IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 10, OCTOBER 1998

Text-Line Extraction and Character
Recognition of Document Headlines
With Graphical Designs Using
Complementary Similarity Measure
Minako Sawaki and
Norihiro Hagita, Member, IEEE Computer Society
Abstract—A method for recognizing characters on graphical designs
is proposed. A new projection feature that separates text-line regions
from backgrounds, and adaptive thresholding in displacement matchingare introduced. Experimental results for newspaper headlines withgraphical designs show a recognition rate of 97.7 percent.
Index Terms—Character recognition, OCR, character segmentation,
projection feature, displacement matching, adaptive thresholding.

———————— F ————————

1 INTRODUCTION
THE optical character reader (OCR) plays an important role as aconvenient device for transforming paper documents into a computer-readable format. One problem encountered by the OCR isthat headlines or emphasized strings are often printed on top ofgraphical designs. For example, graphical designs are often usedin Japanese newspapers to draw the reader’s attention to certain
articles. However, recognition of such decorated characters suffersfrom several problems:1) Characters often have a variety of graphical designs such as
black-plain or white-plain, outline, and textured characters(Figs. 1a-g).
2) Backgrounds also have a variety of graphical designs suchas white-plain, black-plain, or graphical designs over all orpart of the headline (Figs. 1a-g). Graphical designs sometimes change horizontally or vertically (Figs. 1e and 1f).
3) Text-lines can be printed horizontally or vertically (Figs. 1a-g).
A headline sometimes consists of several text-lines (Figs. 1dand 1e).
To solve these problems, several methods for removing thegraphical designs before recognition have been studied. Sakou etal. [1] focused on differences in the texture properties between thecharacter and background regions. Okamoto et al. [2] and Liang etal. [3] proposed methods to remove background texture iteratively
using morphological techniques. Ozawa et al. [4] attempted toseparate character and background regions using gray-scale images. However, these methods often extract incomplete regions ofcharacter parts, that is, character strokes are eliminated or background designs are left in segmented character images. This occurs
because the stroke configurations of complicated Kanji characters
can be quite similar to textured backgrounds.
This paper proposes a method for recognizing characters without
removing the graphical designs. Japanese newspaper headlines are²²²²²²²²²²²²²²²²• M. Sawaki is with NTT Basic Research Laboratories, 3-1 MorinosatoWakamiya, Atsugi, Kanagawa, 243-0198, Japan.
E-mail: minako@apollo3.brl.ntt.co.jp• N. Hagita is with NTT Communication Science Laboratories, 2-4 Hikaridai,
Seika-cho, Soraku-gun, Kyoto, 619-0237, Japan.
E-mail: hagita@cslab.kecl.ntt.co.jp.
Manuscript received 17 Feb. 1997; revised 20 July 1998. Recommended for acceptance by J. Hull.
For information on obtaining reprints of this article, please send e-mail to:tpami@computer.org, and reference IEEECS Log Number 107252.
0162-8828/98/$10.00 © 1998 IEEE1103

selected as an example of this task, as headlines usually include
keywords for queries, and their recognition is especially significant. The proposed method allows text-line regions to be separated
from backgrounds and is independent of the language of the
document. We have already proposed a robust recognition method
for characters with graphical designs and degraded characters [5].
It utilizes a binary image as a feature vector and the complementary similarity measure as a discriminant function. To apply thismethod to the recognition of headlines, the number of text-line
regions and character heights or widths must be extracted before
recognition. A text-line region is, in this paper, a rectangular region
that contains all characters in a text-line and its height or width is
the average height, or average width, of all characters in the textline, as shown in Fig. 5a.
First, a new projection value is introduced for separating thetext-line regions from backgrounds in the headline images. The
number of text-line regions and the averaged character heights are
extracted from a local distribution of the projection values. Next,
characters in the extracted text-line regions are recognized by displacement matching. When conventional methods recognize individual characters with no graphical designs, they select cut positions of individual characters in the text-line region. Features more
effective than projection profiles of black or white pixels have been
proposed for selecting the cut positions, such as the second differential of the marginal density [6], the upper- and lower-contour
shapes of the characters [7], and the break cost [8]. However, thesemethods cannot readily handle headline images with graphicaldesigns. Therefore, we perform recognition without selecting cut
positions. Displacement matching is applied for this purpose.
Kovalevsky formulated a recognition algorithm based on displacement matching for text-lines [9]. Casey and Nagy [10] also
applied this algorithm to the segmentation and classification ofcomposite character patterns. In displacement matching, a character candidate window is matched against reference patterns in adictionary using a similarity measure while being shifted pixel-bypixel along the horizontal axis. In Kovalevsky's algorithm, a directed graph is obtained from the distribution of the similarity
measure and window location. Recognition involves finding the
maximal path of the directed graph. Our character recognition
method is based on Kovalevsky's algorithm, and high recognition
reliability is achieved using the complementary similarity measure
which is sensitive to position translation and adaptive thresholding against the degree of degradation, instead of finding the
maximal path.
We introduce the complementary similarity measure in Section 2,
and a new projection measure for extracting the number of textline regions and the averaged character heights in Section 3. Section 4 describes our method of recognizing individual characters in
the text-line region by displacement matching and learning an
adaptive threshold value. Section 5 presents experimental results.
Section 6 presents conclusions.

2 COMPLEMENTARY SIMILARITY MEASURE
We take a brief look at the complementary similarity measure [5].
An input character is binarized and then normalized in size. The
normalized character pattern (n = N ¥ N pixels) is expressed as
an n-dimensional binary feature vector. Now, let

27

F = f1 , f2 , K , fi , K , fn (where f i = 0 or 1 )be the feature vector of an input character and2

7

T = t1 , t2 , K , ti , K , tn (where ti = 0 or 1 )be the feature vector of a binary reference pattern. The complementary similarity measure Sc of F to T is defined as

1104IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 10, OCTOBER 1998

(a)

(b)

(c)

(d)

(e)

(f)

(g)

Fig. 1. Examples of headline images. (a) Black-plain characters on white-plain background. (b) White-plain characters on textured background.
(c) Outline characters on textured background. (d) Textured characters on textured background. (e) Black-plain characters on textured background. (f) White-plain characters on textured background. (g) White-plain characters on textured background.

1 6

Sc F, T =a◊e -b◊c

1T◊ n-T

6

(1)

where
a=

∑ f ⋅ t , b = ∑ 21 − f 7 ⋅ t , c = ∑ f ⋅ 21 − t 7 ,
n

i =1

n

i

i

n

i

i =1

e=

i

i =1

i

i

∑ 21 − f 7 ⋅ 21 − t 7 ,
n

i

i =1

T = T ,

a+b + c + e = n.

The value of Sc varies in the range

1

i

6

− T n − T ≤ Sc ≤

1

(2)
(3)

6

T n−T .

When an input character F comes from T with deletion noise, cbecomes zero, and when F is derived from T with additive noise, b
becomes zero. In both cases, provided that the term b ⋅ c = 0 , thesimilarity values of these degraded patterns are still high. This
complementary relationship makes this measure robust against
noise. The complementary similarity measure Sc has the following characteristic against the reverse contrast pattern F c of F.

4

9

1 6

Sc F , T = −Sc F, T .
c(4)Equation (4) shows that by using the absolute value of thecomplementary similarity measure as a discriminant function,
input characters may be recognized regardless of character color.
In this paper, we use the measure Sc (absolute value of Sc ,0 ≤ Sc ≤1

Fig. 2 shows recognition rates for the complementary similarity
measure under the assumption that character heights or widths arecorrectly detected [5]. ETL2 (571 categories) was used, which isone of the standard machine-printed Kanji character databases
offered by the Electrotechnical Laboratory. It is noted that the reference patterns were black-plain characters on white-plain backgrounds. The measure achieves over 98 percent recognition accuracy with printed Kanji characters with graphical designs.

6

T n − T ) as a discriminant function in character rec-ognition, since newspaper headlines can have different character
colors. The dimension of the feature vector is n = 32 ¥ 32 = 1,024
pixels, which is large enough to express even complicated Kanjicharacters.

3 TEXT-LINE REGION EXTRACTION BASED ON
COMPLEMENTARY SIMILARITY MEASURE
This section introduces a new projection value for extracting textline regions [11]. The value stems from the complementary relationships between characters and backgrounds in terms of black
and white runs.
We assume that headline images are extracted from the newspaper, and that skews are corrected before text-line region extraction. This assumption is reasonable given conventional methods ofskew correction and text and headline image extraction [12].
Japanese newspaper headlines fall into five types in terms ofcharacter parts and backgrounds. Table 1 shows the occurrence ratesof these types for two of the main newspapers in Japan. Type V is
seldom used, because it is not legible.
With no graphical designs (Type I), the projection profile ofblack or white pixels enables us to detect the text-line regions [12].
However, the projection profile technique is not applicable forheadlines with graphical designs (Types II-IV). Therefore, we developed an alternative measure which estimates the number oftext-lines and the averaged character heights of text-line regionsthat suits Types II-IV as well as Type I. This measure focuses on thecomplementary relationship between characters and backgrounds.
That is, backgrounds usually are white-plain or black-plain for
textured characters, while they usually contain texture or the reverse contrast color for black-plain and white-plain characters.
These relationships are mixed for outline characters. Since textured

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 10, OCTOBER 1998

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Fig. 2. Recognition rates for characters with graphical designs [5] (Test samples in gothic style characters in newspapers in ETL2).

TABLE 1TYPES OF HEADLINE IMAGES IN JAPANESE NEWSPAPERS (ASAHI NEWSPAPER AND NIKKEI NEWSPAPER)

backgrounds can change gradually along the horizontal or verticalaxis, as shown in Figs. 1e and 1f, the number of text-lines and theircharacter heights are locally estimated using the projection profile
in a local rectangle window, which is shifted pixel-by-pixel along
the direction of the text-line.
In this paper, we will explain the algorithm for a headline withhorizontal text-lines. For a headline with vertical text-lines, thescanning direction is vertical.
Let G (Nx ¥ Ny pixels) be the input headline image and Gw(gw(u, y); W ¥ Ny pixels; u = 1, 2, ..., W; y = 1, 2, ..., Ny) be a rectangular window that can include at least one character. The projection value p(y) enhancing the difference between the characterparts and backgrounds for Types I-IV is defined by:ap ⋅ e p − bp ⋅ c p

27

py =

2

7 2

rT r − rt ⋅ rx r − rx

7

,

(5)

where

Â g 2u, y7 ◊ g 2u + 1, y7 ,

W -1

ap =

w

w

u=1

Â 31 - g 2u, y78 ◊ g 2u + 1, y7 ,

W -1

bp =

w

w

u=1

cp =
ep =

∑ g 2u, y7 ⋅ 31 − g 2u + 1, y78 ,

W −1
u =1

w

w

∑ 31 − g 2u, y78 ⋅ 31 − g 2u + 1, y78 ,(6)

rT = ap + bp ,

(7)

W −1
u =1

w

w

rX = ap + cp ,ap + bp + cp + e p = r .

(8)This projection profile makes use of the complementary relationship of these four parameters (ap, ep, bp, cp). They correspond to
the four possible changes of black and white pixels (black-to-black,white-to-white, white-to-black, and black-to-white) that can occur
along the scan direction of the text-line. Using these four parameters, p(y) is invariant for character color.
p(y) lies in the range −1 ≤ p y ≤ 1 . ap ◊ e p corresponds to the27

product of the total of the black run-length and the total of thewhite run-length, and bp ⋅ c p corresponds to the square of line
complexity in each scanning line. In general, ap ⋅ e p takes higher
values in character parts and lower ones in black-plain or whiteplain backgrounds. Also, bp ⋅ c p takes higher values for the textured backgrounds and lower ones for plain characters. As a result,p(y) takes higher values in character parts than in backgrounds for
Types I-IV. Since p(y) is the same expression as four-fold point correlation, a special case of the complementary similarity measure,p(y) comes from the complementary similarity measure.
The projection profile is averaged to avoid notches. The projection axis is divided into N sections and projection values p(y) areaveraged in each section. A group of high projection values in thevertical projection profile is then selected as a candidate of a textline region. Its range h corresponds to the character height at location Gw . When the headline consists of two text-lines, the verticalprojection profile generates two groups with high values. Morespecifically, when the averaged projection value exceeds 30 percentof the maximum of the projection profile, the section is determined
to be a text-line region. This threshold was defined empirically.
In practice, the text-line regions and character heights fluctuate
somewhat over all Gw due to the different character heights ateach location and the graphical designs. To avoid these notches,
the text-line region and its character height are averaged overall Gw .
Fig. 3 shows an example of the averaged projection profile ofp(y) for Gw and the projection profile of black pixels withoutsmoothing at x = 1. The two groups of high values in the averaged
projection profile of p(y) in Fig. 3b show the existence of two textline regions and their character heights, although the projection
values of black pixels in Fig. 3c do not indicate the text-line regions

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 10, OCTOBER 1998

(a)

(b)

(c)

Fig. 3. Observation window for region extraction and projection profile of p(y).

well. Fig. 4 shows the local distributions of p(y) for four types ofheadline images at x = 1. This figure shows that with the proposed
measure, the number of text-lines and the character heights are
estimated for headline images of Types I-IV.

4 CHARACTER RECOGNITION BASED ON
COMPLEMENTARY SIMILARITY MEASURE
4.1 Displacement Matching
Since it is difficult to select cut positions of individual characterseven with the proposed measure p(y), displacement matching isapplied to the extracted text-line region for character recognition.
When estimating text-line positions, text-line candidates are obtained for each observation window and the candidate positions areaveraged. However, for segmenting individual characters, this averaging effect is not available because a text-line has only one character in the vertical direction and the segmentation fails easily. Thoughdisplacement matching may require much processing time, thismethod seldom overlooks existing characters in a text-line.
One of the problems of displacement matching is that extra candidates may be encountered; that is, false categories may be selected

(a)

(b)

at locations where no correct character category is located [9]. In the
proposed method, the complementary similarity measure andadaptive thresholding suppress these spurious candidates. The
complementary similarity measure is sensitive to position translation and takes high values when the input character is matchedagainst the reference pattern of its character category while taking
low values for the other categories. These properties hold even forcharacters with graphical designs or noise. Therefore, whenever thesquare window is located at a correct character position, the similarity between the window and a reference pattern of the correct category is maximal; local peaks are observed in the distribution of
maximum similarity value only at correct character positions.
The extracted text-line region with height h is normalized toyield normalized text-line region L with height N. An N ¥ N-pixel
square window is selected for matching normalized text-line region L, since the binary reference patterns consist of N ¥ N pixels.
The square window is shifted pixel-by-pixel along the direction of
the text-line and the window’s contents, input feature vector F, arecompared to the reference patterns using the complementary
similarity measure. The binary reference patterns are made ofcharacter patterns without graphical designs in advance. As thebinary pattern matching method is used, the similarity decreases
significantly when the input is shifted in the vertical direction.
This shift may be due to fluctuation in text-line extraction. The
amount of pixel shift that can be tolerated mainly depends on
character stroke width because the similarity value is strongly
related to the number of pixels to which the input pattern andreference pattern overlap. For example, the stroke width of thereference patterns we use is within six pixels. Therefore, it is expected that vertical shifts of a few pixels (less than six) can be tolerated when these reference patterns are used.
Fig. 5 shows an example of the distribution of maximum
complementary similarity value and the maximum conventional
similarity value at each position along the horizontal axis for thenormalized text-line region. Fig. 5b and Fig. 5d show that thecomplementary similarity measure has dominant peaks aroundthe left-most side of F of the exactly correct category, while theconventional similarity measure S F , T = a F ◊ T , F = F has

1 6

few peaks.
(c)

(d)

Fig. 4. Local distributions of p(y) for various graphical designs. h: character height for each text-line region. (a) Type I. (b) Type II. (c) Type III.
(d) Type IV.

4.2 Adaptive Thresholding
When the maximum Sc at each position exceeds the threshold, therecognized category and its position are determined. To recognize

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 10, OCTOBER 1998

1107

Zi and Z be a noisy image of Ti and the number of black pixels in

2 7

Zi , respectively. Also, let Th Ti , Z be a threshold value for Ti

when the number of black pixels of an input pattern is Z. A noisy
image Zi is synthesized from Ti and a random dot image [5].
(a)4Similarity values Sc Zi , Tj

9 2 j π i7 are calculated for different Z.

Fig. 6 shows an example of the threshold-to-degradation table, that4is the relationship between the maximum Sc Zi , Tj

4

9

9 for j =Z. For comparison, Fig. 6 also shows Sc Z j , Tj for j =
(b)

and

. In order4

to eliminate false recognition candidates, the maximum Sc Zi , Tj

94 9
T . For S , we select max Th4T , Z9, Th4T , n - Z9 as the threshold.

is determined as the threshold Th Tj , Z for the reference pattern
(c)

j

c

j

j

Other reference patterns showed the same tendency as.

5 EXPERIMENTAL RESULTS(d)Fig. 5 Character recognition by displacement matching. (a) Normalized
text-line region L. (b) Maximum Sc (solid bars) and estimated thresh-

2 7olds Th Ti , Z (dot plots). (c) Recognized categories. (d) Maximum S.

characters with high precision, the most appropriate thresholdshould be determined using the degree of degradation in F and thereference pattern of the category with the maximum Sc value.
When F is located at the correct character position during displacement matching, Sc of F to the reference pattern of the correct
category decreases according to the degree of degradation while
Sc of F to the reference patterns of the other categories decrease
more rapidly. Therefore, by assuming that the square window F ateach position contains the reference pattern having the maximal
similarity value, the degree of degradation for F and the adaptive
threshold against the degree can be estimated.
The thresholds for different degradation degrees are determined in advance by a learning process for each reference pattern.
Let Ti be a reference pattern of the ith category without noise, and

5.1 Headline Data
As test data, we used 50 headlines, Types II-IV, in Japanesenewspapers (25 horizontal and 25 vertical text-lines) which contained 529 characters. They were gathered by using three binarization thresholds, level 1 (low), level 2 (fine), and level 3 (high).
The number of text-lines in one headline was either one or two.
The character font in the headlines was Gothic. Reference patterns without graphical designs were extracted manually from
121 headline images at level 2. Fifty headlines out of 121 were thesame as the test data; the remaining 71 were also gathered toincrease category number. As the aspect ratio of characters differs with text-line direction, the reference patterns were stored ineither a horizontal text-line dictionary or a vertical text-line dictionary according to the direction of the text-line of the headline.
The number of reference patterns was 913 (500 categories) for thehorizontal text-line dictionary and 988 (525 categories) for thevertical text-line dictionary. The numbers of reference patterns
per category in the horizontal dictionary and the vertical textline dictionary were in the range of one to 32 and one to 23, respectively. The horizontal text-line dictionary occupied 139 KB
while the vertical text-line dictionary took 150 KB.

Fig. 6. Relationship between complementary similarity and number of black pixels (Example for reference pattern

).

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 10, OCTOBER 1998

TABLE 2
RESULTS OF TEST-LINE EXTRACTIONTABLE 3
RECOGNITION RESULTSTABLE 4
RECOGNITION RESULTS FOR IMAGES IN FIG. 1 (LEVEL 2)

5.2 Text-Line Extraction
Experimental results for text-line extraction are listed in Table 2.
Table 2 shows the numbers of images whose all-text-lines werecorrectly extracted. We determined that text-lines in which all
characters were misrecognized as NOT correctly extracted. For
images at level 2, all text-lines were correctly extracted, while a
few errors occurred for images at levels 1 and 3. One of the reasonsfor error was that for the images with striped graphical designs
whose direction matched the projection direction, the projectedvalue is similar to that for the character regions and for the background regions. The average processing time was 10.2 seconds per
one headline image on a Sun Sparcstation 10.
For comparison, a conventional method was applied to the test
data with level 2 scanning. Graphical designs were removed using
the method in [2] and text-lines in the resulting images were extracted with commercial OCR software. The method in [2] extracts
character strokes based on morphological approach. The commercial OCR extracts text-lines using projection features of black pixels. This table shows that the proposed projection value achievesmuch higher text-line extraction rates than the conventional
method. The error of the conventional method comes from theconfusion of text regions with backgrounds and the confusion of
text and graphic regions.

5.3 Character Recognition
Character recognition was conducted for the test headline images
including images with text-line extraction errors. In recognition,
the horizontal (vertical) text-line dictionary was used when thewidth of the input headline image was larger (smaller) than its
height. When recognized characters overlapped each other, thecategory, which had the highest similarity, was kept and the others
were removed from the recognition results.
The recognition results for 50 test headline images are shown in
Table 3. Table 3 shows that the recognition rate for level 2 is 97.7 percent and the recognition rates for levels 1 and 3 are 81.3 percent and82.4 percent, respectively. The number of extra candidates are 40(level 1), 18 (level 2), and 43 (level 3), respectively. Recognition results
for the images in Fig. 1 are listed in Table 4. The average processing timewas 39.2 seconds per headline image on the Sun Sparcstation-10.
For comparison, a conventional method was applied to the test
data with level 2 scanning. Graphical designs were removed using
the method in [2] and the resulting images were recognized withcommercial OCR software. This OCR software did not learn thereference patterns we obtained from headline images. The recognition rate was 17.0 percent. This shows that the proposed method
achieves much higher recognition rates than the conventional
method, since it is noted that our test data did not include headline
images in Type I but in Types II-IV.
Recognition errors in our method fall into two main sources:
unsuccessful extraction of text-line regions and adaptive thresholding. Errors in adaptive thresholding occurred when the maximum similarity of the correct category was less than the estimatedthreshold. The complementary similarity measure achieves over 98
percent recognition accuracy against both learning and test samples when the character height is known [5]. The accuracy ofheadline image recognition will be improved by eliminating these
two sources of recognition error.

6 CONCLUSIONSWe proposed a method for text-line extraction and character recognition of Japanese newspaper headlines with graphical designs. A
new projection measure extracts text-line regions by focusing on thecomplementary relationship between characters and backgrounds.
Characters in the text-lines are recognized by the complementary
similarity measure and displacement matching. Spurious candidates
in displacement matching are suppressed by adaptive thresholding.
Experimental results for 50 newspaper headlines showed that thismethod achieves a high recognition rate of 97.7 percent, which ishigher than the 17.0 percent of a conventional method. Improving
text-line extraction and thresholding are future tasks.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE, VOL. 20, NO. 10, OCTOBER 1998

ACKNOWLEDGMENTS
We would like to acknowledge the encouragement and support ofDr. Ken’ichiro Ishii and stimulating discussions with Dr. Kazumi
Odaka.

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