![](bg4.png)
5 EXPERIMENTATION
The aforementioned two classification methods are
combined by using a synthesized similarity as
defined in (6). Let
x and y be the similarity value
between the input pattern and each category in the
AMRM and LFDM, respectively. The following
sum of squared similarity (like Euclid norm) is used.
S_Similarity =
(6)
22
yx +
Thus we have experimented the above three
kinds of methods for 3036 categories of Japanese
handwritten characters (total number of character
patterns: 3036 x 20 patterns per category = 60,720)
in ETL9B (Electro Technical Laboratory in Japan)
database. The data used for experimentation includes
not only Chinese characters but also Japanese
Hiraganas.
In the experimentation, 10 samples (or character
pattern) per category were used for learning, i.e.,
decision of reference patterns and the weight
coefficients. Therefore, they are what we call
learning patterns. Actually, we have decided that the
number of the reference patterns (or eigenvectors of
KL expansion) per category is eight, because the
number has been the most effective for recognition
of the learning patterns used in experimentation. The
rest 10 patterns are experimented as
unknown
pattern
.
The specification of the computer, OS, etc. that
we used in this experimentation is as follows.
OS:Microsoft Windows XP Professional.
CPU:Intel PentiumⅣ(2.4GHz).
Main memory:1024Mbytes.
Programming language: Borland C++5.02J.
The experimental results are shown in Table 1
through 3. In order to compare the effects of three
kinds of weight coefficient, i.e., no weight (
i
=1
for all
i ), eigenvalue, and weight coefficient decided
by the linear regression model, the results in the
three cases are also shown in the tables.
W
Table 1: Recognition rate by the AMRM.
Weight
Coefficient
Input Pattern
o
Weight Eigenvalue
Linear
Regression
Model (LRM)
Learning Pattern 94.01% 68.13% 94.23%
Unknown Pattern 60.72% 60.10% 71.91%
Execution time in LRM: 48 msec/pattern.
Required storage: 6.6 Mbytes.
Table 2: Recognition rate by the LFDM.
Weight
Coefficient
Input Pattern
o
Weight Eigenvalue
Linear
Regression
Model (LRM)
Learning Pattern 99.92% 91.83% 99.61%
Unknown Pattern 85.79% 81.30% 87.24%
Execution time in LRM: 66 msec/pattern.
Required storage: 18.5 Mbytes.
Table 3.: Recognition rate by the combined method.
Weight
Coefficient
Input Pattern
o Weigh
Eigenvalue
Linear
Regression
Model (LRM)
Learning Pattern 99.93% 95.62% 99.81%
Unknown Pattern 90.13% 89.45% 92.20%
Execution time in LRM: 68 msec/pattern.
Required storage: approximately 25 Mbytes.
6 CONCLUSION
We have presented two classification methods and a
combined one for handwritten characters recognition
using features of the vector field. We have also
presented a set of weight coefficients in the
similarity, using the linear regression model (LRM).
Moreover, we have revealed the experimental results.
From the results, we can see that it is very effective
to use the feature of the vector field and the decision
of weight coefficients based on LRM. Therefore,
we consider that the feature point’s vector field
method is promising and worthwhile refining in
order to find more effective and low computational
cost (in the sense of time and storage) method.
REFERENCES
Masato S. et al., 2001. A Discriminant Method of Similar
Characters with Quadratic Compound Function,
IEICE Transactions, Vol.J84-D2, No.8, pp.1557-
1565, Aug. 2001 (in Japanese).
Takashi N. et al., 2000. Accuracy Improvement by
Compound Discriminant Functions for Resembling
Character Recognition, IEICE Transactions, Vol.J83-
D2, No.2, pp.623-633, Feb. 2000 (in Japanese).
Kazuhiro S. et al., 2001. Accuracy Improvement by
Gradient Feature and Variance Absorbing Covariance
Matrix in Handwritten Chinese Character
Recognition, IEICE Transactions, Vol.J84-D2, No.11,
pp.2387-2397, Nov. 2001 (in Japanese).
ICINCO 2004 - ROBOTICS AND AUTOMATION
420