CN101872416A - Vehicle license plate recognition method and system of road image - Google Patents
Vehicle license plate recognition method and system of road image Download PDFInfo
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Abstract
The invention relates to a method and a system for recognizing a vehicle license plate. The method can provide support to an intelligent traffic system and can be widely applied to aspects of parking lots, charging bayonet, criminal apprehend and the like. The method mainly comprises three phases of locating, character segmentation and character recognition, has higher recognition rate, can cope with complex environment and can satisfy the requirement of real-time treatment. The system packages the method, provides different parameters, can be flexibly used and is convenient to distribute and process data in batch.
Description
Technical field
The invention belongs to Digital Image Processing and mode identification technology, be specifically related to a kind of method and system that road image is carried out car plate identification.
Background technology
In recent years, develop rapidly along with computing machine and Internet technology, the rapid growth of various motor vehicle quantity, various information comprises that the information relevant with traffic presents the situation of explosive growth, in order to manage these information safer, efficiently, intelligent transportation system (Intelligence Transportation System) is arisen at the historic moment.Intelligent transportation system can be at the charge bayonet socket, the parking lot, and aspects such as criminal tracking show powerful effect, and are its ingredients of core the most to the detection and Identification of car plate.
At present, though a lot of ripe relatively car plates identification products have been arranged now, higher accuracy rate, still less consuming time still attracting people to go constantly to study.In fact, along with the new development of association area, the invention of for example new low-level image feature, the proposition of better sorting algorithm etc., all the improvement for the car plate recognizer provides new chance.In addition, commercial now Vehicle License Plate Recognition System is mostly only to there being reasonable effect under the certain conditions, for example specific illumination, distance, angle, which system is car plate standard (comprising color, form, literal), and the accuracy rate of system may reduce even lose efficacy greatly under the environment that has changed also do not have to accomplish healthy and strong stable (discern the ability of car plate also there is a big difference with the people) under various condition.Third part at this paper can be discussed specially to one piece of article about this respect.Have again, for example hand-held or vehicle mounted camera shooting gets off for natural conditions picture shot and video, carry out car plate detect with identification also be a direction that is worth research.
In some license plate recognition technologies that exist, mostly whole identification is divided into three processes at present, as shown in Figure 1, is b car plate location, the c characters on license plate is cut apart, the d Recognition of License Plate Characters.
Wherein for b, the technology of use is of a great variety, though can be divided into several classes, the boundary between the class is not clearly.The following method is roughly arranged: binaryzation after the A rim detection, maximum a kind of method that this is to use, it just can access reasonable just result with after Mathematical Morphology Method combines.An edge detection operator that is in daily use is vertical sobel operator.Its calculating formula is
And longitudinal edge detects compared to laterally, and its advantage, and longitudinal edge detects compared to laterally, its advantage are that the transverse edge of car in the image that contains car plate is more, and the longitudinal edge of character is more on the car plate.This method is calculated fast, effect is better, but a very big shortcoming is difficult to handle complex image exactly, how to remove incoherent marginal information effectively, (vehicle intake mouth for example, the light zone, trees on every side, coarse ground etc.), be a very crucial problem .B mathematical morphology operation, mainly be that burn into expands opening and closing operation etc.C connected component analysis (CCA), most typical is four connected sums, eight connection methods and various clustering method, identical with the connected region purpose, tells some candidate regions.D divides block analysis.Image is divided into some, calculates features such as its average, variance, marginal information respectively.Moving window.Similar with piecemeal thought, but be the pointwise calculated characteristics.The E Color Image Processing is according to the RGB colouring information.The various sorters of F comprise Ada-Boost, SVM, ANN, GP, GA etc.
For c, present technology is divided into following a few class.A binary Images Processing projecting method.Be maximum a kind of technology of using in the current various document, common way is to carry out transverse projection earlier, cuts off zone up and down; Carry out longitudinal projection then, be syncopated as each character.B local auto-adaptive binaryzation.Local auto-adaptive binaryzation or similar method in a lot of articles, it is by piecemeal, pointwise or divide character to calculate average in a certain zone, and features such as contrast are carried out binaryzation then respectively.Go out the C sloped correcting method.Kind is many, have to utilize HT location license plate frame, also have to use colouring information, also has histogram analysis.The D level is cut apart and merging, splitting method.The E mathematical morphology, the burn into dilation operation.Characters on license plate cut apart this step in fact difficulty be bigger, because if cut apart the error or the two-value effect bad, the character recognition of back probably will be lost efficacy.And different illumination conditions, approximate color or shape around the car plate, different car plate standards all can restrict the method that characters on license plate is cut apart, and makes to be difficult to produce extremely healthy and strong cutting techniques at present.Majority method all also just shows good performance under specific circumstances.
For d, OCR (optical character identification) is an important branch of area of pattern recognition, and its target is that the various literal with image format are identified as under the textual form.Recognition of License Plate Characters is a kind of special shape of OCR, and the process of Recognition of License Plate Characters can be reduced to feature extraction and characteristic matching.Feature extracting methods at character picture is varied, has by pixel characteristic extraction method, framework characteristic extraction method, vertical orientation data statistical nature extraction method, a lot of based on the feature extraction method of grid, radian gradient feature extraction method etc.Do not carry out feature extraction in addition, in other words with the black and white values of character picture directly as feature, rely on the powerful classification capacity of sorter to discern, also be a kind of mode that can adopt.And the method for characteristic matching mainly is divided into following three kinds of A. based on statistics/mixing/with different levels sorter B.ANN.C. template matching method. wherein ANN be the most common and performance more excellent.
Summary of the invention
The object of the present invention is to provide a kind of method and system that still image is carried out car plate identification, be intended to solve the key problem in the intelligent transportation system (ATI), obtain the number-plate number in the monitoring image, for the deeper application in back is prepared.
The method of described car plate identification provided by the invention comprises the car plate location, and characters on license plate is cut apart and three steps of Recognition of License Plate Characters;
Described car plate location is that the image of input is cut apart, and obtains one group of car plate candidate regions.It is input as a pictures, size is carried out pre-service arbitrarily then, based on two steps of cluster of DBSCAN, exports one group of car plate candidate regions;
The task of described preprocessing process is that original color image is handled, and generates a bianry image that comprises marginal information.It comprises image gray processing, and sobel longitudinal edge detection+car plate color strengthens, three steps of image binaryzation.
Described sobel longitudinal edge detection+car plate color strengthens, and is full figure to be carried out sobel vertically detect, in testing process, for marginal information p (x, y), if＞K, x*x neighborhood around it is scanned, to wherein each is gone, occur if any the car plate color, then strengthen p (x, y) 10%; K=30 among the embodiment, x=7; The color set that may occur in the only various car plates of the car plate color here.
Described cluster based on DBSCAN is to use DBSCAN Density Clustering method that the two-value picture is carried out cluster, all points are divided into several high density areas, the zone that surpasses certain threshold value T is called candidate regions, and threshold value T determines by debugging according to the scene size of reality.Here critical radius is divided into RH and RW, the radius on the expression length and width direction, general RW=3*RH; Get RH=10 among the embodiment, RW=30, T=300; Calculate each regional attribute then, comprise length and width, length breadth ratio etc. are deleted the very few point in regional both sides.At last the attribute of candidate regions is judged, got rid of the zone that does not conform to shape, export possible candidate regions.
Described characters on license plate is cut apart, and is the car plate candidate regions is carried out cutting apart of character respectively that the deletion error candidate regions obtains one group of character block to each other candidate.It comprises the secondary pre-service and based on two steps of cutting apart of projection;
Described secondary preprocessing process is earlier regional picture to be carried out gray processing, utilizes the average threshold value with its binaryzation then, at last by horizontal projection, seeks in the perspective view low ebb zone up and down, thinks that it is a useless region, deletes it.Obtain two-value car plate topography.
Described based on the cutting apart of projection, it be input as two-value car plate topography, output be set of diagrams as character block, be generally 7 (number of characters on the car plate).At first carry out vertical projection, think that the trough district is the dead sector, other is a character area, marks off several regions with this.Carry out analysis and judgement for the zone then, to get rid of non-license plate area.Blank up and down to each character picture excision more at last.Generate one group of character zone.
Described Recognition of License Plate Characters is that every group of character block discerned, and the debug candidate regions generates the number-plate number, car plate color and the license plate area coordinate that identify.It is input as single two-value zone, input is the character that identifies, and comprises Chinese character, numeral and alphabetical.If Qu Yu the ratio of width to height＞4 at first, dot density is greater than a certain threshold value in its zone, and then this character of Direct Recognition is 1.Then with whole bianry image sequence as feature, bring separately sorter into and classify, the output result.If the character of identification is not 7, think that then it is non-license plate area.The final number-plate number, car plate color and the license plate area coordinate that identifies that generate.
Described sorter is characterized in that, respectively the two-value template is set up in numeral, character and Chinese character in advance, divides time-like to adopt the mode of template matches to discern, the output recognition result.
The present invention also provides a kind of system of car plate identification, and comprise three modules that realize three steps of described licence plate recognition method: car plate locating module, characters on license plate are cut apart module and Recognition of License Plate Characters module.Input is a pictures, and input is that plurality of sub zone picture and recognition result are described document xml file.Can there be multiple parameter to select, function with dynamic-configuration parameter, function with Dynamic Definition output, can export the local picture of car plate, the local picture of position of driver, the local picture of car mark, the local picture of vehicle body, thumbnail etc., can conveniently replace licence plate recognition method, be easy to carry out batch processing.
Described multiple parameter is selected to be meant and add parameter behind program name, plate, and other, all can distinguish and only discern car plate output number and position, and only calculating is exported other local pictures (being inferred by the car plate position) and is all handled.Realized separating of critical process (car plate identification) and other processes.
The local picture of described calculating output position of driver according to car plate position and vehicle size, is estimated position of driver, and is compared with original image, proofreaies and correct the local picture of back output position of driver.
The local picture of described calculating output car mark according to the car plate position, scans the car plate upper area, seeks the car mark, is cutting apart the local picture in output car plate position, back.
The local picture of described calculating output vehicle body according to car plate position and vehicle size, is estimated the vehicle body position, and is compared with original image, proofreaies and correct the local picture of back output vehicle body.
Description of drawings
Fig. 1 is car plate identification process figure.
Fig. 2 is a car plate locating module process flow diagram.
Fig. 3 is that characters on license plate is cut apart the module process flow diagram.
Fig. 4 is the enforcement illustration that a pictures is discerned.
Fig. 5 is the process flow diagram of Vehicle License Plate Recognition System.
Embodiment
In order to make purpose of the present invention, technical scheme and advantage clearer,, the present invention is further elaborated below in conjunction with drawings and Examples.Should be appreciated that specific embodiment described herein only in order to explanation the present invention, and be not used in qualification the present invention.
Fig. 1 shows the main flow process of licence plate recognition method.Comprise the car plate location, characters on license plate is cut apart and the Recognition of License Plate Characters three phases.The value of intermediate transfer is 1 or a plurality of license plate candidate area, and in the middle of b, c, d operational process successively, the candidate regions of mistake is progressively abandoned.The final output number-plate number.
Fig. 2 shows car plate locating module flow process.For an input original image, for example shown in Fig. 4 upper left corner.At first it is carried out gray processing, the formula of gray processing is gray=0.299R+0.587G+0.114B, and wherein gray represents gray-scale value, and R, G, B are respectively three components of image.Next carries out vertical sobel rim detection, in the middle of vertical testing process, carries out the enhancing to the car plate color, concrete rule is (for example blue), for marginal information p (x, y), if＞30,7*7 neighborhood around it is scanned, to wherein each is gone, occur if any blue dot, then strengthen p (x, y) 10%, here blueness refers to (G＜150, R＜150, B＞G, B＞R).Carry out binaryzation once more, obtain a width of cloth black and white point diagram, shown in the upper right corner among Fig. 4, be not difficult to find out, near the point the car plate is than comparatively dense, and this is the basis of next step cluster.
Adopt DBSCAN Density Clustering algorithm to obtain one group of candidate regions afterwards, part shown in the dotted line is the concrete steps of clustering algorithm among Fig. 2.Its general idea be exactly for density than higher zone, think that they belong to the same area.In this embodiment, lateral radius RH value is 10, and vertically radius R W value is 30, and density threshold gets 250.Carry out aftertreatment afterwards, delete white space twice, get rid of the zone that does not conform to shape, in this embodiment, think that the license plate area shape should satisfy: 2.5＜length breadth ratio＜7,80＜length＜220,20＜wide＜60.So far, positioning stage finishes.Among Fig. 4, a right side central figure on the upper side has been divided into out 5 zones as can be seen, and these 5 zones are candidate regions.Middle part picture on the lower side shows the topography of that candidate regions of car plate.
Fig. 3 shows characters on license plate and cuts apart the module flow process.Each candidate regions for input carries out a series of pre-service earlier, comprises gray processing, binaryzation and horizontal projection.That bottom central authorities is the result of binaryzation among Fig. 4, and its left side corresponding horizontal projection that is it, is it above it through the result after the cutting between the dead sector up and down.Carry out then specifically comprising vertical projection, telling character area and clear area based on the cutting apart of projection; Regional analysis, superseded false candidates; Single character is frittered three processes of branch.The result has been syncopated as each character by several vertical white lines shown in the little figure on the lower side of Fig. 4 central authorities.Bring recognition engine afterwards into and carry out the identification of literal, the output number-plate number BPG016 of Soviet Union.Shown in the little figure of Fig. 4 left side central portion.
Fig. 5 shows the cardinal principle flow process of Vehicle License Plate Recognition System.It supports three kinds of different use-patterns, is respectively parameter and is all, printenv or parameter other but do not have the situation of follow-up coordinate; The situation of parameter p late; Parameter other back adds the situation of two groups of coordinates.When parameter is all, this system will all operate, and location and identification car plate calculate various regional area pictures by it, at last output then earlier.Parameter p late represents only to discern car plate and output, and parameter other represents only to calculate various regional area pictures and output.The regional area here comprises, driver zone, car mark zone and vehicle body zone.These regional area pictures will provide certain help for other subsequent applications of intelligent transportation system.Also comprise an xml document in the last output, describe the result of identification.
Claims (10)
1. the method that road image is carried out car plate identification is characterized in that, comprises the car plate location, and characters on license plate is cut apart and three steps of Recognition of License Plate Characters;
Described car plate location is that the image of input is cut apart, and obtains one group of car plate candidate regions;
Described characters on license plate is cut apart, and is the car plate candidate regions is carried out cutting apart of character respectively that the deletion error candidate regions obtains one group of character block to each other candidate;
Described Recognition of License Plate Characters is that every group of character block discerned, and the debug candidate regions generates the number-plate number, car plate color and the license plate area coordinate that identify.
2. method according to claim 1 is characterized in that, in the described car plate positioning step, is input as a pictures, and size is arbitrarily carried out pre-service, then based on two steps of cluster of DBSCAN;
The task of described preprocessing process is that original color image is handled, and generates a bianry image that comprises marginal information; Preprocessing process comprises image gray processing, and sobel longitudinal edge detection+car plate color strengthens, three steps of image binaryzation;
The task of described cluster based on DBSCAN is that bianry image is carried out clustering processing, generates one group of car plate candidate regions; The steps include: to use DBSCAN Density Clustering method that the two-value picture is carried out cluster, all points are divided into several high density areas, the zone that surpasses certain threshold value T is called candidate regions; Here critical radius is divided into RH and RW, the radius on the expression length and width direction, RW=3*RH; Calculate each regional attribute then, comprise length and width, length breadth ratio is deleted the very few point in regional both sides; At last the attribute of candidate regions is judged, got rid of the zone that does not conform to shape, export possible candidate regions.
3. method according to claim 2 is characterized in that described sobel longitudinal edge detection+car plate color strengthens, and the steps include: that full figure is carried out sobel vertically to be detected, in testing process, for marginal information p (x, y), if greater than K, x*x neighborhood around it is scanned, to wherein each is gone, occur if any the car plate color, then strengthen p (x, y) 10%, the car plate color here refers to the color set that may occur in the various car plates.
4. method according to claim 1 is characterized in that described characters on license plate is cut apart and comprises the secondary pre-service and based on two steps of cutting apart of projection;
Described secondary preprocessing process is that each candidate regions is carried out gray processing, local binaryzation, and the horizontal projection operation generates the two-value license plate image;
Described based on being cutting apart of projection that bianry image is carried out vertical projection, character cutting is one by one exported set of diagrams at last as character block then.
5. method according to claim 4, it is characterized in that described secondary pre-service, the steps include: earlier regional picture to be carried out gray processing, utilize the average threshold value with its binaryzation then, at last by horizontal projection, seek in the perspective view low ebb zone up and down, think that it is a useless region, delete it, obtain two-value car plate topography.
6. licence plate recognition method according to claim 4 is characterized in that describedly the steps include: at first to carry out vertical projection based on the cutting apart of projection that think that the trough district is the dead sector, other is a character area, marks off several regions with this; Carry out analysis and judgement for the zone then, get rid of non-license plate area; Blank up and down to each character picture excision more at last; Generate one group of character zone.
7. method according to claim 1 is characterized in that described Recognition of License Plate Characters, the steps include: to be input as single two-value zone, and output is the character that identifies, and comprises Chinese character, numeral and alphabetical; If Qu Yu the ratio of width to height＞4 at first, dot density is greater than a certain threshold value in its zone, and then this character of Direct Recognition is 1; Then with whole bianry image sequence as feature, bring separately sorter into and classify, the output result; If the character of identification is not 7, think that then it is non-license plate area; The final number-plate number, car plate color and the license plate area coordinate that identifies that generate.
8. licence plate recognition method according to claim 7 is characterized in that classifying with sorter with described, is respectively the two-value template to be set up in numeral, character and Chinese character in advance, divides time-like to adopt the mode of template matches to discern, the output recognition result.
9. the system of car plate identification is characterized in that comprise: car plate locating module, characters on license plate are cut apart module, three parts of Recognition of License Plate Characters module, is based on the described licence plate recognition method of claim 1; Input is a pictures, and output is that plurality of sub zone picture and recognition result are described document xml file;
There is multiple parameter to select, has the function of dynamic-configuration parameter, have the function of Dynamic Definition output, the local picture of output car plate, the local picture of position of driver, the local picture of car mark, the local picture of vehicle body, thumbnail; The convenient licence plate recognition method of replacing is easy to carry out batch processing;
Described multiple parameter is selected to be meant and add parameter behind program name, plate, and other, all only discerns car plate output number and position respectively, and only calculating is exported other local pictures and is all handled, and realizes separating of critical process and other processes.
10. Vehicle License Plate Recognition System according to claim 9, it is characterized in that the local picture of described calculating output position of driver, is according to car plate position and vehicle size, the estimation position of driver, and compare with original image, proofread and correct the local picture of back output position of driver; The local picture of described calculating output car mark is according to the car plate position, and the car plate upper area is scanned, and seeks the car mark, is cutting apart the local picture in output car plate position, back; The local picture of described calculating output vehicle body is according to car plate position and vehicle size, estimates the vehicle body position, and compares with original image, proofreaies and correct the local picture of back output vehicle body.
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