COMPLEMENTARY SIMILARITY MEASURE

Transcription

1 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 0, OCTOBER Text-Lie Extractio ad Character Recogitio of Documet Headlies With Graphical Desigs Usig Complemetary Similarity Measure Miako Sawaki ad Norihiro Hagita, Member, IEEE Computer Society Abstract A method for recogizig characters o graphical desigs is proposed. A ew projectio feature that separates text-lie regios from backgrouds, ad adaptive thresholdig i displacemet matchig are itroduced. Experimetal results for ewspaper headlies with graphical desigs show a recogitio rate of 97.7 percet. Idex Terms Character recogitio, OCR, character segmetatio, projectio feature, displacemet matchig, adaptive thresholdig. F INTRODUCTION THE optical character reader (OCR) plays a importat role as a coveiet device for trasformig paper documets ito a computer-readable format. Oe problem ecoutered by the OCR is that headlies or emphasized strigs are ofte prited o top of graphical desigs. For example, graphical desigs are ofte used i Japaese ewspapers to draw the reader s attetio to certai articles. However, recogitio of such decorated characters suffers from several problems: ) Characters ofte have a variety of graphical desigs such as black-plai or white-plai, outlie, ad textured characters (Figs. a-g). 2) Backgrouds also have a variety of graphical desigs such as white-plai, black-plai, or graphical desigs over all or part of the headlie (Figs. a-g). Graphical desigs sometimes chage horizotally or vertically (Figs. e ad f). 3) Text-lies ca be prited horizotally or vertically (Figs. a-g). A headlie sometimes cosists of several text-lies (Figs. d ad e). To solve these problems, several methods for removig the graphical desigs before recogitio have bee studied. Sakou et al. [] focused o differeces i the texture properties betwee the character ad backgroud regios. Okamoto et al. [2] ad Liag et al. [3] proposed methods to remove backgroud texture iteratively usig morphological techiques. Ozawa et al. [4] attempted to separate character ad backgroud regios usig gray-scale images. However, these methods ofte extract icomplete regios of character parts, that is, character strokes are elimiated or backgroud desigs are left i segmeted character images. This occurs because the stroke cofiguratios of complicated Kaji characters ca be quite similar to textured backgrouds. This paper proposes a method for recogizig characters without removig the graphical desigs. Japaese ewspaper headlies are ²²²²²²²²²²²²²²²² M. Sawaki is with NTT Basic Research Laboratories, 3- Moriosato- Wakamiya, Atsugi, Kaagawa, , Japa. miako@apollo3.brl.tt.co.jp N. Hagita is with NTT Commuicatio Sciece Laboratories, 2-4 Hikaridai, Seika-cho, Soraku-gu, Kyoto, , Japa. hagita@cslab.kecl.tt.co.jp. Mauscript received 7 Feb. 997; revised 20 July 998. Recommeded for acceptace by J. Hull. For iformatio o obtaiig reprits of this article, please sed to: tpami@computer.org, ad referece IEEECS Log Number selected as a example of this task, as headlies usually iclude keywords for queries, ad their recogitio is especially sigificat. The proposed method allows text-lie regios to be separated from backgrouds ad is idepedet of the laguage of the documet. We have already proposed a robust recogitio method for characters with graphical desigs ad degraded characters [5]. It utilizes a biary image as a feature vector ad the complemetary similarity measure as a discrimiat fuctio. To apply this method to the recogitio of headlies, the umber of text-lie regios ad character heights or widths must be extracted before recogitio. A text-lie regio is, i this paper, a rectagular regio that cotais all characters i a text-lie ad its height or width is the average height, or average width, of all characters i the textlie, as show i Fig. 5a. First, a ew projectio value is itroduced for separatig the text-lie regios from backgrouds i the headlie images. The umber of text-lie regios ad the averaged character heights are extracted from a local distributio of the projectio values. Next, characters i the extracted text-lie regios are recogized by displacemet matchig. Whe covetioal methods recogize idividual characters with o graphical desigs, they select cut positios of idividual characters i the text-lie regio. Features more effective tha projectio profiles of black or white pixels have bee proposed for selectig the cut positios, such as the secod differetial of the margial desity [6], the upper- ad lower-cotour shapes of the characters [7], ad the break cost [8]. However, these methods caot readily hadle headlie images with graphical desigs. Therefore, we perform recogitio without selectig cut positios. Displacemet matchig is applied for this purpose. Kovalevsky formulated a recogitio algorithm based o displacemet matchig for text-lies [9]. Casey ad Nagy [0] also applied this algorithm to the segmetatio ad classificatio of composite character patters. I displacemet matchig, a character cadidate widow is matched agaist referece patters i a dictioary usig a similarity measure while beig shifted pixel-bypixel alog the horizotal axis. I Kovalevsky's algorithm, a directed graph is obtaied from the distributio of the similarity measure ad widow locatio. Recogitio ivolves fidig the maximal path of the directed graph. Our character recogitio method is based o Kovalevsky's algorithm, ad high recogitio reliability is achieved usig the complemetary similarity measure which is sesitive to positio traslatio ad adaptive thresholdig agaist the degree of degradatio, istead of fidig the maximal path. We itroduce the complemetary similarity measure i Sectio 2, ad a ew projectio measure for extractig the umber of textlie regios ad the averaged character heights i Sectio 3. Sectio 4 describes our method of recogizig idividual characters i the text-lie regio by displacemet matchig ad learig a adaptive threshold value. Sectio 5 presets experimetal results. Sectio 6 presets coclusios. 2 COMPLEMENTARY SIMILARITY MEASURE We take a brief look at the complemetary similarity measure [5]. A iput character is biarized ad the ormalized i size. The ormalized character patter ( = N N pixels) is expressed as a -dimesioal biary feature vector. Now, let 2 7 (where f i = 0 F = f, f2, K, fi, K, f or ) be the feature vector of a iput character ad T = 2t, t2, K, ti, K, t7 (where t i = 0 or ) be the feature vector of a biary referece patter. The complemetary similarity measure S c of F to T is defied as /98/$ IEEE

2 04 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 0, OCTOBER 998 (a) (b) (c) (d) (e) (f) (g) Fig.. Examples of headlie images. (a) Black-plai characters o white-plai backgroud. (b) White-plai characters o textured backgroud. (c) Outlie characters o textured backgroud. (d) Textured characters o textured backgroud. (e) Black-plai characters o textured backgroud. (f) White-plai characters o textured backgroud. (g) White-plai characters o textured backgroud. where a e b c S F,T6 c T - = -T 6 a = fi ti, b = fi ti The value of S c 2 7, c = fi ti e = fi ti 2 7, () , (2) T = T, a + b + c + e =. (3) varies i the rage 6 c 6. T T S T T Whe a iput character F comes from T with deletio oise, c becomes zero, ad whe F is derived from T with additive oise, b becomes zero. I both cases, provided that the term b c = 0, the similarity values of these degraded patters are still high. This complemetary relatioship makes this measure robust agaist oise. The complemetary similarity measure S c has the followig characteristic agaist the reverse cotrast patter F c of F. c Sc4 F, T 9 = S F,T c 6. (4) Equatio (4) shows that by usig the absolute value of the complemetary similarity measure as a discrimiat fuctio, iput characters may be recogized regardless of character color. I this paper, we use the measure S c (absolute value of S c, 0 Sc T T6 ) as a discrimiat fuctio i character recogitio, sice ewspaper headlies ca have differet character colors. The dimesio of the feature vector is = =,024 pixels, which is large eough to express eve complicated Kaji characters. Fig. 2 shows recogitio rates for the complemetary similarity measure uder the assumptio that character heights or widths are correctly detected [5]. ETL2 (57 categories) was used, which is oe of the stadard machie-prited Kaji character databases offered by the Electrotechical Laboratory. It is oted that the referece patters were black-plai characters o white-plai backgrouds. The measure achieves over 98 percet recogitio accuracy with prited Kaji characters with graphical desigs. 3 TEXT-LINE REGION EXTRACTION BASED ON COMPLEMENTARY SIMILARITY MEASURE This sectio itroduces a ew projectio value for extractig textlie regios []. The value stems from the complemetary relatioships betwee characters ad backgrouds i terms of black ad white rus. We assume that headlie images are extracted from the ewspaper, ad that skews are corrected before text-lie regio extractio. This assumptio is reasoable give covetioal methods of skew correctio ad text ad headlie image extractio [2]. Japaese ewspaper headlies fall ito five types i terms of character parts ad backgrouds. Table shows the occurrece rates of these types for two of the mai ewspapers i Japa. Type V is seldom used, because it is ot legible. With o graphical desigs (Type I), the projectio profile of black or white pixels eables us to detect the text-lie regios [2]. However, the projectio profile techique is ot applicable for headlies with graphical desigs (Types II-IV). Therefore, we developed a alterative measure which estimates the umber of text-lies ad the averaged character heights of text-lie regios that suits Types II-IV as well as Type I. This measure focuses o the complemetary relatioship betwee characters ad backgrouds. That is, backgrouds usually are white-plai or black-plai for textured characters, while they usually cotai texture or the reverse cotrast color for black-plai ad white-plai characters. These relatioships are mixed for outlie characters. Sice textured

3 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 0, OCTOBER Fig. 2. Recogitio rates for characters with graphical desigs [5] (Test samples i gothic style characters i ewspapers i ETL2). TABLE TYPES OF HEADLINE IMAGES IN JAPANESE NEWSPAPERS (ASAHI NEWSPAPER AND NIKKEI NEWSPAPER) backgrouds ca chage gradually alog the horizotal or vertical axis, as show i Figs. e ad f, the umber of text-lies ad their character heights are locally estimated usig the projectio profile i a local rectagle widow, which is shifted pixel-by-pixel alog the directio of the text-lie. I this paper, we will explai the algorithm for a headlie with horizotal text-lies. For a headlie with vertical text-lies, the scaig directio is vertical. Let G (N x N y pixels) be the iput headlie image ad G w (g w (u, y); W N y pixels; u =, 2,..., W; y =, 2,..., N y ) be a rectagular widow that ca iclude at least oe character. The projectio value p(y) ehacig the differece betwee the character parts ad backgrouds for Types I-IV is defied by: where 27 py = W - ap ep bp cp r r r r r r T t x x ap = Â g u, w y gw u +, y, W bp = Â - gw u, y gw u+, y W , cp = g u, w y gw u +, y , (5) W ep = gw u, y 3 gw u +, y8, (6) rt = ap + bp, rx = ap + cp, (7) ap + bp + cp + ep = r. (8) This projectio profile makes use of the complemetary relatioship of these four parameters (a p, e p, b p, c p ). They correspod to the four possible chages of black ad white pixels (black-to-black,, white-to-white, white-to-black, ad black-to-white) that ca occur alog the sca directio of the text-lie. Usig these four parameters, p(y) is ivariat for character color. p(y) lies i the rage py 27. ap ep correspods to the product of the total of the black ru-legth ad the total of the white ru-legth, ad bp cp correspods to the square of lie complexity i each scaig lie. I geeral, ap ep takes higher values i character parts ad lower oes i black-plai or whiteplai backgrouds. Also, bp cp takes higher values for the textured backgrouds ad lower oes for plai characters. As a result, p(y) takes higher values i character parts tha i backgrouds for Types I-IV. Sice p(y) is the same expressio as four-fold poit correlatio, a special case of the complemetary similarity measure, p(y) comes from the complemetary similarity measure. The projectio profile is averaged to avoid otches. The projectio axis is divided ito N sectios ad projectio values p(y) are averaged i each sectio. A group of high projectio values i the vertical projectio profile is the selected as a cadidate of a textlie regio. Its rage h correspods to the character height at locatio G w. Whe the headlie cosists of two text-lies, the vertical projectio profile geerates two groups with high values. More specifically, whe the averaged projectio value exceeds 30 percet of the maximum of the projectio profile, the sectio is determied to be a text-lie regio. This threshold was defied empirically. I practice, the text-lie regios ad character heights fluctuate somewhat over all G w due to the differet character heights at each locatio ad the graphical desigs. To avoid these otches, the text-lie regio ad its character height are averaged over all G w. Fig. 3 shows a example of the averaged projectio profile of p(y) for G w ad the projectio profile of black pixels without smoothig at x =. The two groups of high values i the averaged projectio profile of p(y) i Fig. 3b show the existece of two textlie regios ad their character heights, although the projectio values of black pixels i Fig. 3c do ot idicate the text-lie regios

4 06 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 0, OCTOBER 998 (a) (b) (c) Fig. 3. Observatio widow for regio extractio ad projectio profile of p(y). well. Fig. 4 shows the local distributios of p(y) for four types of headlie images at x =. This figure shows that with the proposed measure, the umber of text-lies ad the character heights are estimated for headlie images of Types I-IV. 4 CHARACTER RECOGNITION BASED ON COMPLEMENTARY SIMILARITY MEASURE 4. Displacemet Matchig Sice it is difficult to select cut positios of idividual characters eve with the proposed measure p(y), displacemet matchig is applied to the extracted text-lie regio for character recogitio. Whe estimatig text-lie positios, text-lie cadidates are obtaied for each observatio widow ad the cadidate positios are averaged. However, for segmetig idividual characters, this averagig effect is ot available because a text-lie has oly oe character i the vertical directio ad the segmetatio fails easily. Though displacemet matchig may require much processig time, this method seldom overlooks existig characters i a text-lie. Oe of the problems of displacemet matchig is that extra cadidates may be ecoutered; that is, false categories may be selected (a) (c) Fig. 4. Local distributios of p(y) for various graphical desigs. h: character height for each text-lie regio. (a) Type I. (b) Type II. (c) Type III. (d) Type IV. (b) (d) at locatios where o correct character category is located [9]. I the proposed method, the complemetary similarity measure ad adaptive thresholdig suppress these spurious cadidates. The complemetary similarity measure is sesitive to positio traslatio ad takes high values whe the iput character is matched agaist the referece patter of its character category while takig low values for the other categories. These properties hold eve for characters with graphical desigs or oise. Therefore, wheever the square widow is located at a correct character positio, the similarity betwee the widow ad a referece patter of the correct category is maximal; local peaks are observed i the distributio of maximum similarity value oly at correct character positios. The extracted text-lie regio with height h is ormalized to yield ormalized text-lie regio L with height N. A N N-pixel square widow is selected for matchig ormalized text-lie regio L, sice the biary referece patters cosist of N N pixels. The square widow is shifted pixel-by-pixel alog the directio of the text-lie ad the widow s cotets, iput feature vector F, are compared to the referece patters usig the complemetary similarity measure. The biary referece patters are made of character patters without graphical desigs i advace. As the biary patter matchig method is used, the similarity decreases sigificatly whe the iput is shifted i the vertical directio. This shift may be due to fluctuatio i text-lie extractio. The amout of pixel shift that ca be tolerated maily depeds o character stroke width because the similarity value is strogly related to the umber of pixels to which the iput patter ad referece patter overlap. For example, the stroke width of the referece patters we use is withi six pixels. Therefore, it is expected that vertical shifts of a few pixels (less tha six) ca be tolerated whe these referece patters are used. Fig. 5 shows a example of the distributio of maximum complemetary similarity value ad the maximum covetioal similarity value at each positio alog the horizotal axis for the ormalized text-lie regio. Fig. 5b ad Fig. 5d show that the complemetary similarity measure has domiat peaks aroud the left-most side of F of the exactly correct category, while the covetioal similarity measure SFT, 6 = a F T, F = F has few peaks. 4.2 Adaptive Thresholdig Whe the maximum S c at each positio exceeds the threshold, the recogized category ad its positio are determied. To recogize

5 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 0, OCTOBER (a) (b) (c) (d) Fig. 5 Character recogitio by displacemet matchig. (a) Normalized text-lie regio L. (b) Maximum S c (solid bars) ad estimated thresholds Th 2T i,z7(dot plots). (c) Recogized categories. (d) Maximum S. characters with high precisio, the most appropriate threshold should be determied usig the degree of degradatio i F ad the referece patter of the category with the maximum S c value. Whe F is located at the correct character positio durig displacemet matchig, S c of F to the referece patter of the correct category decreases accordig to the degree of degradatio while S c of F to the referece patters of the other categories decrease more rapidly. Therefore, by assumig that the square widow F at each positio cotais the referece patter havig the maximal similarity value, the degree of degradatio for F ad the adaptive threshold agaist the degree ca be estimated. The thresholds for differet degradatio degrees are determied i advace by a learig process for each referece patter. Let T i be a referece patter of the ith category without oise, ad Z i ad Z be a oisy image of T i ad the umber of black pixels i Z i, respectively. Also, let Th T, Z 2 i 7 be a threshold value for T i whe the umber of black pixels of a iput patter is Z. A oisy image Z i is sythesized from T i ad a radom dot image [5]. Similarity values Sc4Zi, Tj92j π i7 are calculated for differet Z. Fig. 6 shows a example of the threshold-to-degradatio table, that is the relatioship betwee the maximum S c Z i, T j Z. For compariso, Fig. 6 also shows S c j j 4 9 for j = ad 4Z, T9 for j =. I order 4Z, T9 4T j, Z9 for the referece patter 4Tj 9 4Tj - 9 as the threshold. to elimiate false recogitio cadidates, the maximum S c i j is determied as the threshold Th T j. For S c, we select max Th, Z, Th, Z Other referece patters showed the same tedecy as. 5 EXPERIMENTAL RESULTS 5. Headlie Data As test data, we used 50 headlies, Types II-IV, i Japaese ewspapers (25 horizotal ad 25 vertical text-lies) which cotaied 529 characters. They were gathered by usig three biarizatio thresholds, level (low), level 2 (fie), ad level 3 (high). The umber of text-lies i oe headlie was either oe or two. The character fot i the headlies was Gothic. Referece patters without graphical desigs were extracted maually from 2 headlie images at level 2. Fifty headlies out of 2 were the same as the test data; the remaiig 7 were also gathered to icrease category umber. As the aspect ratio of characters differs with text-lie directio, the referece patters were stored i either a horizotal text-lie dictioary or a vertical text-lie dictioary accordig to the directio of the text-lie of the headlie. The umber of referece patters was 93 (500 categories) for the horizotal text-lie dictioary ad 988 (525 categories) for the vertical text-lie dictioary. The umbers of referece patters per category i the horizotal dictioary ad the vertical textlie dictioary were i the rage of oe to 32 ad oe to 23, respectively. The horizotal text-lie dictioary occupied 39 KB while the vertical text-lie dictioary took 50 KB. Fig. 6. Relatioship betwee complemetary similarity ad umber of black pixels (Example for referece patter ).

6 08 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 0, OCTOBER 998 TABLE 2 RESULTS OF TEST-LINE EXTRACTION TABLE 3 RECOGNITION RESULTS TABLE 4 RECOGNITION RESULTS FOR IMAGES IN FIG. (LEVEL 2) 5.2 Text-Lie Extractio Experimetal results for text-lie extractio are listed i Table 2. Table 2 shows the umbers of images whose all-text-lies were correctly extracted. We determied that text-lies i which all characters were misrecogized as NOT correctly extracted. For images at level 2, all text-lies were correctly extracted, while a few errors occurred for images at levels ad 3. Oe of the reasos for error was that for the images with striped graphical desigs whose directio matched the projectio directio, the projected value is similar to that for the character regios ad for the backgroud regios. The average processig time was 0.2 secods per oe headlie image o a Su Sparcstatio 0. For compariso, a covetioal method was applied to the test data with level 2 scaig. Graphical desigs were removed usig the method i [2] ad text-lies i the resultig images were extracted with commercial OCR software. The method i [2] extracts character strokes based o morphological approach. The commercial OCR extracts text-lies usig projectio features of black pixels. This table shows that the proposed projectio value achieves much higher text-lie extractio rates tha the covetioal method. The error of the covetioal method comes from the cofusio of text regios with backgrouds ad the cofusio of text ad graphic regios. 5.3 Character Recogitio Character recogitio was coducted for the test headlie images icludig images with text-lie extractio errors. I recogitio, the horizotal (vertical) text-lie dictioary was used whe the width of the iput headlie image was larger (smaller) tha its height. Whe recogized characters overlapped each other, the category, which had the highest similarity, was kept ad the others were removed from the recogitio results. The recogitio results for 50 test headlie images are show i Table 3. Table 3 shows that the recogitio rate for level 2 is 97.7 percet ad the recogitio rates for levels ad 3 are 8.3 percet ad 82.4 percet, respectively. The umber of extra cadidates are 40 (level ), 8 (level 2), ad 43 (level 3), respectively. Recogitio results for the images i Fig. are listed i Table 4. The average processig time was 39.2 secods per headlie image o the Su Sparcstatio-0. For compariso, a covetioal method was applied to the test data with level 2 scaig. Graphical desigs were removed usig the method i [2] ad the resultig images were recogized with commercial OCR software. This OCR software did ot lear the referece patters we obtaied from headlie images. The recogitio rate was 7.0 percet. This shows that the proposed method achieves much higher recogitio rates tha the covetioal method, sice it is oted that our test data did ot iclude headlie images i Type I but i Types II-IV. Recogitio errors i our method fall ito two mai sources: usuccessful extractio of text-lie regios ad adaptive thresholdig. Errors i adaptive thresholdig occurred whe the maximum similarity of the correct category was less tha the estimated threshold. The complemetary similarity measure achieves over 98 percet recogitio accuracy agaist both learig ad test samples whe the character height is kow [5]. The accuracy of headlie image recogitio will be improved by elimiatig these two sources of recogitio error. 6 CONCLUSIONS We proposed a method for text-lie extractio ad character recogitio of Japaese ewspaper headlies with graphical desigs. A ew projectio measure extracts text-lie regios by focusig o the complemetary relatioship betwee characters ad backgrouds. Characters i the text-lies are recogized by the complemetary similarity measure ad displacemet matchig. Spurious cadidates i displacemet matchig are suppressed by adaptive thresholdig. Experimetal results for 50 ewspaper headlies showed that this method achieves a high recogitio rate of 97.7 percet, which is higher tha the 7.0 percet of a covetioal method. Improvig text-lie extractio ad thresholdig are future tasks.

7 IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 0, OCTOBER ACKNOWLEDGMENTS We would like to ackowledge the ecouragemet ad support of Dr. Ke ichiro Ishii ad stimulatig discussios with Dr. Kazumi Odaka. REFERENCES [] H. Sakou, H. Matsushima, ad M. Ejiri, Texture Discrimiatio Usig Self-Orgaized Multiresolutio Filterig, IEICE Tras. D- II, vol. J73-D-II, o. 4, pp , 990 (i Japaese). [2] M. Okamoto ad H. Hayashi, Character Extractio From Headlies With Backgroud Patters by Usig Shrikig/Expadig Methods, IEICE, Techical Report PRU90-5, pp , 99 (i Japaese). [3] S. Liag ad M. Ahmadi, A Morphological Approach to Text Strig Extractio from Regular Periodic Overlappig Text/Backgroud Images, Computer Visio, Graphics, ad Image Processig, vol. 56, o. 5, pp , Sept [4] H. Ozawa ad T. Nakagawa, "A Character Image Ehacemet Method From Characters With Various Backgroud Images, Proc. Secod It l Cof. Documet Aalysis ad Recogitio, pp. 58-6, Tsukuba, Japa, Oct [5] M. Sawaki ad N. Hagita, Recogitio of Degraded Machie- Prited Characters Usig a Complemetary Similarity Measure ad Error-Correctio Learig", IEICE Tras. Iformatio ad Systems, vol. E79-D, o. 5, pp , 996. [6] S. Kaha ad T. Pavlidis, O the Recogitio of Prited Characters of Ay Fot ad Size", IEEE Tras. Patter Aalysis ad Machie Itelligece, vol. 9, o. 3, pp , 987. [7] R. Ferich, Segmetatio of Automatically Located Hadwritte Words, Proc. Secod It l Workshop o Frotiers i Hadwritig Recogitio, pp , Chateau de Boas, Frace, 99. [8] S. Tsujimoto ad H. Asada, Major Compoets of a Complete Text Readig System, Proc. IEEE, vol. 80, o. 7, pp.,33-,49, 992. [9] V.A. Kovalevsky, Image Patter Recogitio. Berli: Spriger, 980. [0] R.G. Casey ad G. Nagy, Recursive Segmetatio ad Classificatio of Composite Character Patters, Proc. Sixth It l Cof. Patter Recogitio, pp.,023-,026, 982. [] M. Sawaki ad N. Hagita, Text-Lie Extractio ad Character Recogitio of Japaese Newspaper Headlies With Graphical Desigs, Proc. 3th It l Cof. Patter Recogitio, Track C, pp , 996. [2] T. Akiyama ad N. Hagita, Automated Etry System for Prited Documets, Patter Recogitio, vol. 23, o., pp.,4-,54, 990.