License Plate Recognition System

Transcription

1 Lcense Plate Recognton System Remus BRAD Computer Scence Department Lucan Blaga Unversty, Sbu, Romana Abstract Ths paper presents a novel method of dentfyng and recognzng lcense plates, requred by an automatc vehcle dentfcaton system. The man challenge was the solaton of the car s lcense plate, from the dgtal mage acqured by the vdeo system, under dfferent llumnaton condtons and varous angles. Thus, we have establshed the requred preprocessng operatons leadng to successful plate dentfcaton. The strng recognton stage s straghtforward, after the completon of the character extracton process. Tested on Romanan plate mages, the system performance was 93% of correct dentfed plates and 88.57% of correct recognzed characters. Suggeston for further mprovements are presented, as well as further related project developpment. 1. Introducton Car s lcense plate detecton and recognton became an nterestng task just from the begnnng of automaton by the means of computer vson. By smply enouncng, one can magne a wde range of applcatons, startng from parkng access to vehcle management, as well as traffc control and publc securty. Beng already subject to comercal many applcatons, lcense plate recognton systems contnued to be an nterestng topc for researchers. In all cases mentoned above, one wll deal wth a seres of problems, manly consstng of: requrement for real tme processng; varous llumnaton condtons; nclned plates; n moton vehcles; and plates belongng to other states. An exhaustve study of lcense plate recognton s provded by Shrdhar et al. [1], concludng that fuson of gray scale morphology and homomorphc processng are yeldng to hgh rates of strng extracton and recognton. In [2] a soluton to the problem of fast passng vehcles and mage blurrng s gven. Varable mage acquston angles gve rotated characters n lcense dentfcaton strngs. Such alteratons are handled usng the Hotellng transform n [3] or by the nvarant propertes of the normalzed moment of nerta [4]. Recognton s generally acheved by template matchng, neural networks [5] [6] [7], or other classfers such as holographc nearestneghbor [4]. In ths paper, we report a smple method for detectng the lcense plate form a grayscale mage and recognze the strng contaned by the plate. We frst use a preprocessng stage, consstng of a certan number of transforms establshed heurstcally for ths specfc type of mages. The plate rectangle s then detected, usng the Hough transform for locatng lnes and a template for the expected rectangular form. After approprate character segmentaton, the recognton stage s based on a template matchng method. 2. Preprocessng The preprocessng stage s generally requred for further hgh level mage processng algorthms and reduces the nformaton quanttes. We have employed the followng preprocessng scheme: edge detecton wth a Prewtt operator low-pass flterng for nose cleanng thresholdng wth the Maxmum Entropy Crteron The use of Prewtt edge detecton operator was motvated by hs hgh potental of detectng horzontal and vertcal lnes, bascally requred for the rectangle shape of the plate. The resultng mage s then low-pass fltered for nose removal and smoothng. The flter used here s a specal case of the followng parametrc flter: = b 1 H + b b 2 b 2 (1) b 1 b 1

2 a) b) Fgure 1. The Maxmum Entropy Crteron thresholdng. a) orgnal mage; b) thresholded mage. The Maxmum Entropy Crteron [8], provdes an unsupervsed soluton to the choce of threshold dlemma. Consder f(x,y), an mage of NxN pxels and m gray levels. Assume G m ={,1,...,(m-1)} the gray levels and f G m the appearance frequency of the gray levels n mage f. The probablty of level n mage f, wll be: f p =, Gm (2) N N Thus, we obtan the {p G m } dstrbuton. For s 1 a gven s gray level, f < p <1, the next two dstrbutons may be derved from ths one, after a normalsaton: p p1 ps 1 A =,,..., (3) ps ps+ 1 pm 1 B =,,..., s 1 where P ( = p s the total probablty tll (s-1) gray level. = = m 1 s s 1 p H ( = p ln( p ) s 1 p ln( p ) H ( The Maxmum Entropy Crteron assumes fndng the threshold s that maxmze the followng measure: TE( s ) = max TE( (6) s G m The method s computatonally feasble and leads n short tme to the soluton. Also, thresholds are determned automatcally. Fgure 1 shows the orgnal mage and the thresholded one, at the end of the preprocessng stage. 3. Lcense Plate Detecton Lcense plate detecton stage n based on the Hough transform and was used n [9]. The man motvaton for usng ths technque was the rectangle shape of the plate and the possblty to emphasze t by preprocessng. (5) The basc dea of the MEC method s the approprate choce of threshold, that maxmze the amount of nformaton obtaned from the object and background. Recallng that the measure of nformaton s the entropy, the total amount of nformaton gven by A and B s: s 1 p p TE( = E A ( + EB ( = ln where: = = ln m 1 s p p ln 1 1 H ( H ( 1 [ (1 ) ] (4) Detecton uses the followng steps: 1. Hough transform of the thresholded mage. We have employed the classcal Hough transform. The sets of thresholds requred by the transform were establshed upon the nformatons observed from the mages. 2. Detecton of peak values above some threshold, usng the polar coordnate mage resulted from transform. By nverse transformaton from the Hough space, obtan the eucldan coordnates of the lnes and nsert them n a prevously created lst. 3. Selecton of vertcal and horzontal lnes. The selecton s made usng a certan threshold, makng near horzontal/vertcal lnes possble canddates. We store them n a lst.

3 a) b) c) d) Fgure 2. Plate detecton. a) detected lnes; b) selected horzontal and vertcal lnes; c) detected rectangles; d) canddate rectangles for strng recognton. 4. Rectangle detecton. Usng the prevously created lsts, fnd two horzontal and two vertcal ntersectng lnes. Create a thrd lst contanng detected rectangles. 5. Rectangle selecton usng a lcense plate template and edges proporton. Cut out the correspondng mage for canddate rectangle and go to recognton stage. Fgure 2 a), b), c) and d) shows the results of steps 2, 3, 4 and 5 for the orgnal mage n fgure 1 a). emphaszed from the background. The cumulatve hstogram s agan computed for the columns of the bnary mage. Selecton of the characters s straghforward, by nspectng the hstogram and fndng the gaps between characters. The mage area correspondng to each character s then extracted and forwarded to the character recognton stage. 4. Strng Detecton Followng the rectangle detecton stage, the strng recognton conssts of two man steps. Frst we have to cut from the orgnal mage the correspondng area. Ths s not always the best selecton and could contan portons from the plate s edge or from the country s symbols. In all cases, we are computng the rows and columns cumulatve hstograms for the selected area. Analyss of the shape of the two hstograms, we are able to select only the porton of the mage contanng the letters. The Maxmum Entropy Crteron s also used to threshold the selected mage and therefore the characters wll be a) b) Fgure 3. a) detected strng; b) bounded characters. Fgure 3 shows the results of strng detecton, after cuttng down unnterestng portons of the mage through the hstogram nspecton (a), and the bounded characters n the thresholded mage (b). 5. Character Recognton The character recognton stage s based on template matchng and uses the followng metrc:

4 a) b) Fgure 4. Test results. Recognzed strng n a) romanan car mage; b) foregn car mage. X oy M = (7) ( X o X )( Y oy ) where X o Y denotes the nner product of matrces X and Y. If the matrces sze s NxM, the product s defned by: N M X o Y = X Y (8) j= The metrc value s for two totally dfferent matrces and 1 for correlated ones. If the matrces are holdng bnary values of character templates, the metrc could be used for matchng. Consequently, we have to rescale the character mages n order to calculate the metrc. The template-matchng algorthm mplements the followng steps: 1. Select the character mage from the detected strng 2. Rescale the mage to the sze of the frst template 3. Compute the matchng metrc 4. Store f hghest mach found 5. For all stored templates, goto step Store the ndex of best mach as recognzed character 7. For all bounded character mages, goto step 1 If the match metrc s below.55, then no character has been recognzed and a dash - s nserted n the strng., j, j totally recognzed. Mssed classfcatons have been caused by drty plates, obstructng objects (lke screws or ncorrect postoned techncal check stcker or ncorrect postoned camera. Lcense plate where correctly detected on 93% of mages. Errors are due to non-unform llumnaton, drty plates or obstructng objects. Fgure 4 shows two test mages, one contanng a romanan plate and a foregn one. Pctures are also showng detected plates and successful recognzed strngs. 7. Concluson We have developed a new method for detectng and recognzng car lcense plates. We have tested the system on romanan plates and obtaned good recognton rates. Results may be mproved by refnng the recognton stage and testng other classfers. The system has been tested on a certan number of foregn lcense plates mages, downloaded from the Internet and performed satsfactory. Character templates could be created and added to the system, f dfferent plates are taken nto account. Future work s ntended to be done n mprovng and testng the system on a larger number of mages. We also ntend to ntegrate the system n a larger traffc management project. The template lbrary was buld upon data extracted from vehcle mages, comprsng letters (A-Z), numbers (-9) and * (for the romanan techncal check stcker). Each character was automatcally selected and thresholded usng methods prevously descrbed. 6. Results The system was tested on 3 romanan plate mages. The total number of characters was 21. Among them, the recognton rate was 88.57% (186 character. 7% (147) of the character strngs dsplayed n test mages where References [1] M. Shrdhar, J.W.V. Mller, G. Houle, L. Bjnagte, Recognton of Lcense Plate Images: Issues and Perspectves, Proc. of the Ffth Intl. Conf. on Document Analyss and Recognton, pp. 17-2, Sept [2] T. Nato, T. Tsukada, K. Yamada, K. Kozuka, S. Yamamoto, Lcense Plate Recognton Method for Inclned Plates Outdoors, Proc. Intl. Conf. on Informaton Intellgence and Systems, pp , Nov. 1999

5 [3] H.A. Hegt, R.J. de la Haye, N.A. Khan, A hgh performance lcense plate recognton system, IEEE Intl. Conf. on Systems, Man, and Cybernetcs, vol.5, pp , 1998 [4] L.A. Torres-Mendez, J.C. Ruz-Suarez, L.E. Sucar, G. Gomez, Translaton, rotaton, and scale-nvarant object recognton, IEEE Trans. on System, Man and Cybernetcs Part C, vol. 3, no.1, pp , Feb. 2 [5] T. Srthnaphong, K. Chamnongtha, Extracton of car lcense plate usng motor vehcle regulaton and character pattern recognton, The 1998 IEEE Asa-Pacfc Conference on Crcuts and Systems, pp , 1998 [6] R. Pars, E.D. D Claudo, G. Lucarell, G. Orland, Car plate recognton by neural networks and mage processng, Proc. of the 1998 IEEE Internatonal Symposum on Crcuts and Systems, pp , June 1998 [7] S.H. Park, K.I. Km, K. Jung, H.J. Km, Locatng car lcense plates usng neural networks, Electroncs Letters, vol.35, pp , Aug [8] J.C. Yen, F.J. Chang, S. Chang, "A New Crteron for Automatc Multlevel Thresholdng", IEEE Transactons on Image Processng, vol. 4, no. 3, pp , 1995 [9] V. Kamat, S. Ganesan, An effcent mplementaton of the Hough transform for detectng vehcle lcense plates usng DSP'S, Proc. Real-Tme Technology and Applcatons Symposum, pp , May 1995

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