horizontal shade of gray amplitude GradientXImage of each pixel in the horizontal direction in region, and utilize sobel operator meters
Vertical shade of gray amplitude GradientYImage of each pixel in vertical direction in calculation identification region.
For each pixel image_n_gray (x, y) in identification region, wherein n is that the identification region of pixel is believed
Breath, x, y are position coordinates of the pixel in corresponding identification region.Pixel image_n_gray (x, y) horizontal gray scale ladder
Degree amplitude is GradientXImage_n (x, y), and pixel image_n_gray (x, y) vertical shade of gray amplitude is
GradientYImage_n(x,y)。
Then the integral gradient amplitude computing unit 82 of computing module 72 calculates each pixel image_n_gray (x, y)
Integral gradient amplitude GradientAllImage_n (x, y)；
Then normalization module 73 sets a designated area [0, m] and is used as normalized setting regions, and m can be 255 etc..
The all values that module 73 travels through GradientAllImage_n (x, y) are normalized, are obtained all
Maximum GradientAllImage_n_max in GradientAllImage_n (x, y).
It is normalized using below equation.The entirety of pixel image_n_gray (x, y) after normalization
Gradient magnitude is：GradientAllImageNormalize_n(x,y).
GradientAllImageNormalize_n (x, y)=m*GradientAllImage_n (x, y)/
GradientAllImage_n_max。
The integral gradient amplitude of each pixel so after normalized is respectively positioned on 0 between m.
Then the overall ladder of all pixels of the extremal region acquisition module 74 in the identification region after normalized
The occurrence number of amplitude is spent, obtains at least one occurrence number extreme value area of the integral gradient amplitude of all pixels of identification region
Domain.
If the pixel grey scale of background area and the difference of pixel grey scale of character area are fixed, then background area and text
The integral gradient amplitude of the pixel grey scale of the intersection in block domain is also fixed, the overall ladder of the pixel grey scale of these intersections
The occurrence number of degree amplitude is directly proportional to the size of character area, i.e., character area is bigger, the entirety of the pixel grey scale of intersection
The occurrence number of gradient magnitude is bigger.
In this step, extremal region acquisition module 74 needs that main character area is identified, and main text
Block domain is larger in the area of identification region, main character area and the integral gradient amplitude of the junction in neighboring background region
Occurrence number it is also just more, therefore the integral gradient that extremal region acquisition module 74 passes through all pixels in identification region here
The occurrence number of amplitude filters to the background area grey scale change that some are disturbed, and such as will appear from the less integral gradient of number
Region corresponding to the pixel of amplitude without Text region,
The specific process for obtaining occurrence number extremal region includes：
The establishment of coordinate system unit 91 of extremal region acquisition module 74 is with the integral gradient width of all pixels of identification region
It is worth for abscissa, the occurrence number of the integral gradient amplitude of all pixels of identification region is ordinate, establishes identification region
Pixel gradient amplitude coordinate system.It is specific as shown in Figure 4.
The pixel gradient width that the mark unit 92 of extremal region acquisition module 74 is then established in establishment of coordinate system unit 91
The occurrence number point of all integral gradient amplitudes is marked on value coordinate system.Here it is the overall ladder to all pixels of identification region
Degree amplitude is counted, so that occurrence number of each integral gradient amplitude obtained in identification region, then by above-mentioned entirety
Gradient magnitude and corresponding occurrence number are labeled on pixel gradient amplitude coordinate system.If the mark point in Fig. 4 can be overall
Gradient magnitude A occurs 40 times, and integral gradient amplitude B occurs 50 times, and integral gradient amplitude C occurs 80 times, and integral gradient amplitude D goes out
Existing 90 times, integral gradient amplitude E occurs 100 times, and integral gradient amplitude F occurs 120 times, and integral gradient amplitude G occurs 95 times, whole
Body gradient magnitude H occurs 85 times, and integral gradient amplitude I occurs 50 times, and integral gradient amplitude J occurs 55 times, integral gradient amplitude K
Occur 50 times.Wherein A>B>C>D>E>F>G>H>I>J>K.Here the integral gradient amplitude of too small (essentially 0) is deleted.
Appearance of the curve acquisition unit 93 of extremal region acquisition module 74 to the integral gradient amplitude of mark unit mark
Number point carries out Gaussian smoothing, and the occurrence number curve for obtaining integral gradient amplitude (occurs with Gaussian function to these
Number point carries out that one group of new point distribution is calculated).It is specific as shown in Figure 5.
The maximum of the maximum region acquiring unit 94 of extremal region acquisition module 74 obtains subelement 941 to going out occurrence
Point in number curve carries out a derivation and secondary derivation, can obtain the maximum point and minimum of occurrence number curve
Point.If integral gradient the amplitude F, integral gradient amplitude B in Fig. 4 and integral gradient amplitude J are maximum point, integral gradient width
There is minimum point between value B and integral gradient amplitude C, between integral gradient amplitude H and integral gradient amplitude I.
The integral gradient amplitude of maximum acquiring unit 94 obtains subelement 942 and passed through to the pole between two minimum points
Big value point is analyzed, and obtains the integral gradient amplitude of the non-zero occurrence number around the maximum, i.e. integral gradient amplitude C,
Integral gradient amplitude D, integral gradient amplitude E, integral gradient amplitude F, integral gradient amplitude G and integral gradient amplitude H.
The maximum region of maximum acquiring unit 94 obtains subelement 943 by the non-zero around the maximum of above-mentioned acquisition
The region of the integral gradient amplitude of occurrence number is arranged to maximum region corresponding to maximum F.Certainly through the above way may be used
To obtain multiple maximum points of occurrence number curve and corresponding maximum region.
The extremal region acquiring unit 95 of extremal region acquisition module 74 will be greater than the maximum point and correspondingly of setting value
Maximum region corresponding to integral gradient amplitude region, be arranged to occurrence number extremal region.
So all pixels of the extremal region acquiring unit 95 in the identification region after use setting value filtering is whole
The occurrence number of body gradient magnitude, obtain at least one occurrence number pole of the integral gradient amplitude of all pixels of identification region
It is worth region.Here occurrence number extremal region refers to the more region of the occurrence number of integral gradient amplitude in identification region,
The most region of the occurrence number of the integral gradient amplitude of all pixels should be included but is not limited to.
The occurrence number extremal region for the identification region that subsequent identification module 75 obtains according to extremal region acquisition module 74,
Determine the character area border of image.
The region in character area border that finally filling module 76 obtains to identification module 75 is filled operation, so
Character area and background area can be preferably distinguished, so as to accurately obtain the position of the character area of image, passes through text
Background area can be removed by word identification well.
Text region process in the image for the image processing apparatus 70 for so completing this preferred embodiment.
On the basis of first preferred embodiment, the image processing apparatus of this preferred embodiment is carried out to integral gradient amplitude
Normalized, further increase the degree of accuracy of Text region operation；Occur by establishing occurrence number curve to obtain
Number extremal region, simplify the calculating process of occurrence number extremal region acquisition；Region in character area border is carried out
Padding so that the difference of character area and background area is bigger, is more easy to background area being removed operation.
Illustrate the image processing method of the present invention and the specific works of image processing apparatus below by a specific embodiment
Principle.Text region processing is carried out to the image shown in Figure 11 in this specific embodiment.Its Text region process includes：
First, image is subjected to region conversion to image using MSER algorithms, the image after conversion is as shown in figure 12, therein
Black region is maximum stability region, and white portion is the identification region with word.
2nd, using sobel operators calculate in identification region each horizontal shade of gray amplitude of pixel in the horizontal direction with
And the vertical shade of gray amplitude in vertical direction.
3rd, using the horizontal shade of gray amplitude of each pixel and vertical shade of gray amplitude in identification region, obtain
The integral gradient amplitude of all pixels in each identification region.
4th, the integral gradient amplitude of all pixels in identification region is normalized so that all pixels it is whole
The distribution of body gradient magnitude, which is in, specifies in section [0,255], i.e. the maximum of integral gradient amplitude is set as 255.
5th, by the occurrence number of the integral gradient amplitude of all pixels in the identification region after normalized in pixel
It is labeled in gradient magnitude coordinate system, and Gaussian smoothing is carried out to above-mentioned occurrence number point, so as to obtains integral gradient
The occurrence number curve of amplitude.
6th, according to the maximum and minimum of occurrence number curve, integral gradient amplitude area corresponding to maximum is determined
Domain, and will be greater than integral gradient amplitude region corresponding to the maximum of setting value and be arranged to occurrence number extremal region.
7th, the character area border of image is determined according to the occurrence number extremal region of identification region, specifically such as Figure 13 institutes
Show.Can then operation be filled to the region in character area border, so as to accurately obtain the character area of image
Position.
So complete the knowledge of word in the image processing method of this specific embodiment and the image of image processing apparatus
Other process.
The image processing method and image processing apparatus of the present invention is by the occurrence number extremal region of identification region come really
Determine the character area border of image, accurately the word in image can be identified, and the calculating of occurrence number extremal region
Process is simple；Solve existing image processing method and image processing apparatus can not carry out accurate area to the word in image
Divide or identify the complex technical problem of calculating process.
" component ", " module ", " system ", " interface ", " process " etc. are usually intended to as used herein the term
Refer to computer related entity：Hardware, the combination of hardware and software, software or executory software.For example, component can be but not
It is limited to run process on a processor, processor, object, executable application, thread, program and/or the computer performed.
By diagram, it can be component to run both application and controllers on the controller.One or more assemblies can have
It is in process and/or the thread of execution, and component can be located on a computer and/or be distributed in two or more meters
Between calculation machine.
Figure 14 and the discussion below are provided to realizing the electronic equipment where image processing apparatus of the present invention
Brief, summary the description of working environment.Figure 14 working environment is only an example of appropriate working environment and not
Be intended to suggestion on working environment purposes or function scope any restrictions.Example electronic equipment 1412 includes but is not limited to
Wearable device, helmet, medical treatment ＆ health platform, personal computer, server computer, hand-held or laptop devices,
Mobile device (such as mobile phone, personal digital assistant (PDA), media player etc.), multicomputer system, consumption-orientation electricity
Sub- equipment, minicom, mainframe computer including above-mentioned arbitrarily DCE of system or equipment, etc..
Although not requiring, in the common background that " computer-readable instruction " is performed by one or more electronic equipments
Lower description embodiment.Computer-readable instruction can be distributed and (be discussed below) via computer-readable medium.It is computer-readable
Instruction can be implemented as program module, for example performs particular task or realize the function of particular abstract data type, object, application
DLL (API), data structure etc..Typically, the function of the computer-readable instruction can be in various environment arbitrarily
Combination or distribution.
Figure 14 illustrates the electronic equipment 1412 of one or more embodiments of the image processing apparatus including the present invention
Example.In one configuration, electronic equipment 1412 includes at least one processing unit 1416 and memory 1418.Set according to electronics
Standby exact configuration and type, memory 1418 can be volatibility (such as RAM), non-volatile (such as ROM, flash memory
Deng) or certain combination of the two.The configuration is illustrated by dotted line 1414 in fig. 14.
In other embodiments, electronic equipment 1412 can include supplementary features and/or function.For example, equipment 1412 is also
Additional storage device (such as removable and/or non-removable) can be included, it includes but is not limited to magnetic memory apparatus, light
Storage device etc..This additional memory devices are illustrated by storage device 1420 in fig. 14.In one embodiment, for reality
The computer-readable instruction of existing one or more embodiments provided in this article can be in storage device 1420.Storage device
1420 can also store other computer-readable instructions for realizing operating system, application program etc..Computer-readable instruction
It can be loaded into memory 1418 and be performed by such as processing unit 1416.
Term as used herein " computer-readable medium " includes computer-readable storage medium.Computer-readable storage medium includes
The volatibility realized for any method or technique of the information of storage such as computer-readable instruction or other data etc
With non-volatile, removable and nonremovable medium.Memory 1418 and storage device 1420 are the realities of computer-readable storage medium
Example.Computer-readable storage medium includes but is not limited to RAM, ROM, EEPROM, flash memory or other memory technologies, CD-ROM, numeral
Universal disc (DVD) or other light storage devices, cassette tape, tape, disk storage device or other magnetic storage apparatus can be with
For storing any other medium it is expected information and can accessed by electronic equipment 1412.Any such computer storage is situated between
Matter can be a part for electronic equipment 1412.
Electronic equipment 1412 can also include the communication connection 1426 for allowing electronic equipment 1412 to be communicated with other equipment.It is logical
Letter connection 1426 can include but is not limited to modem, NIC (NIC), integrated network interface, radiofrequency launcher/
Receiver, infrared port, USB connections or other interfaces for electronic equipment 1412 to be connected to other electronic equipments.Communication
Connection 1426 can include wired connection or wireless connection.Communication connection 1426 can launch and/or receive communication medium.
Term " computer-readable medium " can include communication media.Communication media typically comprises computer-readable instruction
Or other data in " the own modulated data signal " of such as carrier wave or other transmission mechanisms etc, and passed including any information
Send medium.Term " own modulated data signal " can include such signal：One or more of the characteristics of signals is according to general
Information is encoded to the mode in signal to be set or changed.
Electronic equipment 1412 can include input equipment 1424, for example, keyboard, mouse, pen, voice-input device, touch it is defeated
Enter equipment, infrared camera, video input apparatus and/or any other input equipment.It can also be set in equipment 1412 including output
Standby 1422, such as one or more displays, loudspeaker, printer and/or other any output equipments.The He of input equipment 1424
Output equipment 1422 can be connected to electronic equipment 1412 via wired connection, wireless connection or its any combination.In a reality
Apply in example, input equipment or output equipment from another electronic equipment are used as the input equipment of electronic equipment 1412
1424 or output equipment 1422.
The component of electronic equipment 1412 can be connected by various interconnection (such as bus).Such interconnection can include outer
Enclose component interconnection (PCI) (such as quick PCI), USB (USB), live wire (IEEE1394), optical bus structure etc.
Deng.In another embodiment, the component of electronic equipment 1412 can pass through network interconnection.For example, memory 1418 can be by
Multiple physical memory cells arcs composition in different physical locations, by network interconnection.
It would be recognized by those skilled in the art that can be across network point for the storage device for storing computer-readable instruction
Cloth.For example, can via network 1428 access electronic equipment 1430 can store for realize one provided by the present invention or
The computer-readable instruction of multiple embodiments.Electronic equipment 1412 can access electronic equipment 1430 and downloading computer is readable
What is instructed is part or all of for execution.Alternately, electronic equipment 1412 can be downloaded a plurality of computer-readable on demand
Instruction, or some instructions can be performed at electronic equipment 1412 and some instructions can be held at electronic equipment 1430
OK.
There is provided herein the various operations of embodiment.In one embodiment, described one or more operations can be with structure
The computer-readable instruction stored on into one or more computer-readable mediums, it will make to succeed in one's scheme when being performed by electronic equipment
Calculate equipment and perform the operation.Describing the orders of some or all of operations, to should not be construed as to imply that these operations necessarily suitable
Sequence correlation.It will be appreciated by those skilled in the art that the alternative sequence of the benefit with this specification.Furthermore, it is to be understood that
Not all operation must exist in each embodiment provided in this article.
Moreover, although having shown and described the disclosure relative to one or more implementations, but this area skill
Art personnel are based on the reading to the specification and drawings and understand it will be appreciated that equivalent variations and modification.The disclosure include it is all this
The modifications and variations of sample, and be limited only by the scope of the following claims.In particular, to by said modules (such as element,
Resource etc.) various functions that perform, the term for describing such component is intended to correspond to the specified work(for performing the component
The random component (unless otherwise instructed) of energy (such as it is functionally of equal value), it is illustrated herein with execution in structure
The disclosure exemplary implementations in function open structure it is not equivalent.In addition, although the special characteristic of the disclosure
Through being disclosed relative to the only one in some implementations, but this feature can with such as can be to given or application-specific
For be it is expected and other one or more combinations of features of other favourable implementations.Moreover, with regard to term " comprising ", " tool
Have ", " containing " or its deformation be used in embodiment or claim for, such term be intended to with term
The similar mode of "comprising" includes.
Each functional unit in the embodiment of the present invention can be integrated in a processing module or unit list
Solely be physically present, can also two or more units be integrated in a module.Above-mentioned integrated module can both use
The form of hardware is realized, can also be realized in the form of software function module.If the integrated module is with software function
The form of module is realized and is used as independent production marketing or is situated between in use, a computer-readable storage can also be stored in
In matter.Storage medium mentioned above can be read-only storage, disk or CD etc..Above-mentioned each device or system, can be with
Perform the method in correlation method embodiment.
In summary, although disclosed above with embodiment, the sequence number before embodiment of the invention, such as " first ", " second "
Deng only using for convenience of description, the order of various embodiments of the present invention is not caused to limit.Also, above-described embodiment is simultaneously not used to
Limitation the present invention, one of ordinary skill in the art, without departing from the spirit and scope of the present invention, can make it is various change with
Retouching, therefore protection scope of the present invention is defined by the scope that claim defines.
Claims (14)
- A kind of 1. image processing method, it is characterised in that including：Based on the variable quantity of pixel grey scale in image, described image is divided into multiple identification regions；According to the gray value of all pixels of the identification region, the integral gradient width of all pixels in the identification region is calculated Value；The occurrence number of the integral gradient amplitude of all pixels in the identification region, obtain the institute of the identification region There is at least one occurrence number extremal region of the integral gradient amplitude of pixel；AndAccording to the occurrence number extremal region of the identification region, the character area border of described image is determined, so as to right Described image carries out Text region processing.
- 2. image processing method according to claim 1, it is characterised in that the change based on pixel grey scale in image The step of measuring, described image is divided into multiple identification regions includes：Based on the changing value of the pixel grey scale in described image, described image is divided into using maximum stability region algorithm more Individual identification region.
- 3. image processing method according to claim 1, it is characterised in that all pictures according to the identification region The gray value of element, include the step of the integral gradient amplitude for calculating all pixels in the identification region：According to the gray value of all pixels of the identification region, the horizontal gray scale for calculating all pixels of the identification region is terraced Spend amplitude and vertical shade of gray amplitude；AndHorizontal shade of gray amplitude and vertical shade of gray amplitude using all pixels of the identification region, described in calculating The integral gradient amplitude of all pixels of identification region.
- 4. image processing method according to claim 1, it is characterised in that described to calculate all pictures in the identification region After the step of integral gradient amplitude of element, the integral gradient amplitude of all pixels for obtaining the identification region is at least Also include before the step of one occurrence number extremal region：Based on default normalization setting, place is normalized to the integral gradient amplitude of all pixels in the identification region Reason.
- 5. image processing method according to claim 1, it is characterised in that described all in the identification region The occurrence number of the integral gradient amplitude of pixel, obtain at least the one of the integral gradient amplitude of all pixels of the identification region Individual occurrence number extremal region step includes：Using the integral gradient amplitude of all pixels of the identification region as abscissa, all pixels of the identification region it is whole The occurrence number of body gradient magnitude is ordinate, establishes the pixel gradient amplitude coordinate system of the identification region；The occurrence number point of all integral gradient amplitudes is marked on the pixel gradient amplitude coordinate system；Gaussian smoothing is carried out to the occurrence number point of the integral gradient amplitude, obtains the appearance of the integral gradient amplitude Frequency curve；Obtain at least one maximum point of the occurrence number curve and corresponding maximum region；AndWill be greater than setting value maximum point and corresponding maximum region corresponding to integral gradient amplitude region, be arranged to institute State occurrence number extremal region.
- 6. image processing method according to claim 5, it is characterised in that described to obtain the occurrence number curve extremely The step of few maximum point and corresponding maximum region, includes：Obtain at least one maximum of the occurrence number curve；Obtain the integral gradient amplitude of the non-zero occurrence number around the maximum；AndThe region of the integral gradient amplitude of non-zero occurrence number around the maximum is arranged to corresponding to the maximum Maximum region.
- 7. image processing method according to claim 1, it is characterised in that described image processing method also includes step：Operation is filled to the region in the character area border, to carry out Text region to the image after padding Processing.
- A kind of 8. image processing apparatus, it is characterised in that including：Division module, for the variable quantity based on pixel grey scale in image, described image is divided into multiple identification regions；Computing module, for the gray value of all pixels according to the identification region, calculate all pictures in the identification region The integral gradient amplitude of element；Extremal region acquisition module, the integral gradient amplitude for all pixels in the identification region go out occurrence Number, obtain at least one occurrence number extremal region of the integral gradient amplitude of all pixels of the identification region；AndIdentification module, for the occurrence number extremal region according to the identification region, determine the literal field of described image Domain border, to carry out Text region processing to described image.
- 9. image processing apparatus according to claim 8, it is characterised in that the division module is specifically used for based on described The changing value of pixel grey scale in image, described image is divided into multiple identification regions using maximum stability region algorithm.
- 10. image processing apparatus according to claim 8, it is characterised in that the computing module includes：Horizontal vertical shade of gray amplitude computing unit, for the gray value of all pixels according to the identification region, calculate The horizontal shade of gray amplitude and vertical shade of gray amplitude of all pixels of the identification region；AndIntegral gradient amplitude computing unit, for using the identification region all pixels horizontal shade of gray amplitude and Vertical shade of gray amplitude, calculate the integral gradient amplitude of all pixels of the identification region.
- 11. image processing apparatus according to claim 8, it is characterised in that described image processing unit also includes：Module is normalized, for being set based on default normalization, to the integral gradient width of all pixels in the identification region Value is normalized.
- 12. image processing apparatus according to claim 8, it is characterised in that the extremal region acquisition module includes：Establishment of coordinate system unit, for using the integral gradient amplitude of all pixels of the identification region as abscissa, the knowledge The occurrence number of the integral gradient amplitude of all pixels in other region is ordinate, establishes the pixel gradient width of the identification region It is worth coordinate system；Unit is marked, for marking the occurrence number of all integral gradient amplitudes on the pixel gradient amplitude coordinate system Point；Curve acquisition unit, for carrying out Gaussian smoothing to the occurrence number point of the integral gradient amplitude, described in acquisition The occurrence number curve of integral gradient amplitude；Maximum region acquiring unit, at least one maximum point for obtaining the occurrence number curve and corresponding pole Big value region；AndExtremal region acquiring unit, for will be greater than setting value maximum point and corresponding maximum region corresponding to it is overall Gradient magnitude region, it is arranged to the occurrence number extremal region.
- 13. image processing apparatus according to claim 12, it is characterised in that the maximum region acquiring unit bag Include：Maximum obtains subelement, for obtaining at least one maximum of the occurrence number curve；Integral gradient amplitude obtains subelement, for obtaining the integral gradient width of the non-zero occurrence number around the maximum Value；AndMaximum region obtains subelement, for by the area of the integral gradient amplitude of the non-zero occurrence number around the maximum Domain is arranged to maximum region corresponding to the maximum.
- 14. image processing apparatus according to claim 8, it is characterised in that described image processing unit also includes：Module is filled, for being filled operation to the region in the character area border, so as to the figure after padding As carrying out Text region processing.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108564076A (en) * | 2018-04-03 | 2018-09-21 | 中建二局安装工程有限公司 | Visual control system in power wiring in a kind of intelligent building |
| CN109992691A (en) * | 2019-03-26 | 2019-07-09 | 厦门南洋职业学院 | A kind of image-recognizing method and device |
| CN115331119A (en) * | 2022-10-13 | 2022-11-11 | 山东爱福地生物股份有限公司 | Solid waste identification method |
Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101140624A (en) * | 2007-10-18 | 2008-03-12 | 清华大学 | Image matching method |
| CN101859382A (en) * | 2010-06-03 | 2010-10-13 | 复旦大学 | License plate detection and identification method based on maximum stable extremal region |
| CN102663382A (en) * | 2012-04-25 | 2012-09-12 | 重庆邮电大学 | Video image character recognition method based on submesh characteristic adaptive weighting |
| CN102750540A (en) * | 2012-06-12 | 2012-10-24 | 大连理工大学 | Morphological filtering enhancement-based maximally stable extremal region (MSER) video text detection method |
| CN102842126A (en) * | 2011-05-09 | 2012-12-26 | 佳能株式会社 | Image processing apparatus and image processing method |
| CN103136523A (en) * | 2012-11-29 | 2013-06-05 | 浙江大学 | Arbitrary direction text line detection method in natural image |
| CN104077773A (en) * | 2014-06-23 | 2014-10-01 | 北京京东方视讯科技有限公司 | Image edge detection method, and image target identification method and device |
| CN104809433A (en) * | 2015-04-21 | 2015-07-29 | 电子科技大学 | Zebra stripe detection method based on maximum stable region and random sampling |
| CN105447859A (en) * | 2015-11-18 | 2016-03-30 | 扬州大学 | Field wheat aphid counting method |
-
2016
- 2016-08-05 CN CN201610639485.8A patent/CN107688807B/en active Active
Patent Citations (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN101140624A (en) * | 2007-10-18 | 2008-03-12 | 清华大学 | Image matching method |
| CN101859382A (en) * | 2010-06-03 | 2010-10-13 | 复旦大学 | License plate detection and identification method based on maximum stable extremal region |
| CN102842126A (en) * | 2011-05-09 | 2012-12-26 | 佳能株式会社 | Image processing apparatus and image processing method |
| CN102663382A (en) * | 2012-04-25 | 2012-09-12 | 重庆邮电大学 | Video image character recognition method based on submesh characteristic adaptive weighting |
| CN102750540A (en) * | 2012-06-12 | 2012-10-24 | 大连理工大学 | Morphological filtering enhancement-based maximally stable extremal region (MSER) video text detection method |
| CN103136523A (en) * | 2012-11-29 | 2013-06-05 | 浙江大学 | Arbitrary direction text line detection method in natural image |
| CN104077773A (en) * | 2014-06-23 | 2014-10-01 | 北京京东方视讯科技有限公司 | Image edge detection method, and image target identification method and device |
| CN104809433A (en) * | 2015-04-21 | 2015-07-29 | 电子科技大学 | Zebra stripe detection method based on maximum stable region and random sampling |
| CN105447859A (en) * | 2015-11-18 | 2016-03-30 | 扬州大学 | Field wheat aphid counting method |
Non-Patent Citations (5)
| Title |
|---|
| ASIF SHAHAB 等: "ICDAR 2011 Robust Reading Competition Challenge 2: Reading Text in Scene Images", 《DOCUMENT ANALYSIS AND RECOGNITION（ICDAR2011）》 * |
| BORIS EPSHTEIN 等: "Detecting text in natural scenes with stroke width transform", 《2010 IEEE COMPUTER SOCIETY CONFERENCE ON COMPUTER VISION AND PATTERN RECOGNITION》 * |
| 宋砚 等: "基于聚类的视频字幕提取方法", 《通信学报》 * |
| 陈梓洋 等: "自然场景下基于区域检测的文字识别算法", 《计算机技术与发展》 * |
| 黄晓明 等: "自然场景文本区域定位", 《重庆邮电大学学报(自然科学版)》 * |
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| CN108564076B (en) * | 2018-04-03 | 2022-01-18 | 中建二局安装工程有限公司 | Visual control system in electric power wiring in intelligent building |
| CN109992691A (en) * | 2019-03-26 | 2019-07-09 | 厦门南洋职业学院 | A kind of image-recognizing method and device |
| CN115331119A (en) * | 2022-10-13 | 2022-11-11 | 山东爱福地生物股份有限公司 | Solid waste identification method |
| CN115331119B (en) * | 2022-10-13 | 2023-01-31 | 山东爱福地生物股份有限公司 | Solid waste identification method |
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