Fuzzy Classifier for Church Cyrillic Handwritten Characters
Cveta Martinovska
1
, Igor Nedelkovski
2
, Mimoza Klekovska
2
and Dragan Kaevski
3
1
Computer Science Faculty, University Goce Delcev, Tosho Arsov 14, Stip, R. Macedonia
2
Faculty of Technical Sciences, University St Kliment Ohridski, Ivo Ribar Lola bb, Bitola, R. Macedonia
3
Faculty of Electrical Engineering and Information Technologies, University St Cyril and Methodius,
Rugjer Boshkovik bb Skopje, R. Macedonia
Keywords: Handwritten Character Recognition, Historical Manuscripts Recognition, Fuzzy Decision Techniques,
Feature Extraction, Recognition Accuracy and Precision.
Abstract: This paper presents a fuzzy methodology for classification of Old Slavic Cyrillic handwritten characters.
The main idea is that the most discriminative features are extracted from the outer character segments
defined by intersections. Prototype classes are formed using fuzzy aggregation techniques applied over the
fuzzy rules that constitute the descriptions of the characters. Recognition methods use features like number
and position of spots in outer segments, compactness, symmetry, beams and columns to assign a pattern to a
prototype class. The accuracy and precision of the fuzzy classifier are evaluated experimentally. This fuzzy
recognition system is applicable to a large collection of Old Church Slavic Cyrillic manuscripts.
1 INTRODUCTION
Recognition of handwritten characters has been a
subject of intensive research in the last 20 years
(Arica and Yarman-Vural, 2001); (Vinciarelli,
2002). Different approaches for developing
handwritten character recognition systems are
proposed, like Fuzzy Logic (Malaviya and Peters,
2000); (Ranawana et al., 2004), Neural Networks
(Zhang, 2000) and Genetic Algorithms (Kim and
Kim, 2000).
This paper describes a character recognition
system developed for digitalization of a large Old
Cyrillic manuscripts collection found in Macedonian
churches and monasteries. This process cannot be
performed using the existing computer software due
to the specific properties of Old Slavic characters.
A novel classification methodology based on the
fuzzy descriptions of characters is proposed.
Number and position of spots, beams and columns
that appear in the outer segments of the topological
character map are considered as significant features.
This character recognition system is applicable to a
large historical collection of manuscripts that
originate from various periods and locations. The
manuscripts used for church liturgical purposes are
unaffected by style changes. They are written in
Constitutional Script. This Script looks like printed
text, where character contour lines can be easily
extracted.
2 CHARACTER ANALYSIS AND
FEATURE EXTRACTION
Manuscripts are converted to black and white
bitmaps. The first step of processing is extracting the
characters using contour following function (Fig. 1).
Visual prototype of a normalized character is
analyzed to determine character features and their
membership functions. Several features are
examined, such as compactness, x-y symmetry,
presence of beams and columns in three horizontal
and vertical segments and number of spots in outer
segments.
According to visual features, the characters of
the Church Slavic alphabet can be grouped in
several subsets. There is a subset whose members
are Г, В and Б that have emphasized vertical lines on
the left-side or left column. Another subset contains
characters such as П and Ш that have a right-side
and left-side column. The third subset consists of
characters like П, Г and Б that have noticeable
horizontal line in the upper segment (upper beam).
The fourth subset consisting of characters as Ш and
310
Martinovska C., Nedelkovski I., Klekovska M. and Kaevski D..
Fuzzy Classifier for Church Cyrillic Handwritten Characters.
DOI: 10.5220/0003968403100313
In Proceedings of the 14th International Conference on Enterprise Information Systems (ICEIS-2012), pages 310-313
ISBN: 978-989-8565-10-5
Copyright
c
2012 SCITEPRESS (Science and Technology Publications, Lda.)
q has horizontal line in the bottom segment (bottom
beam).
Figure 1: Extracting a character contour.
These features are illustrated in Fig. 2. Particular
character can be а member of several of these
subsets.
Figure 2: Vertical lines and horizontal lines of characters.
The character can be intersected in such a way
that 6 segments are formed. Four outer segments
provide useful information for the proposed
character recognition system (Fig. 3).
Figure 3: Intersections of the characters that form the
upper, down, left and right segments.
Visual prototype of a character is formed
applying fuzzy intersection and fuzzy union
operators over a set of character samples (Fig. 4).
a) Fuzzy Intersection b) Fuzzy Union
Figure 4: a) Fuzzy intersection and b) fuzzy union.
3 FUZZY CLASSIFIER
Fuzzy classifier for Church Slavic characters is
based on character prototypes created in the form of
fuzzy linguistic rules. Fuzziness emerges from the
fact that texts are written by individuals with
different ways of writing and manuscripts originate
from different historical periods characterized with
certain styles of writing.
3.1 Operators for Fuzzy Aggregation
The precision of the character recognition system to
a certain extent depends on the proper selection of
features. This is done by calculating the overall
measure for the features applying the fuzzy
aggregation techniques. The general fuzzy
membership function
that combines the fuzzy
information (
,
,…,
) for the character features
can be represented as:
=
(
,
,…,
)
(1)
where Agg is a fuzzy aggregation operator.
This approach uses operators defined by Yager
(Yager, 1990) for the union and for the calculation
of weighted median aggregation.
Let w
1
, w
2
, …,w
N
represent weights associated
with fuzzy sets A
1
, A
2
, …, A
N
. Yager defines the
union using the following formula:
U
(
a
,a
,…,a
)
=min1,(a
)
(2)
where α is a real non-zero number and the value that
can be obtained as a result of the union ranges
between 1 and min(a
,a
,…,a
).
The weighted median aggregation is defined by
the following formula (Malaviya and Peters, 1995):
Med
(
a
,…,a
,w
,…,w
)
=(w
a
)
(3)
where
∑
=1
and α is a real non-zero number
with values between max
(
,
,…,
)
and
min(
,
,…,
).
3.2 Fuzzy Descriptions of Characters
The Church Slavic character recognition system
operates in two working regimes: building the
prototypes and character recognition. The first
regime creates a matrix of combined characteristics.
Using this matrix, fuzzy rules are generated in the
form of linguistic descriptions of the characters.
The rules contain only the features that are
relevant for the character classification and
identification. For example, the character “В“ is
described by the following combination of features:
two vertical holes, x symmetry, left column, one
spot in the left segment, one spot in the upper
segment, two spots in the right segment, and one
FuzzyClassifierforChurchCyrillicHandwrittenCharacters
311
spot in the lower segment. In the fuzzy description
of this character less significant features are upper
beam and lower beam.
Let the number of significant features for the
particular character is S and the number of segments
is C. The importance of every feature in the
aggregation process is represented by a certain
weight. The input values in the system are the
features of the character segments for which the
fuzzy values are calculated.
Generally, the input matrix with the character
features has dimension C x G, where G is the total
number of features that can be of structural nature
(symmetry, compactness, number of spots) or to
denote position:
I=i
c=
[
1,C
]
,g=[1,G]}
(4)
From the elements of the above matrix, aK
matrix
with dimensions p x C can be formed for each
segment, where p is a number of significant features
for the segment and j=1,…,C;p≤G.
K
=k
,k
,…,k
(5)
Using the weighted median aggregation operator, the
feature vector for a particular character is calculated
μ
=Med
(
a
,…,a
,w
,…,w
)
(6)
Then, using the union operator, the most significant
features from the list of features obtained in the
previous step, are selected:
μ
=min1,(μ
)
(7)
Weight matrices implicitly represent fuzzy rules that
describe the character prototypes. The weights are
obtained by statistical calculations from the training
samples. The number of appearances of a particular
feature is measured for every character. Higher
frequency of a feature decreases its recognition
importance. Smaller weights are assigned to more
frequent and hence less important features.
Weight matrices are used to reduce the number
of features that are considered for each character.
Different features are considered at each step in the
recognition phase and character prototypes that
possess these features are activated. Finally, only the
most similar character prototype is winner.
4 CHARACTER RECOGNITION
Procedure for character recognition consists of
several steps: 1. Determining the membership
functions for the global features of the unknown
symbol. 2. Calculating the membership functions of
an unknown symbol for all the prototypes according
to formula:
=
∑
.
=1,..
(8)
3. Selecting the possible prototypes that are most
similar to the unknown character, following the
formula:
=
(9)
The result of the classification process is a list of
prototypes that have the most similar features with
the features of the unknown character.
5 EXPERIMENTAL RESULTS
Several experiments are performed to test the
performance of the proposed fuzzy classifier. Table
1 shows the recall and the precision measures for
each character. Recall (R) is computed as a fraction
of the number of retrieved correct characters divided
by the total number of relevant characters:
=/(+)
(10)
Precision (P) is computed as a fraction of the
number of retrieved correct characters, divided with
the number of retrieved characters:
=/(+)
(11)
In formulas (10) and (11), the TP (True Positive) is
the number of correctly predicted examples, FP
(False Positive) is the number of negative examples
wrongly predicted as positive, and FN (False
Negative) is the number of positive examples
wrongly predicted as negative. The sum of precision
and recall i.e. F1 metric is computed as
1=2/(+)
(12)
The proposed fuzzy classifier recognizes the
characters with an average recall of 0.69, average
precision of 0.72 and an overall average measure of
precision and recall F1 of 0.70.
6 CONCLUSIONS
In this paper a novel methodology for recognition of
Old Slavic Cyrillic handwritten characters based on
fuzzy prototypes is described. Fuzzy descriptions of
ICEIS2012-14thInternationalConferenceonEnterpriseInformationSystems
312
the characters are represented as fuzzy rules. Fuzzy
aggregation techniques are used to combine different
character features, such as number and position of
spots in outer segments, compactness, symmetry,
beams and columns.
Table 1: Precision and recall of the fuzzy classifier.
Number of characters recall precision
Aa
10 0.4 1
b
7 0.67 0.67
v
6 0.83 1
g
10 1 0.58
d
12 0.75 0.56
e
7 0.43 1
/
5 0.4 1
\
6 0.67 0.36
z
4 0.25 0.5
J
12 0.83 1
i
5 0.4 1
k
1 1 o.5
l
10 0.9 0.75
m
4 0.25 1
n
11 1 0.73
o
8 1 0.61
p
4 0.5 0.67
r
5 0.2 1
s
9 0.89 0.73
t
7 1 0.78
U
4 0.75 1
f
7 1 0.87
H
6 0.67 0.8
h
9 0.28 0.67
w
5 0 0
]
7 1 1
c
11 0.91 0.58
;
10 0.8 0.89
[
7 0.86 0.75
q
14 0.86 0.63
Q
9 0.67 0.67
2
9 1 0.9
`
5 0.2 1
1
1 1 1
5
2 1 0.67
3
5 0 0
u
3 1 0.43
Total 257 0.69 0.72
Higher weights are assigned to features that are
more discriminative. For example, three spots
right/left/up or down and two holes are the most
indicative for the recognition process.
The accuracy and precision of the proposed
fuzzy classifier are acceptable and motivational for
future work and improvement.
Presented experimental results of this visual
methodology are comparable to the recognition of
the human visual system. Characters that are
misclassified are also unrecognizable for the
humans. Besides the fuzzy classifier a decision tree
classifier is designed. The recognition results of the
two classifiers are comparable. Both classifiers use
the same set of discriminative features.
For future work a combination of these two
classifiers is planned to achieve more accurate and
precise recognition of the Old Slavic Cyrillic
characters.
REFERENCES
Arica, N., Yarman-Vural, F. T., 2001. An Overview of
Character Recognition Focused on Off-Line
Handwriting. IEEE Trans. Systems, Man, and
Cybernetics-Part C: Applications and Rev., vol. 31,
no. 2, pp. 216-233.
Kim G., Kim S., 2000. Feature Selection using Genetic
Algorithms for Handwritten Character Recognition,
In: L.R.B. Schomaker and L.G. Vuurpijl (Eds.), Proc.
of the 7
th
Int. Workshop on Frontiers in Handwriting
Recognition, pp 103-112.
Kittler, J., Hatef, M., Duin, R., Matas, J., 1998. On
Combining Classifiers. IEEE Trans. on Pattern
Analysis and Machine Intelligence, vol. 20, no. 3.
Malaviya A., Peters L., 1995. Extracting Meaningful
Handwriting Features with Fuzzy Aggregation
Method, Proc. of the 3
rd
Int. Conf. on Document
Analysis and Recognition, Montreal, pp. 841-844
Malaviya A., Peters L., 2000. Fuzzy Handwritten
Description Language: FOHDEL, Pattern
Recognition, 33, pp. 119-131.
Ranawana, R., Palade V., Bandara, GEMDC, 2004. An
efficient Fuzzy method for Handwritten Character
Recognition, In M.Gh. Negoita et al. (eds.), KES 2004,
LNAI 3214, Springer-Verlag, pp.698-707.
Vinciarelli, A., A Survey on Off-line Cursive Word
Recognition, 2002. Pattern Recognition 35, pp.1433-
1446.
Yager, R., 1990. On the Representation of Multi-Agent
Aggregation using Fuzzy Logic, Cybernetics and
Systems 21, pp.575-590.
Zhang, G. P., 2000. Neural Networks for Classification: A
Survey. IEEE Trans. on Systems, Man, and
Cybernetics, Part C: Applications and Reviews, vol.
30 no.4, pp.451-462.
FuzzyClassifierforChurchCyrillicHandwrittenCharacters
313
