We proposed a rotated character recognition
	method using eigen-subspace for alpha-numeric characters so
	far. We first construct an eigen-subspace for each category
	using the covariance matrix calculated from a sufficient
	number of rotated character patterns. Next, we can obtain a
	locus by projecting their rotated characters onto the eigen sub-space
	and interpolating between their projected points. An
	unknown character is also projected onto the eigen subspace
	of each category. A single projection and multiple projections
	of the input character image were proposed. Then, the
	verification is carried out by calculating the distance between
	the projected points of the unknown character and the locus.
	Then the multiple projections showed a higher accuracy at low
	dimensions than a single projection for alphanumeric 62
	categories. This time, we applied it for the first class of
	Japanese Industrial Standard (JIS) Kanji set which includes
	2,965 categories. As the result, very high recognition accuracy
	over 99.8% was achieved by especially multiple projections of
	the input rotated images

Baba, Yuta

Hase, Hiroyuki

Sato, Shiori

Tokai, Shogo

Community Repository of Fukui

International Workshop on Advanced Image Technology 2013
Rotated Japanese Character Recognition
  Yuta Baba, Shiori Sato, Hiroyuki Hase and Shogo Tokai 
      Graduate School of Engineering, University of Fukui
          3-9-1 Bunkyo, Fukui-city, 910-8507, Japan
{y-baba,  s-sato}@monju.fuis.u-tlikui.ac.jp  {haseh,tokai}@u-fulcui.acjp
 Abstract—We proposed a rotated character recognition 
method using eigen-subspace for alpha-numeric characters o 
far. We first construct an eigen-subspace for each category 
using the covariance matrix calculated from a sufficient 
number of rotated character patterns. Next, we can obtain a 
locus by projecting their rotated characters onto the eigen sub-
space and interpolating between their projected points. An 
unknown character  is also projected onto the eigen sub-space 
of each category. A single projection and multiple projections 
of the input character image were proposed. Then, the 
verification is carried out by calculating the distance between 
the projected points of the unknown character and the locus. 
Then the multiple projections howed a higher accuracy at low 
dimensions than a single projection for alphanumeric 62 
categories. This time, we applied it for the first class of 
Japanese Industrial Standard (JIS) Kanji set which includes 
2,965 categories. As the result, very high recognition accuracy 
over 99.8% was achieved by especially multiple projections of 
the input rotated images. 
 Index  Terms—character r cognition, rotated  character,  eigen-
subspace, multiple  projections.
                I. INTRODUCTION 
 Some researches on rotated character recognition have 
been reported so  far[1,2]. Recently, we proposed a new 
scheme on rotated character ecognition[4] which was 
based on the parametric eigen-space method[3]. And also, 
the other esearch covering 3D rotation of a character image 
has been proposed[5]. Both of them targeted 62 categories 
of alphanumeric letters,. However, these are the same 
method using eigen-subspace. 
 In this paper, we apply this method for Japanese 
characters of First class of  JIS Kanji which includes 2,965
categories. We experimented using our method for 2D 
rotated Kanji characters. 
 In character recognition research, we generally collect a 
lot of sample images from the real world, then we develop 
effective feature xtraction and/or ecognition algorithm to 
absorb the intra-class variation, lastly, the recognition 
performance is evaluated by experiments. An open database 
of character images may be sometimes u ed instead of data 
collection. Printed  Kanji image database, ETL2 was opened 
in 1973[6]. This database consists of 2,184 categories that 
contains 50,000 data collected from the patent journals and 
newspapers. But, the number of categories of this database 
is not enough and collected ata is uneven depending on 
the category and there xists a category which contains only 
two data. From this meaning, ETL2 is inadequate for our 
research. Actually, collecting data evenly for all categories 
is very difficult. In a practical study of character 
recognition, we must reconsider the recognition result 
depending on data quality or the method. Deterioration f 
recognition rate is caused by many factors, i.e. image noise, 
font, feature xtraction, recognition method, and so on. In 
this paper, we handle noise-free character image data, 
because those factors affect he system performance and it 
makes ample image collection easy. Getting these data are 
difficult in the real world. But, we try to keep a higher 
image quality as good as possible when we acquire images. 
Therefore, you can refer to the results of this study as the 
nearly best result when you could get higher quality image. 
In this study, the recognition rate depends on only three 
factors, that is, a lack of information by reducing image size 
and limited dimensions, and a lot of competing categories.
 
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International Workshop on Advanced Image Technology 2013
In our previous work, some  simulations[4] using ideal 
character images were first carried out, then, we got good 
results close to the simulation result by developing a real-
time system[8] with a camera. 
  In this simulation, we used automatically generated 
character images. As the results, we could obtain  very high 
accuracy even  lbr any angle of character image of any 
category of First Class  ol'3IS Kanji.
 IL KANJI  IMAGE GENERATION A D RECOGNITION  METHOD 
 In this section, automatic  Kanji character image 
generation is described first, and then the learning and the 
recognition method are  presented[4]. 
A.  Kanji image  generrnion 
 Kanji images are automatically generated by using Free 
Type library that is  software font  engine[7]. By this  program, 
we can get any  fonts and any size of bitmap Kanji images. 
 We generated binary images of First class of  S Kanji 
with gothic font and  128x128 pixels. 36 character images 
rotated by  10 degrees are used for learning process in each 
category, and these image sizes  arc changed into 50x50 
pixels after extracting the bounding square of a character 
area. We prepared  51 images for recognition  test that are 
from 7 degrees to 357 degrees rotated by 7 degrees. 
Therefore 5 images used for the learning are included in test 
images in each  category_ In the previous  paper[8], between 
the recognition results experiment using 32x32 binary 
image for alphanumeric letters and the experimental results 
 using 8x8 pixels with 17 levels, it was no  difference in 
recognition  rate_ So, in this work, we converted from  50x50 
binary image to 8x8 pixels 65 levels image. You may think 
that a 8x8 pixels image is very coarse for Kanji pattern, but 
8x8 pixels images keep their information by the pixel value 
of 65 levels.  This  feature representation s one of the easy 
methods. Computing speed becomes very fast by this 
resizing. In subsection 3.1, we compare the recognition 
performance byimage size.
B. Learning  process 
 For  example, a 50x50 binary image can  be described by a 
2,500 dimensional vector. The value of a pixel  is  0 or  1. a 
8x8 image with 65 levels can be described by a 64 
dimensional vector. In general, let an image pattern be f(,), 
where k is the category number, 0(i) is a character angle, 
that is  OW  0,1,2,•  • ,35. 
 Next, we create the eigen-space using 36 image data with 
respect o each category. The covariance matrix  V" is 
calculated as follows; 
 Eik) =Ek—ink)(lkinkY (I)       ..oo) 
 where  nik is the mean  vector of the k -th category.  The 
covariance matrix can be obtained through eigen expansion; 
 E(k)0 = Ach (2) 
 where, category index  k was omitted for  A.  and  0  . We 
obtain at most 35 non-zero eigenvalues b cause the rank of 
the covariance matrix is at most 35. Let the eigenvectors 
corresponding to  eigenvalues  ,•  -•  , -35 be  0.,02,•••,035 
Using the  first  n (<35)  eigenvectors, an eigen-subspace 
0k)  =  {A,0),.-•,0„) canbe created. 
 Then, as projected  A0(1'  =0,1,.  • ,35) onto the  0,k), that 
               E is, the projected pointamkis(JnOfkink , a set  of the 
              (77k 
projected pointskiIdraws a locus sequentially because 
the angle changes consecutively. We denote the locus as 
/1(:) . The locus by 36 points can be interpolated. In this 
research, 360 points were interpolated by the periodic  spline 
interpolation. The angle  of an interpolation point is  given 
integer value by dividing two angles between and 
 140 by  ten.  Therefore, the precision f angle stimation is 
at least one degree. We show loci of  "" and  "" on 2D 
 eigen-subspace that their character shapes  arc similar in 
Figure 1,  but their  loci are quite different in this case.
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International Workshop on Advanced Image Technology 2013
                       4-*  
                                                       ./
 I) 
 
1  OD  8U  (.1-/0 3
•2 
 4')
 Hl)
bU
         (a)  category"*" 
 Fis.1.1 Examples of  loci(category  ".* and  *.")
                   e"..•"- 
                               ••-.                           ir,
                  ••--. 
       4•             .*4•• ..1'41%1...---s4‘.   H:160 :t.4(tiO.-v-...,•.4.4(1 ilO • 00  00 
           • 
  4 • 
     4 4— 4-- • 4. ••••# 
            S.  ,4- Z---
.• .               •. 
 HO
                      (b)  category"" 
 Fig.  l Examples of  loei(catcgory  "-* and  *.") 
 C. Recognition process 
 (a) Recognition by simple  projection 
 An unknown image x is first projected onto all  t  ;,k) 
(k  =1,2,•  •  •,C). We will denote he projected point of x as 
          (01  X, that is X =Un (x—mk). Verification s carried out by 
finding the shortest distance b tween X and  /Ink) . We 
represent theshortest distance to the category k as  dk  (X)  .
Therefore we can obtain the recognition result  k* as 
follows; 
 k*  =  arg  min  {cik  (X)} (3) 
 The angle of the unknown image is given by the angle of 
closest point on the locus. In this way, we can obtain the 
recognition result and the character angle of the input image 
at the same  time. We show the recognition scheme by 
simple projection n Figure 2.
 k=1 k=2k=C 
                                    mur 
         ^iih„ •  •  •41^4 
 (X),01  d2  (X),02 de (X),Oc 
                                    ,6*** 
          Fig.2 Recognition scheme bysimple projection 
 (b) Recognition  hi,  nndtiple  projections 
 In the previous method, there may be many cases that he 
projected point is accidentally close to the locus of other 
category.  Toprevent this misclassification by accident, we 
proposed a method that creates multiple images by rotation 
 of an unknown  image[4J. These images are projected onto 
every eigen-subspaces. We obtained higher accuracy  for 
alphanumeric letters. We denote the number of created 
multiple images including the original image as R . For 
example, in the case  of  R =  3  , two images are created by 
rotating by 120 degrees and 240 degrees. In our previous 
work, we changed R from  1 to 5, as the result, R=3 or 5 
showed higher  performance than R=2 or 4. In addition, 
recognition by  simple projection isthe case  of R  =1. 
 Figure 3 shows a recognition scheme  of multiple 
projections in case of R  =3  , the distance  of the category is
defined as the average distance among three distances. 
 k* =  arg  inin[E{d  . (X)}1 (4) 
 k  j 
Where,  E{.} is average operation. 
                 Projection        R
otation 
 Unknown • 
         L  dt.1•)() 
  
•')-- 
 13 
                       ,"4 (AT•• , 
                            d,(X 
         Fig.3 Recognition scheme by multiple projections
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International Workshop on Advanced Image Technology 2013
 The angle is estimated to the angle of corresponding point 
on the locus to the original image. 
 In addition, we adopta reasonable r striction that the 
angles of corresponding adjacent points on the locus should 
have the same angle  difference because the rotation angles 
of multiple images are known. So, adjacent corresponding 
points on the locus must have the same angle difference of 
120 degrees in case of Figure 3. In the previous paper[4], 
we did not use this restriction. This restriction is one of 
ingenuities for handling alot of categories. 
   III. EXPERIMENTAL RESULTS AND CONSIDERATIONS 
A. Recognition  performance forsize of character image 
 In recognition experiment by simple projection, Figure 4 
shows recognition rates by the simple projection using the 
first n dimensions for  50x50 binary images and 8  x 8 65 
levels images. The recognition rate using the first 35 
dimensions i 99.80% for  50x50 character images, and 
99.74%(150,826 correct per 151,215 samples) for  8X8 
character images. Both results are very high. 
 The accuracy for 8x8 character images is lower than the 
one of  50x50 character images at low dimensions. But, the 
deterioration of recognition rate is not seen at high 
dimensions. This is conceivable that the loss of the 
information is suppressed by 65 levels of  8x8 character 
image. Since we will show experimental results using  8x8 
character images hereafter. 
 8  X  8  -  -  50  X  50  100 
i      nnI                0 0 0
I 
  • 
 0 
 et
0  cJ
100 
90 
80 
70 
60 
50 
40 
30 
20 
10 
 0
1 4( 
 0  5
B. Recognition by simple projection 
 To cope with the deterioration f recognition rate at low 
dimensions, two-step recognition process will be effective, 
that is, categories included in the p-place are nominated, 
then apply detailed recognition for the candidate categories. 
On the other hand, multiple projections by rotated unknown 
characters will also be effective. 
 First, we show the cumulative r cognition rate for 8x8 
character images in Figure 5. The parameter p in Figure 5 is 
the number of best candidates. 
                     - -  p=5 — A— • p=10 
100   A  I: 
     k ;112 -    90   
  80   f# 
17 70 
   60 
 /' -   
F 50   
 40   
 F.  30    */ 
   20 
   10      
0   
   0 5 10 15 20 25 30 35 I 
                          dimension 
               Fig.5 Cumulativerecognition rate
  The cumulative r cognition rate at 10 dimensions for 
 p=10 is 98.66%. In addition, all true categorieswere 
included in 23 best candidates with the first 35 dimensions. 
Further classification for the best candidates will be 
 effective by using high resolution image, as  50x50 images 
already showed high recognition rate in low dimensions in
Figure 4. 
C. Recognition by  multiple projections 
  Next, we show the recognition rate by multiple 
projections of R  =3 in Figure 6. You can see that the 
recognition performance at low dimensions is quite higher 
than that of  R  =1. 
  The recognition rate of R=3 at the first 10 dimensions is 
99.66%, and 99.99%(20 errors in 151,215 test samples) at 
the first 35 dimensions. 
  We cancover a deterioration f the recognition rate at 
low dimensions by the two-stage classification and by the
Jf
,11211-0-111-63-411 111-U
If-
I1
r
10 15 20 
dimension
25 30 35
Fig.4 Recognition rates for 50x50 binary  images and 8x8 65 level 
                     images
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International Workshop on Advanced Image Technology 2013
multiple projections. In recognition by the multiple 
projections, we got high recognition rate at low dimensions 
because contingency in simple projection can he 
suppressed. 
 100 - - - •  M 
    90 
 — 80 
    70 
 11° 60 
    50 
    40 
   ec     30 
  ", 20 
    10 
     0
   0 5  10 15 20 25 30  35 
                              dimension 
         Fig.6 Recognition ratebymultiple  projections(k=3) 
 We show a histogram  of angle  difference between true 
angle  and estimated one in Figure 7. This graph is depicted 
using truly recognized samples and estimated angles  are 
restricted within  ±-7 degrees, and  misclassified sample  are 
excluded.  Furthermore, 26 samples  of category  "  —" are 
excluded because their estimated angles were upside down. 
All samples correctly recognized except  "—"  are  within ±3 
degrees. The most are high precision within ±1  degrees.
.771  100000   90000 80000   70000 
  60000 
  50000 
E  40000 
z  30000 
20000 
10000
        -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 
                     Angle  differneeklegi 
             Fig.7 Precision  festimated angle errors 
 From these results, you can see that interpolated angles 
of 360 points are nearly correct. In recognition by simple 
projection and recognition by multiple projections( R =  3), 
examples of misclassification using the first 35 
dimensions  are  shown in Table  I. Misclassified categories
are very similar to the input categories. But, their estimated 
angles are within ±1 degrees though they are  misclassified. 
In addition, in  misclassified samples by the multiple 
projections(R=3), their true categories  were included in the 
best wo candidates.
TABLE 1.  EXAMPLES  OF  M  ISC  LA  SS  !VIC  ATION
IIlpu1　Rc3ult R-1 R=3
晋　⇒　普
24
14
震　⇒　霊
11
o
鳳　⇒　風 11 o
演　⇒　濃 9 3
漠　⇒　漢 9
島　⇒　鳥
8
o
廷　⇒　延 7 D
奥　⇒　臭 C n
筒　⇒　簡 G 4
論　⇒　論
6
0
others
2J2 2
D.  Coinputalion tune 
 The specification ofthe system used  for the simuration is 
as  follows; 
 CPU : Intel  Xeon(6core)12t1u-ead),Memory  :  48(113. 
 In the recognition process, all categories are divided into 
15 threads  1br parallel computation. 
 (a) The case of 50x5^ binary images  using 35 
     dimensions 
    The computation time per a character is 2.26[s] 
    Memory for the  dictionary  :  2.43[013]
 (b) The case of 8x8 65 levels image using 35 dimensions 
    The computation time per a character is 0.20[s] 
    Memory for the  dictionary  :  353_5[MB] 
 (c) The case of 8x8 65 levels image using the first  1() 
    dimensions by multiple  projections( I? =  3) 
    The computation time per a character is 0.15[s]
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International Workshop on Advanced Image Technology 2013
                IV.  SUMMARY 
 We proposed a rotated character recognition method 
using eigen-subspace foralpha-numeric characters before, 
In this  paper, we applied it for the  first class of Japanese 
Industrial Standard  (JIS) Kanji set which includes 2,965 
categories. As the result, we have obtained very high 
recognition rate. At  first, we showed that the recognition 
rates at 35 dimensions for different sizes of image are 
almost same. But, it decreases a little for small image size at 
low dimensions. Next, we experimented by two recognition 
methods that were the simple projections using only input 
image and the multiple projections using rotated images 
created by rotation of the input image. As the results, we 
have got very higher ecognition rate than single projection. 
The recognition rate is 99.74% for simple projection using 
35 dimensions, and 99.99% for multiple 
projections( R  =3 )using 35 dimensions. Especially, the 
multiple projections howed high performance at low 
dimensions. In the  future, we will consider the influence of 
the noise on a real image that exerts to the recognition rate. 
Furthermore, we try to absorb variations of real image by 
effective feature xtraction. In addition, we want to develop 
a real time system.
[7]  Shukun NING, Hiroyuki HASE, Shogo  TOICAI  , "Kanji 
   Character Image Generation for Character Recognition", 2012 
   Joint Conference of  Hokuriku Chapters of Electrical Societies, 
   F-56, 2012. 
[8]  Masafumi Yamamoto, Shiori Ssato, Hiroyuki Hase, Shogo 
   Tokai,  "Real-time Rotated Character Recognition using 
   Eigen-subspace", IEICE, Pattern Recognition and Media 
   Understanding,  PRMU2010-246,55-60,2011-03.
                REFERENCES 
[1] Q.Xie,  A.Kobayashi, "A Construction ofPattern Recognition 
   System Invariant of Translation, Scale-Change and Rotation 
   Transformation of  Patterns(in Japanese),  " Trans.of The 
   Society of Instrument and Control Engineers, Vol.27,No.10, 
 pp.1167-1174 (1991). 
 [2] S.Sato,  S.Miyake and H.Aso, "Evaluation of Two 
   Neocognitron-type Models for Recognition of Rotated 
   Patterns," ICONIP 2000, WBP-04,pp295-299 (2000).
[3] H.Murase, S.  K. Nayar, "3D  Object Recognition from 
   Appearance-Parametric Eigenspace Method-(in Japanese)," 
   Trans. of IEICE,  Vo1.J77-D-II,No.11, pp.2179-2187 (1994). 
[4] Toshiyuki Sinokawa, Hiroyuki Hase, Hiroshi Kakutani, 
   Masaaki Yoneda, "Recognition ofRotated Characters bythe 
   Parametric Eigen-space Method",  IIEEJ,  vol.33,no.6,pp.1123- 
   1131(2004.11)(in Japanese). 
[5] Ryo Narita, Wataru Ohyama, Tetsushi,  Wakabayashi, 
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   recognition",  MIRU2011, OSI-4: 26-32,  (2011)(in Japanese). 
[6]  http://projects.itri.aist.go.jp/eticdb/etIn/et12/ed2.htm
 -  1017  -


Rotated　Japanese Character Recognition

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