Handwritten Japanese Address Recognition Technique Based on Improved Phased Search of Candidate Rectangle Lattice

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1 Hnwrittn Jpns Arss Rognition Thniqu Bs on Improv Phs Srh o Cnit Rtngl Ltti Hihis NAKAYAMA, Msto SUZUKI, Ni KATO, n Yoshiki NEMOTO Grut Shool o Inormtion Sins, Tohoku Univrsity z-ao 05, Armki, Ao-ku, Sni-shi, Miygi, Jpn Dprtmnt o Computr Sin, Tokyo Ntionl Collg o Thnology Kunugi-mhi , Hhioji-shi, Tokyo, Jpn ABSTRACT In th il o hnwrittn Jpns rss rognition, it is ommon to rogniz pl-nm strings rom plnm imgs. Howvr, in prti, it is nssry to rogniz th pl-nm strings rom rss imgs. Thror, w hv propos th post-prossing systm, whih hks th list o th pl-nm strings in two-stgs or rognizing th pl-nm imgs. In this ppr, w propos nw thniqu s on phs srh o nit rtngl ltti, n improv th thniqu with th ttion o ky-hrtrs or inl output. Applying our proposl to th IPTP 1840 img t o rss strings, th rsults o xprimnts lrly show th iiny o our systm in hnwrittn Jpns rss rognition. Kywors Hnwrittn Jpns Arss Rognition, Arss Img, Cnit Rtngl Ltti, Ky-Chrtrs, Phs Srh n Post-prossing. 1. INTRODUCTION A Jpns rss string onsists o two lmnts, whih r pl-nm string n pl-numr string. Hnwrittn Jpns rss rognition sts gol to r th pl-nm string rom its rss-nm img on th postr without rrring to th zip o. In th gnrl systm, n input img is sgmnt n iniviul hrtrs r rogniz s ths mrg sgmnts. In [1, w n s som prolms with sgmnttion. Whil th Jpns hrtrs r writtn iniviully, th string writtn y rush(u) is ompos o hrtrs wl togthr n thus it is iiult to sgmnt n rss img. Thror, it is not suiint nough to rogniz h hrtr in hnwrittn rss imgs. Th gnrl solution is post-prossing, whih tks into ount th inormtion out th lngug strutur. Cnit rtngl hrtr mtho [2 is rprsnttiv o ll th post-prossings, ut vrious rlt thniqus hv n propos. Prviously in [3[4, w propos two-stg lssiition systm y pting this mtho. Th mthing sor o pl-nm string is lult rom th hrtrs within th ltti strutur. Howvr, in th s o just using nit rtngl ltti, th post-prossing rquirs hug mounts o prossing tim. Thror, w hv init tht it is ruil to hk th list o pl-nm strings in two-stgs or tiv postprossing [4. Our thniqu hs improv th tr-o twn sp n prision y rough lssiition n in lssiition o th pl-nm strings. A hrtr rognition omptition or hnwrittn knji hrtrs on postrs ws hl y th Institut or Posts n Tlommunitions Poliy (IPTP) or vloping thonologis in this il [5. All propos mthos in th omptition wr s on th sm onpt. Unr this onpt, our thniqu n onsir goo solution to hnwrittn Jpns rss rognition. Howvr, in prti, it is nssry to rogniz th pl-nm string rom its rss img [6 (Fig.1). Pl-nm String Pl-numr String Fig. 1 Arss Img Applying th two-stg lssiition systm to n rss img, th ring ury gos own whn th rsults o rognizing pl-numr img hv inlun on this systm. In ition, sin suh systm hs possiility o mismhing uring th rsoning pross, w onsir tht 64

2 th rognition ury oms lss i th systm rognizs th pl-nm string rom its rss-nm img only. In this ppr, w propos th st solution to improv this prolm. Th min i is to us nit rtngl ltti n to put th rsults o hrtr rognition into nit rtngl ltti. Th sgmnts o n rss img must orrspon to hrtrs within th pl-nm string. W sign th post-prossing s on phs srh o nit rtngl ltti. Tking rjtion into ount, th rsults o our xprimnts lrly show th iny o our thniqu. This ppr is orgniz s ollows: Stion 2 sris rognition systm rom n img to th string, n xplins nit rtngl ltti out our tritionl mthos. Th Jpns rss rognition thniqu s on phs srh o nit rtngl ltti is propos in Stion 3, n som xmpls o th sors r prsnt. Th xprimntl rsults or th IPTP 1840 rss img t is rport in Stion 4. In Stion 5, w improvs our propos mtho with xtn ltti srh or inl output. Finlly som onlusions r rwn in Stion RECOGNITION SYSTEM Our rognition systm hs th ollowing our mouls. Img Pr-prossing: Pr-prossing th input img (256 lvl gry-sl) or img sgmnttion into th high qulity inry imgs. This pross onsists o thr stps: Contrst Strthing [7, Eg Prsrving Smoothing [8 n Otsu s Binriz Mtho [9. Img Sgmnttion: Sgmnting th inry img into rtngls with lk ots n strok inormtion (. [1[10). Chrtr Rognition: Rognizing hrtrs with th improv irtionl lmnt tur [11 n th moii mhrnois istn [12. Post-Prossing: Duing th propr pl-nm string rom th rsults o rogniz hrtrs. Fig.2 shows n outlin o post-prossing. Cnit rtngl ltti As rsult o img sgmnttion, w ll unit sgmnt s rtngl n ontinuous sgmnts mrg rtngls. Both o th s rtngl n mrg rtngls ompos hrtr nit rtngl. Th rss img is rprsnt y th nit rtngl ltti y mns o irt grph: nit rtngl ltti is th vrtx n th onntion twn th g o nit rtngls. Th lt prt o Fig.3 shows th originl img n th rm o th rtngl, n th right prt o Fig.3 shows th nit rtngl ltti. Lt {,,, } st o s rtngls, {,,, } st o mrg rtngls, n st, n th vrtx o th strt/n point o th string. On this nit rtngl ltti, thr is plurl rout n h rout nots th lin o nit rtngls. In orr to otin th orrt rsult o rss rognition, systm ns to slt th propr lin o nit rtngls. Two-stg lssiition Two-stg lssiition is th post-prossing lgorithm(fig.4). In th ormr stps, nit rtngl ltti with mthing sors o its hrtrs is gnrt rom th rss img. Th list o pl-nm strings is m orhn rom th Jpns rss ts. This ts ontins ll th rsss in Jpn n hs out 170,000 ntris, rsulting in out 800,000 pl-nm strings. Th mthing sor o pl-nm string is lult rom th hrtrs within th ltti strutur. Lt S = s 1,s 2,,s n th pl-nm hrtrs, n X = x 1,x 2,,x n nit rtngls. Th st U(x i ) onsists o hrtrs rsulting y rognizing x i (1 i n). Inlusion in th rsults o hrtr rognition is rprsnt y s i U(x i ). Th ttion o s i rom th i-th pl-nm hrtr is in s: { 1 s i U(x i ) (s i,u(x i )) = (1) 0 othrwis Th ttion ount is in s: (s i,u(x i )) (2) Whn th systm hs to nrrow own th pl-nm lists, Eq.(3) is our stnr or th limintion o unnssry lists. (s i,u(x i )) < n (3) 2 It shoul not tht ll th prmtrs in this ppr pn on [4. Cnit Rtngl Ltti o Pl-nm Strings Fig. 2 Rognition Systm using Post-Prossing 65

3 st [Sgmnttion Positions [Rognition Chrtrs Chrtr Inx Cnit Rtngls Rsults o Chr.R. Ltti Strutur Sttistis or Chr.R. Pl-nm Chrtrs Originl Simplii Dtil Phs 1 Phs 2 Phs 3 [Post-prossing Pl-nm string Fig. 3 Cnit Rtngl Ltti n Fig. 5 Propos Mtho Th sor o rough lssi- Rough Clssition: tion is in s: [ L A (S, X) = 1 n (s i,u(x i )) (4) n Aoring to Eq.(4), ll th pl-nm lists r soring. Th systm omprs th sor o h pl-nm string with th thrshol. I thr is only on string whos sor xs th thrshol, th string oms th inl rsult o th omputtion tr rough lssiition. Othrwis, w pro to in lssiition o th strings with sor xing th thrshol. Fin Clssiition: During th rough lssiition, nit rtngl x i is givn. k (x i ) is th k-th istn o x i n th rtio o k (x i ) to k (x k ) is not s th stt o onin. Th sor o Fin lssition is in s: L B (S, X) = 1 n [ n 1 (x i ) (5) k (x i ) Finl output is th string tht hs th st sor tr pplying Eq.(5). [Sgmnttion Positions [Rognition Chrtrs Chrtr Inx Originl Rough Clssiition Cnit Rtngls Rsults o Chr.R. Rough Clssii Rough Ltti Strutur Fin Clssiition Fig. 4 Two-stg Clssiition Fin Clssii Fin 3. PROPOSED METHOD In th two-stg lssiition [3[4, it is only suiint to rogniz th pl-nm string rom its pl-nm img. This works on th ssumption tht th numr o nit rtngls quls to tht o pl-nm hrtrs. Howvr, in this ppr, tht rstrition must rmov. Thn, it is th t tht two-stg lssiition osn t kp th propr sor, whih is to th st sor o ll pl-nm lists. W onsir th us to rtngl orrsponing to th hrtr on th pl-numr string. In orr to rogniz th hrtrs on pl-nm strings with high ury, w improv th ollowing two points: Without rough lssiition, srhing th propr plnm on ltti to lr ths orrsponns. R-srhing ltti with rognizing th hrtrs on th stlish pl-nm string ovr gin. W propos nw mtho s on th phs srh o nit rtngl ltti. Outlin This mtho onsists o th ollowing thr phss (Fig.5). Phs 1: Rrring to th hrtr-inx n lgging hrtrs in th pl-nm string. Phs 2: Srhing nit rtngl ltti with th rsults o hrtr rognition. Phs 3: R-srhing th ltti with rognizing mismthing hrtrs. During thr phss ov, th orrsponn o nit rtngls with pl-nm hrtrs is onirm. Ruing th numr o pl-nm lists Th ttion ounts nrrow own th pl-nm lists, so w hv th ollowing two onitions. Corrsponn with pl-nm string. (s i,u(x i )) < n 2 66

4 st st g h i j k l th prt o pl-nm string gh ij kl Fig. 6 Rognition Rsults Cs 1 Sltion rom nits. (s i,u(x i )) < mx 2 whr mx is th mximum ount in ll th numrs o pl-nm lists. Srhing th Ltti Mthing th vrtxs with th rsults o hrtr rognition on th ltti, th nits rom pl-nm strings r slt. For xmpl, w srh th ollowing two pl-nms rom th nit rtngl ltti(fig.6), Pl-nm 1: S 1 =,,,,,,, U(gh) ls to X =,,,,,, gh, ij Pl-nm 2: S 2 =,,,,,,,, X =,,,,,, g, h, i or X =,,,,,, g, h, ij or X =,,,,,,gh,i,j or X =,,,,,, gh, ij, k Th rtngl with th irst rsult o hrtr rognition is ix t th irst srh, th lgg vrtx is ix on ltti. Th rtngl on Fig.6 osn t hv th irst rsult o hrtr rognition, ut it oms ix utomtilly tr th uniqu pss ixs th ront n rr o th rtngl. Thror, ll rtngls orrspon to h hrtr o pl-nm string t Pl-nm 1. Howvr, t Pl-nm 2, som nit strings r ssum, sin th irst rsult,, os not xist. Tking noti o this t, w propos nw i o phs srh with orrsponn twn th nit rtngl n th hrtr o pl-nm string. At Phs 2, nits r slt using only th irst rsult o hrtr rognition. At Phs 3, our systm r-srhs th ltti with rognizing mismthing rtngls in orr to lr th orrsponn. Furthrmor, th ttion ounts nrrow own th pl-nm lists, so w hv th ollowing two onitions t Phs 2. Corrsponn with pl-nm string. (s i,u(x i )) n 2 n g h i j Rog. g hij n Fig. 7 Rognition Rsults Cs 2 Sltion rom nits. (s i,u(x i )) mx 2 Rog. Mthing sor Th mthing sor is vry importnt in iing th orrt nits. W minly improv th point tht th istn o hrtr rognition hs n inlun on srhing th ltti. Tht is, L C (S, X) is in s: [ L C (S, X) = 1 n mx 1 n (s i,u(x i )) [ n 1 (x i ) (6) k (x i ) Sin w hv introu mx into srhing th ltti, Eq.(6) rprsnts th ttion rt o th hrtrs. Exmpl This stion givs xmpls o vlus L A, L B n L C, whih r in in s.2 n s.3. Fig.7 is th nit rtngl ltti X. Thr r thr pl-nms low. Corrt pl-nm string: S 0 =,,,, (This is just, s rsult o rognition) Inorrt pl-nm string 1: S 1 =,,,, (This is long, s rsult o rognition) Inorrt pl-nm string 2: S 2 =,, (This is short, s rsult o rognition) Applying Eq.(4)(5) to ths thr strings, w r l to gt th ollowing vlus. Rough lssiition[4 67

5 L A (S 0,X)= S=50 E-R S L A (S 1,X)= S=40 L A (S 2,X) = L A (S 2,X) > L A (S 1,X)=L A (S 0,X) rjt rt R[% S=30 S=20 rjt onition Fin lssiition[3 L B (S 0,X)=60.14 L B (S 1,X)=59.64 L B (S 2,X)=83.48 L B (S 2,X) > L B (S 0,X) > L B (S 1,X) Both outputs o using L A n L B r S 2. But ths rsults r mismthing. Bus ths qutions hv inorrt long or short pl-nm strings, thy giv n vntg ovr orrt just pl-nm strings. Although L A n t istinguish S 0 n S 1,L B n istinguish S 0 n S 1. Thror, oring to ths vlus, th sors o hrtr rognition woul improv th rsult o lssiition. Applying Eq.(6) to thos pl-nm strings, th output is S 0. By using mx (= 5), Eq.(6) ls to th orrt rsult S=10 (Rmin) rror rt E[% Fig. 8 E R Curv Exprimntl Rsults In th ol systm using L A n L B,Sis In th nw systm using L C,Sis Thror th improvmnt is S = Whn w invstigt th mismthing t y using L A n L B, th numr o th strings tht hv n rror is 76. Th rrors in 53 strings r minly us y stimting th short string. Spiilly, 51 strings om orrt strings y using L C. Fig.9 lrly shows th iiny o our systm. It shoul point out tht th highst vlu o S ws out 60 in th IPTP omptition. Propos mtho L C (S 0,X)= S=90 S=80 S=70 E-R(nw) S(nw) E-R(ol) S(ol) L C (S 1,X)=86.92 L C (S 2,X)=69.95 L C (S 0,X) > L C (S 1,X) > L C (S 2,X) rjt rt R[% S=60 S=50 S=40 S=30 S=20 4. EXPERIMENTS ON RECOGNITION SYSTEM Applying our proposl to th IPTP 1840 img t o rss strings [5, th rsult o xprimnts is xprss in th Error-Rjt Curv n Point S. S is systm ost, in s: S =10E + R whr E nots th rror rt (%) n R nots th rjt rt (%). W st two thrshols: THD n THR. THD mns th thrshol or th vlu o th mhing sors. THR mns th thrshol or th rtio o istns twn th mhing sors. Ths thrshols n writtn s th ollowing onitions. L(S 1,X) < THD L(S 1,X) < L(S 2,X) THR whr L(S 1,X) is th st sor, L(S 2,X) is th son st sor. W n plot th xprimntl rsults otin y ssigning irnt vlus to thrshols THD n THR. On th grph in Fig.8, w only show th optiml urv. W in optiml s th monoton rsing urv tht hs th lowst R whn E is onstnt S= rror rt E[% Fig. 9 Exprimntl rsults or th IPTP 1840 t 5. IMPROVED METHOD For th strings tht wr mismth, w rri out stuy or th rson hin th rrors. W oun out tht son orr ons wr tru or most o th ss n tht th propos mtho n urthr moiition. W will isuss out this in this stion. Extn Ltti Srh In th rss img shown in Fig.1, thr is prt ll pl-numr string. This prt is not us in ommon rognition systm. But, or urthr improvmnt, w will implmnt ltti srh or this prt too. W ll this ltti Extn Ltti (Fig.10). In our priliminry stuy [13, w hv oun tht, th 30 kyhrtrs shown in Tl 1 r inlu in xtn ltti in most o th ss. 68

6 [Sgmnttion Positions Cnit Rtngls Sttistis or Chr.R. st [Rognition Chrtrs Chrtr Inx Originl Rsults o Chr.R. Simplii Ltti Strutur Dtil Phs 1 Phs 2 Phs 3 [Post-prossing Pl-nm string Pl-nm Chrtrs Extn Ltti Srh Ky Chrtrs [Finl output Pl-nm string Fig. 10 Improv Mtho Tl 1 th 30 ky-hrtrs in th pl-numr string prt Numrl hrtrs Chins hrtrs Spil hrtrs g h i j g h g n Rog. Extn Ltti In, w onsir th sor o irst orr to L C1 n th sor o son orr to L C2. Whn th rtio (L C1 /L C2 ) o ths sors is lss thn r i (r i =1.2), w i it s rsmln n whn ths ky-hrtrs r tt, urthr lultion is on. Nxt, th nw vlu o sors r us or inl output. Exmpl out improv mtho Fig.11 is th nit rtngl ltti X. Thr r two pl-nms low. First orr pl-nm string: S 4 =,,,, (This is inorrt. Th lngth o S 4 is 5(= n 4 ).) Son orr pl-nm string: S 5 =,, (This is orrt.th lngth o S 4 is 3(= n 5 ).) W lult th sors o S 4 n S 5 using th mtho mntion in Stion 3, s ollows. Propos mtho L C (S 4,X)=92.90 L C (S 5,X)=88.77 L C (S 4,X) > L C (S 5,X) Hr, th rtio is 1.05 whih is lss thn r i. Thror, s shown in Fig. 11, n = n4 n5 =2n, U(), U() So, w rlult th sors, s ollows. Improv mtho L C(S 4,X)=92.90 L C(S 5,X)=98.77 Fig. 11 Rognition Rsults Cs 3 L C(S 4,X) < L C(S 5,X) In this wy, or th strings hving rsmln, y solving th prolm tht riss whn n >0, improvmnt o th rsults o Finl output is possil. Exprimntl Rsults out improv mtho Applying our improv proposl to th IPTP 1840 img t o rss strings. Th rsults r gthr in Fig.12, whih shows th prntg o orrt strings without rjtion. orrt rognition rt [% improv mtho tritionl mtho propos mtho rognition orr Fig. 12 Corrt rognition rt (umult rognition rt) Apprntly, th propos mtho is ttr ompr to th tritionl mtho. As whol, in s o th improv mtho, vn ttr rsults ompr to thos o th propos mtho wr otin. For xmpl, n improvmnt o 0.5 point (10 strings) ws possil or Son orr. Th irn twn th propos mtho n th 69

7 Improv mtho my look nominl, ut us th numr o th ntir postl rs in Jpn is wll ovr 75 hunr million (2003, [6), th 0.1% quivlnt is 7.5 million. Thus, rom th viwpoint o th tul postl nvironmnt, this irn n not nglt t ll. 6. CONCLUSION In this ppr, w ppli th two-stg rognition pproh to rss img rognition n isuss rlt issus. Th i hin our proposl is to us nit rtngl ltti with rognizing th hrtrs tht oul not sussully rogniz t th irst phs o rognition. Th otin rsults r us to stimt th vlu o pl strings. By so oing, w oul rmrkly improv th rss rognition ury. W implmnt our proposl on hnwrittn rss rognition systm, n th xprimntl rsults isply th trrii prormn o our propos mtho in trms o systm ost (S =29.78). In ition, w improv our propos mtho using xtn ltti srh with th ttion o th ky-hrtrs. W propos th high ury hnwrittn Jpns rss rognition systm. W woul lik to mphsiz tht in our stuis, w minly ous on th rognition ury n tht mor work shoul on on th rspons tim o th systm. Bus non-hrtr rtngls otn rsult in mismthing in our systm, w think tht th utur tsk in this il is urthr rsrh in Img sgmnttion. Furthrmor, th ppr [14 hs us 3589 smpls or th xprimnt. So, w shoul lso onsir out using lrgr tsts. Aknowlgmnt W woul lik to knowlg th Institut or Posts n Tlommunitions Poliy or proviing th IPTP 1840 rss img t n th Avn Inustril Sin n Thnology or proviing th ETL hrtr ts. Rrns [1 H. Ino, K. Srut t l.: Hnwrittn rss sgmnttion lgorithm s on strok inormtion, Trns. IPS Jpn, Vol. 38, No. 2, pp (1997 2). [2 H. Murs, M. Shiny t l.: Sgmnttion n rognition o hn-writtn hrtr string using linguisti inormtion, Trns. IEICE Jpn, Vol. J69- D, No. 9, pp (1986 9). [3 M. Suzuki, H. A t l.: Two-stg mtho o hnwrittn rss rognition using ynmi strtion o hrtr nits, Trns. IEICE Jpn, Vol. J82-D-II, No. 11, pp ( ). [4 K. Tokumoto, M. Suzuki t l.: Rognition systm or hnwrittn rss using rough lssiition mtho lrt y pplying priority to rss nits, Trns. IEICE Jpn, Vol. J84- DII, No. 1, pp (2001 1). [5 F. Kwmt, T. Wkhr t l.: Th rsults o th thir IPTP hrtr rognition omptition or hnwrittn knji hrtr on post rs, Pro. o IEICE Fll Con. 94, D-321 (1994 9). [6 Ministry o Posts n Tlommunitions: Nwly stlish Jpns postl os mnul. [7 M. Tkgi n H. Shimo(suprvis): Img nlysis hnook, Univrsity o Tokyo Prss (1991). [8 M.Ngo n T.Mtsuym: Eg prsrving smoothing, Computr Grphis n Img Prossing, Vol. 9, pp (1979 4). [9 N. Otsu: An utomti thrshol sltion mtho s on isriminnt n lst squrs ritri, Trns. IEICE Jpn, Vol. J63-D, No. 4, pp (1980 4). [10 H. Nkym, M. Suzuki t l.: A stuy o itiv hrtr sgmnttion or hnwrittn rss rognition, 2001 Tohoku-stion joint onvntion o instituts o ltril n inormtion nginrs, Jpn, 2G-17 (2001 8). [11 N. Sun, M. A n Y. Nmoto: A hnwrittn hrtr rognition systm y using improv irtionl lmnt tur n susp mtho, Trns. IEICE Jpn, Vol. J78-D-II, No. 6, pp (1995 6). [12 N. Kto, M. A n Y. Nmoto: A hnwrittn hrtr rognition systm using moii mhlnois istn, Trns. IEICE Jpn, Vol. J72-D- II, No. 1, pp (1996 1). [13 H. Nkym, M. Suzuki t l.: An inrn mtho o pl-nm prt or hnwrittn rss rognition, 2000 Tohoku-stion joint onvntion o instituts o ltril n inormtion nginrs, Jpn, 2D-6 (2000 8). [14 C.-L. Liu, M. Kog t l.: Lxion-rivn sgmnttion n rognition o hnwrittn hrtr strings or Jpns rss ring, IEEE Trns. on Pttrn Anlysis n Mhin Intllign, Vol. 24, No. 11, pp ( ). 70