CN107292311A - A kind of recognition methods of the Characters Stuck identifying code based on neutral net - Google Patents
A kind of recognition methods of the Characters Stuck identifying code based on neutral net Download PDFInfo
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Abstract
A kind of recognition methods of the Characters Stuck identifying code based on neutral net, comprises the following steps：Intercept a number of identifying code picture automatically from website standby, basic pretreatment is carried out to the image of the identifying code picture of deposit, original identifying code image is set to be easier to Character segmentation, by the Character segmentation in pretreated image into single character, the character picture segmented is normalized, training is identified to normalized character picture by artificial neural network, recognition accuracy is calculated.Beneficial effect of the present invention：Pass through different degrees of noise reduction process, the complicated ambient noise of identifying code can be removed well, substantially zero noise Character segmentation of progress can be reached, also there is preferable effect simultaneously for the segmentation of the character of adhesion, have higher discrimination for the complex background identifying code with adhesion character by the training of neutral net.
Description
Technical field
The present invention relates to intelligent information processing technology field, specifically a kind of Characters Stuck based on neutral net is tested
Demonstrate,prove the recognition methods of code.
Background technology
The invention of identifying code strengthens the protection to information, prevents some illegal with ensureing the safety of network using being
Molecule passes through the various means such as high performance hardware device, the rogue program of specific function, web crawlers and website design leak
Crack account password, steal the automatic registration of user profile, malice.Identifying code is included compared with other safety verification modes
Data volume is smaller, and effectively raises web portal security performance and anti-attack ability.Identifying code is to discriminate between computer and the mankind
Full-automatic turing test program.
But due to continuing to develop for network, identifying code is also reformed constantly, identification identifying code will be seen that identifying code is set
The rule and principle of meter, contribute to find identifying code design defect, so as to design it is safer, more ripe, more conform to use
The identifying code of family usage experience.The identification of identifying code simultaneously is also a large amount of big data analyses that data are captured from network of needs
Worker provides facility, promotes the development of correlative study.
Checking code type on network has many kinds, mainly have it is based on Text Mode, based on image model, based on language
Sound pattern and video mode is at least partly based on, it is most widely used with the identifying code based on Text Mode.Text based is tested
Card code initially only includes simple numeral and alphabetical, optical character identification (Optical Character Recognition,
OCR) text in image can directly be extracted identification and have good recognition effect by technology.The safety of network, which is promoted, to be verified
The identification difficulty of code is continuously increased, and starts to change character color, increases image background, increase image noise interference etc., for
This kind of identifying code first can carry out reusing OCR extractions after simply pre-processing to image, also there is more good effect.
With continuing to develop for identifying code, begin through the text in identifying code image is distorted, the method such as adhesion
To increase the difficulty of its identification.The difficulty of such identifying code is to be split the character of adhesion, and researcher carries
Go out many character extraction algorithms, such as skeletal extraction algorithm, color filling method, Drop fall algorithm etc., passing through these algorithms will
Character in identifying code split or profile extraction, then by OCR, naive Bayesian, SVMs etc. is trained.
But segmentation effect is often unsatisfactory, so as to cause final recognition correct rate not high.
The content of the invention
The technical problems to be solved by the invention are to provide a kind of identification of the Characters Stuck identifying code based on neutral net
Method, solves the problem of current identifying code Characters Stuck, background complexity are difficult to.
The present invention is for the technical scheme that is used of solution above-mentioned technical problem：A kind of Characters Stuck based on neutral net
The recognition methods of identifying code, comprises the following steps：
Step 1: intercepting a number of identifying code picture automatically from website, deposit file is standby；
Step 2: carrying out basic pretreatment to the image of the identifying code picture of deposit, original identifying code image is set to be easier to word
Symbol is cut, and specific method is：
（1）By the coloured image gray processing in the identifying code picture of collection；
（2）Gray level image goes dry processing：The noise in corrosion treatment, downscaled images, usage threshold algorithm, selection are carried out to image
Appropriate threshold value, binary conversion treatment is carried out by image, and the image after binary conversion treatment is negated, finally using median filtering method
Remove most of noise in image；
（3）The size of locating verification code, to step（2）Image after denoising removes frame；
（4）To going the image after frame further to remove companies all in noise, i.e. selection image by connected region noise reduction hair
Logical region, selectes a suitable threshold value, removes the less noise of connected region, retains the larger character of connected region；
Step 3: by the Character segmentation in the pretreated image of step 2 into single character, specific method is：
（1）The gray value of image after step 2 is handled carries out the projection of vertical direction, obtains the projection of image vertical direction
Histogram；
（2）By Character segmentation of the position of trough in the projection histogram of obtained vertical direction, character picture is divided into list by point
Individual character picture；
（3）Single character picture gray value is subjected to the projection in horizontal direction, the throwing of single character picture horizontal direction is obtained
Shadow histogram, determines the up-and-down boundary of character by the projection histogram of horizontal direction and is split；
Step 4: the character picture segmented is normalized；
Step 5: training is identified to normalized character picture by artificial neural network；
Step 6: calculating recognition accuracy.
In step 5 of the present invention, it is to the specific method that training is identified in normalized character picture：Selected BP
Neutral net is as training tool, from three-layer neural network structure, i.e. input layer, single hidden layer and output layer, using with
Training is identified in the training type of machine gradient method and adaptive regularized learning algorithm rate.
The beneficial effects of the invention are as follows：The present invention is by selecting the order of suitable denoising mode and denoising method to checking
Code image is pre-processed, by different degrees of noise reduction process, well can be removed the complicated ambient noise of identifying code, right
Also there is preferable effect in the segmentation of the character of adhesion.During corrosion treatment is applied into noise reduction, background not remove only
In molecule noise, while also been removed the noise for being attached to character edge, become apparent from character outline；On basis
The method that connected region noise reduction is utilized after the completion of noise reduction process, the noise that medium filtering is difficult to remove is removed, can be reached substantially
To zero noise Character segmentation of progress；Verify that this method is for the complex background with adhesion character using the training of neutral net
Identifying code has higher discrimination.
Brief description of the drawings
Fig. 1 is the flow chart of method for recognizing verification code involved in the present invention；
Fig. 2 is identifying code original image of the embodiment of the present invention；
Fig. 3 is the later image of identifying code image gray processing of the embodiment of the present invention；
Fig. 4 is the later design sketch of identifying code Image erosion of the embodiment of the present invention；
Fig. 5 is identifying code of embodiment of the present invention binaryzation and the image negated；
Fig. 6 is the design sketch after identifying code medium filtering of the embodiment of the present invention；
Fig. 7 is the design sketch after identifying code connected region noise reduction of the embodiment of the present invention；
Fig. 8 is identifying code image projection histogram divion explanation of the embodiment of the present invention；
Fig. 9 is identifying code Character segmentation design sketch of the embodiment of the present invention；
Figure 10 is autoadapted learning rate of the embodiment of the present invention and error change curve map.
Embodiment
With reference to embodiment of the Figure of description to the present invention（Embodiment）It is described, makes the skill of this area
Art personnel better understood when the present invention.
A kind of recognition methods of the Characters Stuck identifying code based on neutral net, comprises the following steps：
Step 1, by the way that C++ codings are automatic refresh Website page and intercept identifying code picture automatically, a certain amount of picture of acquisition is simultaneously
It is stored in standby in specific file；
Step 2, the image to the identifying code picture of interception are pre-processed, and original identifying code image is become suitable for segmentation
Image, specific method is：
2.1st, it is first according to formula for the image of identifying code to be identified
Coloured image gray processing processing is carried out, wherein,R、G、BThe pixel value of pixel red, green, blue in coloured image is represented respectively,GrayFor the gray value of required point, effect is as shown in Figure 3；
2.2nd, the noise in gray level image can influence the result that image is split, therefore carry out denoising to gray level image, due to
Identifying code image background is complicated, it is necessary to from suitable denoising method and suitable noise reduction order so that image to be split is
The effect of noise reduction is reached, while remaining the clear profile of character.Specific method is：
2.2.1, corrosion is a kind of elimination boundary point, and the process for making border internally shrink, expansion can regard pair of corrosion as
Even computing.Use expansion formulaReverse operating to gray level image carry out corrosion treatment, wherein A is to treat
The image of processing, B is used structural element,Represent that B hits A, i.e., in the presence of a point being both the element and A in B
Element,It is to be obtained after structural element B is translated z, ifA is hit, this z point is write down, all z points for meeting condition
Set is referred to as the result that A is expanded by B.The region relative decrease of character inside image can be made by corrosion treatment, and eroded
Some noises of image character edge adhesion, become apparent from character outline, at the same can also by particle in background image compared with
Small noise is easily removed, and effect is as shown in Figure 4；
2.2.2, usage threshold algorithm, selects appropriate threshold value, and the image that step 2.2.1 is treated carries out binary conversion treatment,
And the image after binaryzation is negated, the image of such black matrix wrongly written or mispronounced character can more clearly observe the noise in background, understand
The convenient denoising work for carrying out next step of the feature of remaining noise, effect is as shown in Figure 5；
2.2.3, because noise particle is smaller and position isolated, therefore it is filtered, is removed using 3 × 3 medium filtering template
Most of noise in image, if starting directly to use medium filtering, can cause background to be obscured with character, character outline is not clear enough
It is clear, and increase the difficulty of later stage character recognition, effect is as shown in Figure 6；
2.3rd, locating verification code image size, the frame for hindering processing is removed to the image after denoising；
2.4th, the image treated to step 2.3 further removes noise by connected region Method of Noise, i.e., will own in image
Connected region be marked, because most of noise has been removed in image, so noise larger and isolated in image is just
It can clearly mark out and, due to a required character necessarily big connected region, so the size of contrast connected region
Appropriate threshold is set, less connected region is deleted, retains the big connected region for including character, to reach the purpose of denoising.
If starting just to use connected region noise reduction, the noise sticked in around character can be made together to calculate the connected region for belonging to character
In, cause character outline smudgy, segmentation difficulty is increased, and effect is as shown in Figure 7；
Step 3, the image pretreated to step 2 are divided into single character picture, and specific method is：
The 3.1st, the gray value of pretreated image is carried out to the projection of vertical direction, the projection Nogata of image vertical direction is obtained
Figure, because background color is black, and the gray value of black is 0, and for the character of non-adhesion, gray value is two for 0 position
Space between character, judges the relative width of single character；
3.2nd, for adhesion character, Characters Stuck part is smaller, and gray value is also relatively small, and resulting projection histogram will
Corresponding trough is formed, the position of trough is selected with reference to the relative width of character, Character segmentation point, projection histogram segmentation is determined
Illustrate as shown in Figure 8；
3.3rd, the character picture of pre-segmentation is divided into single character picture according to cut point, Character segmentation effect is as shown in Figure 9；
3.4th, single character picture gray value is subjected to the projection in horizontal direction, by grey in the projection histogram of horizontal direction
Angle value determines the up-and-down boundary of character and split for 0 peak width；
Step 4, the character picture that step 3 is segmented is normalized, the purpose is to the dimension of the input due to neutral net
Degree needs unified, and normalized size is；
Step 5, by BP neural network training is identified in the character picture normalized, specific method is：
5.1st, from three-layer neural network structure, i.e. input layer, single hidden layer and output layer, the pixel letter of single character is obtained
Breath, and as the input layer of BP neural network；It is 126 to set node in hidden layer, if setting excessive node easily to lead
Cause over-fitting and slow down training speed；Set output layer and include 37 as one（10 numerals, 26 letters and one are not
Know）The linear neuron of target output, wherein activation primitive used in hidden layer is hyperbolic tangent function in neutral net
(tanh) sigmoid functions, are used in output layer；
5.2nd, using the training type of Stochastic gradient method, there is faster training speed and just when to set maximum exercise wheel number be 256
True rate；
5.3rd, using adaptive regularized learning algorithm rate, when training error declines, learning rate is raised, and makes instruction to increase learning process
Practice result more accurate；But then directly terminate training when reaching target error, therefore target error numerical value should not be set
Greatly, learning rate and error change curve are as shown in Figure 9.Other specification sets as shown in table 1 in neutral net.
The parameter setting of table 1
| Parameter name | Initial learning rate | Momentum | Target error |
| Parameter value | 0.0001 | Learning rate * 0.9 | 0.002 |
Step 6, calculating recognition accuracy, using the method for nine folding cross validations, wherein recognition correct rate is that all characters are correct
RatePAverage, and calculate nine times accuracy average, to reach an accurate result of fair relatively.Wherein accuracyP's
Calculation formula is：, whereinTPThe quantity being correctly validated for a certain character,FNIt is erroneously identified for the character
Into other character quantities.
By different degrees of noise reduction process, the complicated ambient noise of identifying code can be removed well, can reached substantially
To zero noise Character segmentation of progress.Also there is preferable effect simultaneously for the segmentation of the character of adhesion, pass through the instruction of neutral net
White silk has higher discrimination for the complex background identifying code with adhesion character.
Claims (2)
1. a kind of recognition methods of the Characters Stuck identifying code based on neutral net, it is characterised in that：Comprise the following steps：
Step 1: intercepting a number of identifying code picture automatically from website, deposit file is standby；
Step 2: carrying out basic pretreatment to the image of the identifying code picture of deposit, original identifying code image is set to be easier to word
Symbol is cut, and specific method is：
By the coloured image gray processing in the identifying code picture of collection；
Gray level image goes dry processing：The noise in corrosion treatment, downscaled images is carried out to image, usage threshold algorithm, selection is suitable
When threshold value, image is subjected to binary conversion treatment, and the image after binary conversion treatment is negated, finally gone using median filtering method
Except most of noise in image；
The size of locating verification code, to step（2）Go it is dry after image remove frame；
To going the image after frame further to remove connected regions all in noise, i.e. selection image by connected region noise reduction hair
Domain, selectes a suitable threshold value, removes the less noise of connected region, retains the larger character of connected region；
Step 3: by the Character segmentation in the pretreated image of step 2 into single character, specific method is：
The gray value of image after step 2 is handled carries out the projection of vertical direction, obtains the projection Nogata of image vertical direction
Figure；
By Character segmentation of the position of trough in the projection histogram of obtained vertical direction, character picture is divided into single by point
Character picture；
Single character picture gray value is subjected to the projection in horizontal direction, the projection for obtaining single character picture horizontal direction is straight
Fang Tu, determines the up-and-down boundary of character by the projection histogram of horizontal direction and is split；
Step 4: the character picture segmented is normalized；
Step 5: training is identified to normalized character picture by artificial neural network；
Step 6: calculating recognition accuracy.
2. a kind of recognition methods of Characters Stuck identifying code based on neutral net according to claim 1, its feature exists
In：In the step 5, it is to the specific method that training is identified in normalized character picture：Selected BP neural network conduct
Training tool, from three-layer neural network structure, i.e. input layer, single hidden layer and output layer, using the instruction of Stochastic gradient method
Practice type and training is identified in adaptive regularized learning algorithm rate.
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Cited By (21)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN108446704A (en) * | 2018-03-29 | 2018-08-24 | 哈尔滨理工大学 | A kind of segmentation of adhesion character identifying code and recognition methods |
| CN108647683A (en) * | 2018-04-28 | 2018-10-12 | 浙江大学 | It is a kind of that the character made an uproar is added to fight verification code generation method and system based on frequency domain |
| CN108874756A (en) * | 2018-06-29 | 2018-11-23 | 广东智媒云图科技股份有限公司 | A kind of identifying code optimization method |
| CN109086772A (en) * | 2018-08-16 | 2018-12-25 | 成都市映潮科技股份有限公司 | A kind of recognition methods and system distorting adhesion character picture validation code |
| CN109101810A (en) * | 2018-08-14 | 2018-12-28 | 电子科技大学 | A kind of text method for recognizing verification code based on OCR technique |
| CN109933969A (en) * | 2017-12-15 | 2019-06-25 | 腾讯科技（深圳）有限公司 | Method for recognizing verification code, device, electronic equipment and readable storage medium storing program for executing |
| CN109948621A (en) * | 2019-03-20 | 2019-06-28 | 南京工业大学 | A kind of image procossing and character segmentation method based on picture validation code |
| CN110069915A (en) * | 2019-03-12 | 2019-07-30 | 杭州电子科技大学 | A kind of nine grids figure method for recognizing verification code based on contours extract |
| CN110222753A (en) * | 2019-05-28 | 2019-09-10 | 北京金山数字娱乐科技有限公司 | Character recognition verification method, system, computer equipment, storage medium and chip |
| CN110232375A (en) * | 2018-03-05 | 2019-09-13 | 重庆邮电大学 | A kind of method for recognizing verification code hollow end to end |
| CN110298811A (en) * | 2018-03-21 | 2019-10-01 | 北京大学 | Preprocess method, device, terminal and the computer readable storage medium of image |
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| CN112257715A (en) * | 2020-11-18 | 2021-01-22 | 西南交通大学 | Method and system for identifying adhesive characters |
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