A Robust Algorithm for Text Detection in Color Images

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1 A Robust Algorthm for Tet Deteton n Color Images Yangng LIU Satosh GOTO Takesh IKENAGA Abstrat Tet deteton n olor mages has beome an atve researh area sne reent deades. In ths paper we present a novel approah to auratel detet tet n olor mages possbl wth a omple bakground. Frst we use an elaborate edge deteton algorthm to etrat all possble tet edge pels. Seond onneted omponent analss s emploed to onstrut tet anddate regon and lassf part non-tet regons. Thrd eah tet anddate regon s verfed wth teture features derved from wavelet doman. Fnall the Epetaton mamzaton algorthm s ntrodued to bnarze tet regons to prepare data for reognton. In ontrast to prevous approah our algorthm ombnes both the effen of onneted omponent based method and robustness of teture based analss. Epermental results show that our algorthm s robust n tet deteton wth respet to dfferent harater sze orentaton olor and language and an provde relable tet bnarzaton result.. Introduton The retreval of tet nformaton from olor mages has ganed nreasng attenton n reent ears. Tet appearng n mages an provde ver useful semant nformaton and ma be a good ke to desrbe the mage ontent. an papers about the tet deteton from olor mages or vdeo sequene have been publshed. However due to the omplet of tet appearane n olor mages tet deteton s stll a dffult and hallengng task n mage proessng. Problems wth man estng methods are that the an not perform well n the ase of varant tet orentaton sze and low resoluton mage where haraters ma be touhng. an methods assume that the tet dreton s horzontal or vertal [] and tet font sze s n lmted range. Some proposed methods [] fal to detet solated harater beause there s no ontetual nformaton and ertan tet parameters suh as base lne harater wdth and heght an not be aumulated wth statstal method n suh ase. In ths paper a novel tet deteton method targeted towards beng robust wth respet to dverse knds of tet appearanes nludng harater font sze orentaton olor and language s presented. Frst wth an elaborate edge deteton algorthm we etrat the edge pels of all possble tet regons n olor mages. Then onneted omponents generated from the edge map are arefull eamned to eld tet regon anddates. Ths s followed b the analss of teture features of anddate tet regons nstead of the whole mage to verf true tet regons and separate non-tet regons. Eventuall the Epetaton mamzaton (E) algorthm s ntrodued to bnarze eah tet regon to prepare data for OCR (Optal Charater Reognton). Sne our algorthm s appled to the edge maps whh are more stable than omplete olor mages under varng llumnaton ondtons and not to entre mage t s robust to ntenst varatons. The rest of ths paper s organzed as follows. Seton gves an overvew of prevous work on tet deteton. Seton 3 desrbes the proposed algorthm n detal. Epermental results are eplaned n seton 4. Conluson remarks are gven n last seton.. Related work an methods for tet deteton n mages and vdeos have been publshed n the past. Based on the was beng used to loate tet regons most of the tet deteton tehnques an be lassfed as ether onneted omponent(cc) or teture based algorthms. The frst lass s based on the analss of the geometral arrangement of edges or homogeneous olor and grasale omponents that belong to haraters. The CC based algorthms are relatvel smple to mplement but the are not ver robust for tet loalzaton n mages wth omple bakground. Chen et al [3] deteted vertal and horzontal edges n an mage and dlated two knds of edges usng dfferent dlaton operators. Real tet regons are then dentfed b usng support vetor mahne. Zhong et al [4] etrated tet as those onneted omponents that follow ertan sze onstrants and horzontal algnment onstrants. Jan and Yu [5] also use onneted omponent analss to loalze tet anddates. But ths method an etrat onl horzontal tets of large szes. Proeedngs of the 005 Eght Internatonal Conferene on Doument Analss and Reognton (ICDAR 05) $ IEEE Authorzed lensed use lmted to: Natonal Tawan Unverst. Downloaded on arh 500 at 07:7:6 EDT from IEEE Xplore. Restrtons appl.

2 Teture-based methods are based on the fat that tets n mages have dstnt tetural propertes that an be used to dsrmnate them from the bakground or other non-tet regon. In general the tetured-based algorthms are more robust than the onneted omponent-based algorthms n dealng wth omple bakground [6]. The hgh omplet of teture segmentaton s the man drawbak of ths method. The algorthm proposed n [] usng teture features to etrat tet but faled n the ase of small font haraters. In [7] wavelet features from f-sze bloks of pels and lassf the feature vetors nto tet or non-tet usng neural networks. Beause the neural network based lassfaton s performed n the whole mage the deteton sstem s not ver effent n terms of omputaton ost. 3. Tet deteton algorthm In ontrast to prevous approahes our algorthm s a hbrd approah whh ombnes onneted omponent based and teture based method. Frst wth mage edge deteton result we generate onneted omponent of tet regon anddate effentl. Then teture analss n wavelet doman s performed on eah tet regon anddate to verf the tet regon and remove false alarms (.e. non-tet regons). In the followng we eplan and state the detals of four man steps of our algorthm n order of proessng. 3.. Edge Deteton In ths stage we should etrat prese edge nformaton of all tet regons and flter out edges of most non-tet regons n mage. An mportant fat we observed s that the pels representng tet ontour usuall have a hgh ontrast to ther neghbor pels. In our algorthm we use blak pels to represent the edge pels and whte to represent non-edge pels. Beause the edge detetor we adopted s dfferent from most estng sotrop edge detetor whh an not provde aurate edge dreton nformaton bas deas are frst presented below to llustrate the edge detetor. Some deas about the detetor have been desrbed n [8] Edge Detetor Gven a olor mage the dfferene vetor DV n rgb olor spae ndued b movng an nfntesmal step n the mage plane n the dreton {dd} s : DV=(d d) J T r r J r g r g g g b b b b where J s the Jaoban matr of the mage. The Euldean squared magntude of DV s DV =(d d) (d d) T where T J J (r ) (g ) (b ) r r g g b b (r ) (g ) (b ) and askng for the dreton of {dd} mamzng ths magntude s an egenvalue problem. We an obtan the magntude etremum n the dreton of the egenvetor of the matr and the etremum value s the orrespondng egenvalue. So we an get prese gradent magntude and dreton of eah mage pel b omputng orrespondng egenvalue and egenvetor dreton Edge Etraton Algorthm Frst medan flter was appled to the nput mage C to redue the nose of the nput mages whle preservng sharp edges. Then the matr s omputed for eah pel on the mage and we wll get a seres of egenvalues V and egenvetors E that reflet the gradent magntude and dreton of eah pel. As we know edge pels are those ponts wth loal mamum gradent magntude n ther gradent dreton. Furthermore we also note that the edges of tet smbols are tpall stronger than those of nose or bakground areas. So a pel (j) s aepted as an edge pel onl f t meets the followng two requrements. Frst the pel must have a larger gradent magntude than that of ts neghbor loated n the dreton losest to ts gradent dreton.e. the egenvalue of an edge pel must be greater than that of both two neghborng pels whh are losest to ts egenvetor dreton. Seond the pel gradent magntude (.e. egenvalue V(j)) must be greater than the adaptve threshold T to elmnate weak edges. T s determned b the followng formula: (V( j) Vdf ( j) ) (j) C T V ( j) (j) C df Proeedngs of the 005 Eght Internatonal Conferene on Doument Analss and Reognton (ICDAR 05) $ IEEE Authorzed lensed use lmted to: Natonal Tawan Unverst. Downloaded on arh 500 at 07:7:6 EDT from IEEE Xplore. Restrtons appl.

3 where V df s the orrespondng egenvalue dfferene between two neghborng pels losest to the egenvetor dreton of pel (j). To make harater edge more ontnuous ondtonal dlaton s performed on edge olleton obtaned n prevous operaton to onnet tet edges to form losed ontours. A 3 3 square struturng element wth the orgn at ts enter s seleted to dlate the mage edge. Furthermore the gradent magntude of enter pel must eeed T and the gradent dreton (orrespondng egenvetor dreton) dfferene between the enter pel and ts neghborng edge pel must be less than a onstant whh has been epermentall set to 0.6 about 5 degree. After ths all harater edge pels as well as some non-harater edge pels whh also show hgh loal olor ontrast are remaned n the mage edge map. Then we an lnk onneted edge pels through onneted omponent analss to generate anddate tet regons. 3.. Canddate tet regons generaton In ths phrase we frst need to group onneted edge pels nto dfferent regons. Unlke prevous work we etrat onneted omponent from tet ontour. Our onneted omponent analss s performed on blak edge pels generated n the prevous step. A onneted omponent s defned as a set of blak pels where eah pel s a dret neghbor of at least one other blak pel n the omponent. Our onneted omponent generaton basall follows the 8-neghborhood-onnetvt algorthm dsussed below. San the mage from left to rght and from top to bottom. Intalze the lass label of eah edge pel CL(j) to number 0. Gven an edge pel P eamne the four neghbors of P whh have alread been enountered n the san (.e. the neghbors to the left of P above t and the two upper dagonal terms). Then we an label P aordng to followng dfferent ases. If the lass number of all four neghbors are 0 assgn a new lass number to P else If onl one neghbor has lass number bgger than 0 and the gradent dreton dfferene between ths neghbor and P s less than assgn ths lass number to P else If more than one neghbor has lass number bgger than 0 and the gradent dreton dfferene among these neghbor pels and P s less than assgn one of the labels to P and make a note of the equvalenes else Assgn a new lass number to P. After ompletng the san of the whole mage the equvalent label pars are sorted nto equvalene lasses and a unque label s assgned to eah lass. Then a seond san s made through the whole mage durng whh eah label s replaed b the label assgned to ts equvalene lasses. Usng above algorthm edges of a harater an be lnked nto a losed ontour and we ma obtan man onneted omponents from edge map. The net stage s seletng part of the omponents as tet regon anddate and removng false alarms. To develop the rtera for flterng out non-tet regons the followng features of tet are observed: ) Average edge pel gradent magntude The frst fat s that the average edge pel gradent magntude s usuall hgher for tet than non-tet bloks. We an alulate ths average value of eah onneted omponent regon R wth followng formula: V( j) (j) R Vavg (CL( j) CLR ) m where m s the number of pels labeled wth one onneted omponent lass CL R. The V avg of a tet regon should be greater than *T. ) Edge gradent dreton varane Another mportant fat we noted s that tet regons have a hgher gradent dreton dstrbuton varane than graph regons. The varane of a regon an be estmated b ma mn where ma and mn are the mamum and mnmum edge gradent dreton angle respetvel n a onneted omponent regon. Wthn a tet regon the varane must be greater than PI (80 degree).e. ( ma mn ) > PI. 3) Edge pel number The thrd valuable fat we observed that tet blok should have more edge pels than some non-tet blok. The edge pel ount n a tet blok labeled as the same lass wthn a onneted omponent regon must be greater than AX( * W * H) where W and H are wdth and heght of the onneted omponent regon respetvel. Wth features desrbed above three rtera are appled on ever onneted omponent n order to rejet bloks onstruted of nose pels and lassf some non-tet bloks Tet regon verfaton The tet regon loalzaton desrbed n prevous seton ma produe false alarm whh also have hgh edge denst and strength. So we need to perform more aurate teture analss to lassf tet regons. Proeedngs of the 005 Eght Internatonal Conferene on Doument Analss and Reognton (ICDAR 05) $ IEEE Authorzed lensed use lmted to: Natonal Tawan Unverst. Downloaded on arh 500 at 07:7:6 EDT from IEEE Xplore. Restrtons appl.

4 Instead of performng a global teture analss on the whole mage we onsder ever regon of nterest separatel to remove false alarms whh makes our algorthm more effent and robust. Tet shows a rhthm spatal pattern dstrbuton whh onssts of a regular alternaton of ontrast hanges n one spef dreton. Therefore teture analss an be eploted to separate non tet regons lke graphs mages and other non-tet retangular homogeneous and hgh ontrast regons. We selet the Harr wavelet as the bass for teture haraterzaton beause of ts good ablt to haraterze teture features and ts omputaton effen [9]. Let () and () be the two Haar wavelet bases of one-dmensonal. k k ks () ( s) k k kt () ( t) Wth -dmensonal mage data the orrespondng tensor produt transform bass an be alulated as follows: LL HL k:st ( j) ks () kt ( j) k:st ( j) ks () kt ( j) LH HH k:st ( j) ks () kt ( j) k:st ( j) ks () kt ( j) Beause the nput mage s olored frst the nput mage s onverted nto gre-level mage I. Then the mage I s proessed wth dsrete wavelet transform and transformed nto four sub-bands LL HL LH and HH wth a -hannel flter bank (L: low pass flter H: hgh pass flter). After deomposng the mage I nto -D Haar wavelet we an ompute wavelet moment features to apture eah anddate tet regon teture propert. The wavelet energ feature of a pel (j) s defned as: ENG(j)= LH( j) HL( j) HH( j). Gven a tet regon anddate R wth N e edge pels labeled wth CL R the thrd order wavelet moment 3 (R) an be alulated as N e (j) R 3 (R)= 3 (ENG( j) ENG(R)) (CL( j) CL ) where ENG(R) s the mean energ feature of regon R and ENG(R)= ENG( j)(cl( j) CLR ) N. e (j) R For eah tet regon anddate ts thrd order wavelet moment s heked to verf whether t s a tet regon or not. The anddate regon R s rejeted as non-tet regon f ts thrd moment value s greater than T m whh an be alulated wth equaton (). R 3.4. Tet regon bnarzaton T m =T * N e *.5 () After we verf eah anddate tet regon wth ts teture propert we an bnarze eah tet regon separatel and feed the result to OCR. Although human ma pereve sngle harater wth the same olor appearane the atual pel olors ma var sgnfantl. So t s neessar to perform olor lusterng to ompensate for these effets. In eah tet regon we just want to luster all olors nto two dstntve olors to dsrmnate between tet and other non-tet part. So we use a mture model of Gaussans desrbed wth equaton () to dept the olor dstrbuton n tet regons where s an (r g b) vetor. Beause E algorthm s best suted for fttng Gaussan lusters we use t to estmate the dstrbuton parameter. The E algorthm terates b adjustng the parameters of the Gaussan probablt model to mamze the lkelhood of data n dataset. The teraton stops when the dfferene between two suessve teratons beomes neglgble. T n p() ep{ ( ) ( )} P()= () (d ) () where s the mean of luster s the ovarane matr of luster d s the dmensonalt of the data and d = 3 wth olor mage (rgb omponent). In our work we use the fllng algorthm to selet two dfferent olors of two pels nsde and outsde n tet ontour to arefull ntalze for E algorthm (but we have found that the ntalzaton has lttle effet on the qualt of the resultng lusterng proess from eperment results). Upon onvergene of the E algorthm the two mean vetors an be reorded as the two domnant olors n a tet regon.e. tet and non-tet part olor. Thus we an bnarze ths tet regon va measurng the Euldan dstane between eah pel olor and two mean olor vetors. 4. Epermental results Currentl our algorthm has been mplemented n C++ language under Wndows-XP on an EPSON Endeavor T7000 PC. To evaluate the atual performane of our proposed algorthm we tested 45 real olor mages whh nlude dfferent tpes of tets. The mage resoluton range s between 73*4 and 307*048 pels. Among Proeedngs of the 005 Eght Internatonal Conferene on Doument Analss and Reognton (ICDAR 05) $ IEEE Authorzed lensed use lmted to: Natonal Tawan Unverst. Downloaded on arh 500 at 07:7:6 EDT from IEEE Xplore. Restrtons appl.

5 them 00 mages are from the onferene ICDAR 003 sene tet deteton ompetton dataset (59 sene mages). Another 5 real olor mages are arefull hosen wth a wde varet of bakground omplet and tet tpes. Tet appearane vares wth dfferent olors orentaton and languages and the harater font sze n mages ranges from 8pt to 530pt. The lassfaton result s gven n table I. For measurng aura reall and false alarm rate are alulated to evaluate our algorthm performane. Reall rate s defned as follows number of deteted haraters [Reall rate]= 00%. number of haraters n mage False alarm rate s evaluated as follows [False alarm rate]= total pel ount n deteted non - tet regons 00%. number of pels n mage Epermental results show that our algorthm has a hgh reall rate wth low false alarm rate. Table I shows evdene that our teture analss led to small defts for reall rate but greatl redued the false alarm rate from.5% to 3.7%. Some false alarms left beause of ther strong teture features hgh ontrast and resemble tet-lke attrbutes. So we plan to ombne reognton engne to deteton proess to further redue the false alarm n future. Epermental results also show that the bnarzaton proedure wth E algorthm s also qute usable. Even when the olor dstrbuton nsde a regon s unmodal suh as haraters lke l et. the two mean vetors beome nearl ondent whh makes the bnarzaton proess more robust. 5. Conlusons In ths paper we propose a hbrd approah whh ombnes onneted omponent based and teture based methods to detet varant tets n olor mages. Frst an elaborate mage edge etraton algorthm and onneted omponent analss are used to etrat anddate tet regons. Then teture feature n wavelet doman s eplored to dentf tet regons from anddate regons. After tet regon verfaton E algorthm s ntrodued to bnarze all tet regons to prepare data for OCR. Epermental results demonstrate that our algorthm s ndependent from dfferent tet orentaton sze and language. Furthermore our algorthm an also detet an solated harater whh has no neghborng tet regon. Referenes [] Kwang In Km Keehul Jung and Jn Hung Teturebased approah for tet deteton n mages usng support vetor mahnes and ontnuousl adaptve mean shft algorthm IEEE Transatons on Pattern Analss and ahne Intellgene vol. 5 ssue pp De [] Yefeng Zheng Hupng L and Doermann D. Tet dentfaton n nos doument mages usng arkov random model In Proeedngs of the Seventh Internatonal Conferene on Doument analss and Reognton vol. pp Aug [3] Chen Datong Bourlard H. and Thran J. P. Tet dentfaton n omple bakground usng SV In Proeedngs of the IEEE Computer Soet Conferene on Computer Vson and Pattern Reognton vol. pp De. 00. [4] Y. Zhong K. Karu and A.K. Jan. Loatng Tet n Comple Color Images Pattern Reognton vol. 8 no. 0 pp Otober 995. [5] Jan A.k. and Bn Yu Automat tet loaton n mages and vdeo frames In Proeedngs of the Fourteenth Internatonal Conferene on Pattern Reognton vol. 6-0 pp Aug [6] Xaoou Tang Xnbo Gao Janzhuang Lu and Hongjang Zhang A spatal-temporal approah for vdeo apton deteton and reognton IEEE Transatons on Neural Networks vol. 3 ssue 4 pp Jul 00. [7] Fuj asafum. Wolfgang J. R. and Hoefer Fled- Sngulart orreton n -D tme-doman Haar-wavelet modelng of wavegude omponents IEEE Transatons on rowave Theor and Tehnques vol. 49 ssue 4 pp Apr. 00. [8] Lee H.-C. Cok D.R. Detetng Boundares n a Vetor Feld IEEE Transatons on Sgnal Proessng vol. 39 ssue 5 pp a 99. [9] ojslov A. Popov.V. and Rakov D.. On the seleton of an optmal wavelet bass for teture haraterzaton IEEE Transatons on Image Proessng vol. 9 ssue pp De. 000 Table I Tet Deteton Result Charater Language Charater font sze (pels) Charater Orentaton Englsh Japanese Chnese 8~00 0~300 30~530 Vertal Horzontal Arbtrar Number of haraters Before Teture Analss Reall rate 9.3% 90.5% 9.7% 9.4% 9.% 90.5% 90.8% 9.% 93.8% False alarm rate.5% After Teture Analss Reall rate 9.% 89.9% 9.% 9.3% 90.8% 88.4% 90.6% 90.9% 93.5% False alarm rate 3.7% Proeedngs of the 005 Eght Internatonal Conferene on Doument Analss and Reognton (ICDAR 05) $ IEEE Authorzed lensed use lmted to: Natonal Tawan Unverst. Downloaded on arh 500 at 07:7:6 EDT from IEEE Xplore. Restrtons appl.