Print Character Recognition System Base on Localized Arc Pattern Method.
Proceedings of the 2nd International Conference on Sustainable Technology Development
Special Pattern Development for Feature Extraction
In Balinese Print Character Recognition System
Base on Localized Arc Pattern Method
AA. K. Oka Sudana a); Ni Kadek Ayu Wirdianib); Gusti Agung Ayu Putri c);
a
Lecture at Program Study of Information Technology , Udayana University, Bali
Email: agungokas@unud.ac.id
b
Lecture at Program Study of Informatics , STIKI Indonesia, Denpasar, Bali
Email: ayu_wirdi@yahoo.com
c
Lecture at Program Study of Information Technology , Udayana University, Bali
Email: dongdek@yahoo.com
ABSTRACT: One of pattern recognition that people usually know is character recognition.
Object of character recognition in this research is Balinese print character recognition system.
Balinese character is unique, the form of one and the other is almost same and some character
is differentiated by one line.
Feature extraction of character is conducted by special pattern that is formed from Localized
Arc Pattern Methods. Model selection based on apparition each model frequency is got from
Balinese character database image. The patterns is formed by the characteristic point in a
square 5 x 5 produces 125 pieces of possible initial patterns that can be grouped into an 103
patterns early models. Reduction of processing time is done by selection of 125 patterns that
are frequently come up in Aksara Bali. The selection patterns are performed by using
computer program to calculate the frequency of each pattern appeared on 600 pieces sample
of binary image the Balinese character. Patterns are obtained from the model selection process
as many as 23 pieces pattern.
The features of image tester are compared with reference feature. These comparisons yield
dissimilarity value. Then this value is sorted and the smallest dissimilarity value is used to
define whether the character test is recognized or not, through a critical value comparison.
The experiment achieved a success rate of 96.4%.
Key words: pattern recognition, Balinese character recognition, Localized Arc Pattern
Methods, special model pattern of Aksara Bali, feature extraction.
1. INTRODUCTION
Technological developments in the field of informatics and computer are very fast. Computer
system was developed to perform as a pattern recognition process of human ability. Pattern
recognition systems are widely used today, for example, fingerprint and hand palm of images
recognations, voice recognition, until the handwriting recognation. One of the common
pattern recognition is the handwriting recognition. Writing has unique properties that result in
an exciting new problem to be investigated.
Wealth of diverse cultures in Indonesia has its own advantages in terms of literary writings is
known to a wide range of areas. Writing in each region has a variety of typefaces and has its
Proceedings of the 2nd International Conference on Sustainable Technology Development
own uniqueness. Handwriting recognition is used as the object in this research area is
Balinese simbol as know as Aksara Bali. It has a unique writing of a similar shape to one
another and some writings are distinguished only by a single line sketch (Agung BW et al,
2009). Aksara Bali also have different properties with the Latin, Japan, Korea and China
writings characteristic. It becomes a problem in recognizing the Balinese writing. Therefore,
here is built a system for the Balinese writing recognation, which will help people to be easier
reading balinese writing (Aksara Bali). Development of this system is expected to provide an
alternative method for the recognition of a computerized image of Balinese writing simbol,
that it can attract the younger generation to learn it which is one of Bali's cultural heritages.
Feature extraction applies a specific pattern based on the Localized Arc Pattern method,
which is compatible to the Aksara Bali. It is chosen because this method takes the
characteristics of Balinese writing which is expected to give better recognition results. This
method has proven quite successful in terms of image signature verification and handwriting
recognition of Latin, Japanese, Korean and Chinese. Measurement of accuracy levels of the
method for Balinese Character Recognition by calculating the percentage of success, the
average error and the complexity of the system.
2. RESEARCH METHOD
2.1.Aksara Bali
Orthography of Aksara Bali in the form of Latin letters is adjusted to the Indonesian language
orthography, which the spelling is as simple as possible and its phonetic, that is correct or
close to the actual utterance. The letters that is used to write the Aksara Bali in Latin letters
form is divided into two, namely: Aksara Suara (vocals alphabet) and Aksara Wianjana
(consonants alpahabet) as shown in Table 1 and Table 2.
Table 1. List of Aksara Suara (Vocals Alphabet)
Nomor
Aksara Bali
Bali Latin
A
Ê
I
U
E
O
Proceedings of the 2nd International Conference on Sustainable Technology Development
Table 2. List of Aksara Wianjana (Consonants Alphabet)
Nomer Aksara Bali
Bali Latin
Nomer Aksara Bali Bali Latin
h/a
l
n
m
c
g
r
b
k
ng
d
p
t
j
s
y
w
ny
2.2. Data
Source of data as Aksara Bali samples that is used to build models of pattern formation and
testing of the character recognition is an image of Aksara Bali from the study of I Komang
Gede Suamba Dharmayasa (Dharmayasa, 2009). Balinese simbol samples are obtained from
the scan results of Balinese language textbooks are extracted using the characters
segmentation per block and also from the internet.
2.3. Step of Character Recognition of Aksara Bali
Generally, in image processing, there are usually a precursor prosesses to obtain the
information that is contained on it. After that, the subsequent processes will be done with
respect to what information is desired to be processed. Similarly, it is implemented in the
Aksara Bali recognation. Steps that performs Balinese Character Recognition system,
particularly the Localized Arc Pattern Method, are as follows:
Proceedings of the 2nd International Conference on Sustainable Technology Development
i. Data acquisition is a data conversion process. Here, scanner is used to convert analog data
Aksara Bali to the digital image. It is stored in bitmap file format of the raw data and will
be processed on the next step.
ii. Pre-processing, if the resulting bitmap file in the data collection phase has not been
shaped in two colors (black and white) then, that image must be converted into image data
in two colors. Next, elimination was done to data that is not required, to ensure that the
data which will be processed on the next step is a valid data.
iii. Feature extraction. Characteristics extractions apply a special model for the pattern of
Aksara Bali Localized Arc Pattern Method as shown in Figure 2. Aksara Bali that have
shaped the binary image will be processed to obtain the frequency of occurrence of each
pattern. Patterns that have the same model number but with different serial number,
frequency occurrence summed to obtain the frequency of occurrence of the pattern model.
iv. Enrollment. Steps of Aksara Bali reference registration are done by extracting the
characteristics of some of the Balinese reference, and the results obtained are stored in a
database file reference.
v. Comparison. The comparison step is the core of the whole recognition process. Here, the
characteristic image of Aksara Bali input will be compared to the reference characteristics
that exist in the database. At this stage, the calculations of the frequencies obtained in the
process of feature extraction will be done. Based on it, the dissimilarity (dissimilarity
measure) of each reference to the input image is obtained. Dissimilarity values are applied
as the basic of the recognition decision. Reference database record is read one character
reference data.
vi. Reference database design. Database design is the process of establishing a reference
database file to be used as a reference during the recognition process. In the execution of
this recognition system used 6 pieces of Aksara Bali samples for each character, with the
details: 3 for the reference and the remaining 3 as a comparison to determine the threshold
value. Reference database design phase consists of two main points of reference, namely
Aksara Bali registration and determination of threshold values to be stored in one record
with the ID numbers of keywords. Afterwards, it was continued by comparing the Aksara
Bali that will be used to determine the threshold value. Based on a comparison of three
Aksara Bali then are obtained value of each inequality. The median value of inequalities is
stored in the reference database complements the previous sample frequency, and used as
the threshold value (threshold) or the critical value (Cc) is multiplied by a constant Cd.
vii.
Decision making, it is the final step. This phase intend to give the decision of the
benchmarking process that has been done. Dissimilarity values obtained in the previously
is sequenced. The identity reference with the smallest dissimilarity value and meet the
threshold value (threshold) are decided as a alphabet of Balinese simbol corresponding to
the entered image Aksara Bali. If the smallest dissimilarity values obtained are above the
threshold value, it is concluded that Balinese character input is not recognized. Threshold
values obtained with the previous tests. If d(Pj, Qi) is defined as the value of the
dissimilarity between the reference Balinese owned by a Balinese character Pj tested by
Balinese Qi, Ccj is the critical value has been obtained previously from a Balinese
character of Pj and Cd is a constant multiplier, then apply the relationship:
if d(Pj, Qi) ≤ Ccj x Cd then ‘RECOGNIZED’
else ‘NOT YET IN LIST’.
Proceedings of the 2nd International Conference on Sustainable Technology Development
2.4. Recognition System Modeling
System
Developer
Model pattern
development
Image Input:
Balinese
character
Image Input:
Balinese
character
Database of
Model pattern
Character
Enrolment
Comparison with All
Record in Reference
Database
Output: smallest
dissimilarity
value and ID
Aksara
Reference
Database
Searching the smallest
dissimilarity value
Threshold and
Critical Value
Decision
Making
Recognition
Result Report
Figure1. Balinese Character Recognition System Modeling
3. RESULT AND DISCUSION
New model of pattern formation is based on the constraints in the Localized Arc Pattern
Method for Japanese writing and Latin signature in order to reduce the number of pattern
models used. Therefore, the processing time of the system can be shortened. Its main
limitation is the localization problem in a defined pattern of the model in a small square
measuring 5 x 5; however, the election is based on a sample Aksara Bali.
3.1 Models Pattern Development
The patterns is formed by the characteristic point in a square 5 x 5 produces 125 pieces of
possible initial patterns that can be grouped into an 103 patterns early models. Reduction of
processing time is done by selection of 125 patterns (show in Figure 2) that are frequently
come up in Aksara Bali.
3.2 Models Pattern Selection
The selection patterns are performed by using computer program to calculate the frequency of
each pattern appeared on a number of binary image of the Balinese character. Sample data
that is used to establish the pattern of the model are 600 pieces of Aksara Bali image which is
taken from some books and the internet. Frequency of each selected pattern appeared as
shown in Table 3, and the final result by reorder of this frequency appeared and rename of
each selected model as shown at Table 4. Patterns are obtained from the model selection
process as many as 23 pieces pattern as shown in Figure 3.
Proceedings of the 2nd International Conference on Sustainable Technology Development
No.1 Model 1
No.2 Model 2
No.3 Model 3
No.4 Model 4
No.6 Model 6
No.5 Model 5
No.7 Model 7
z
z
No.9 Model 9
No.10 Model 10
z
No.8 Model 8
z
No.15 Model 15
z
z
No.22 Model 22
No.23 Model 23
No.29 Model 29
No.30 Model 30
No.19 Model 19
No.20 Model 20
No.21 Model 21
No.25 Model 25
z
z
No.32 Model 32
z
No.38 Model 38
No.39 Model 39
z
z
z
z
No.58 Model 58
z
z
z
No.59 Model 59
No.60 Model 59
z
z
z
No.80 Model 71
z
z
z
No.85 Model 74
No.86 Model 75
No.87 Model 76
No.81 Model 71
z
z
z
z
No.83 Model 73
No.88 Model 76
No.89 Model 77
z
z
No.41 Model 41
No.42 Model 42
No.99 Model 85
No.100 Model 86
z
z
z
No.55 Model 55
z
z
No.56 Model 56
z
No.62 Model 60
z
No.63 Model 61
z
z
No.106 Model 92
z
No.107 Model 92
z
z
No.120 Model 100 No.121 Model 101
z
z
No.102 Model 88
z
No.108 Model 92
No.109 Model 93
z
z
No.114 Model 96
z
No.113 Model 95
No.101 Model 87
z
No.115 Model 97
z
No.116 Model 98
z
z
No.103 Model 89
No.104 Model 90
z
No.111 Model 94
z
No.117 Model 98
z
z
z
No.125 Model 103
z
Table 3. The frequency of 23 selected pattern appeared from 600 binary image of Aksara Bali
Model
Freq
No
Model
Freq
1
2
3
4
5
6
7
8
9
10
11
12
58
1
63
46
49
4
83
2
6
5
3
8
58154
36365
17262
11319
8896
743
539
489
244
226
223
171
13
14
15
16
17
18
19
20
21
22
23
82
14
12
86
19
10
26
90
31
13
47
126
68
49
32
30
23
19
15
15
14
10
z
z
No.112 Model 94
Figure2. All pattern possibility from Localized Arch Pattern with matrix 5x5.
No
z
No.105 Model 91
No.118 Model 98
z
No.124 Model 102
z
No.110 Model 94
z
No.122 Model 101 No.123 Model 101
No.98 Model 84
z
z
z
z
z
z
z
z
No.84 Model 74
No.91 Model 78
No.90 Model 78
z
z
No.97 Model 83
z
No.77 Model 69
No.82 Model 72
No.76 Model 68
z
No.70 Model 64
z
z
No.96 Model 82
z
No.95 Model 81
No.75 Model 67
No.94 Model 81
z
No.61 Model 59
No.74 Model 66
z
z
No.79 Model 70
No.73 Model 66
No.93 Model 80
z
z
No.49 Model 49
No.54 Model 54
z
No.69 Model 64
No.92 Model 79
No.53 Model 53
No.78 Model 69
No.68 Model 64
z
z
z
No.67 Model 63
No.35 Model 35
No.48 Model 48
z
z
No.34 Model 34
No.47 Model 47
No.72 Model 66
z
No.46 Model 46
No.52 Model 52
z
No.66 Model 62
z
z
z
No.40 Model 40
z
No.51 Model 51
No.28 Model 28
No.45 Model 45
No.50 Model 50
z
z
z
z
No.33 Model 33
No.27 Model 27
z
No.57 Model 57
z
z
No.44 Model 44
No.26 Model 26
z
No.71 Model 65
z
z
z
z
z
No.43 Model 43
No.18 Model 18
No.37 Model 37
z
z
z
No.36 Model 36
No.14 Model 14
z
No.31 Model 31
No.13 Model 13
z
z
z
z
No.24 Model 24
No.12 Model 12
z
z
No.65 Model 61
z
z
No.11 Model 11
z
No.17 Model 17
z
z
z
No.16 Model 16
No.64 Model 61
z
No.119 Model 99
z
Proceedings of the 2nd International Conference on Sustainable Technology Development
Table 4. The reorder frequency and rename of 23 selected pattern appeared
No
Model
Freq
No
Model
Freq
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
8
10
12
13
14
19
36365
489
223
743
226
244
171
23
49
14
68
30
13
14
15
16
17
18
19
20
21
22
23
26
31
46
47
49
58
63
82
83
86
90
19
15
11319
10
8896
58154
17262
126
539
32
15
Figure 3. The 23 Selected Special Model Pattern of Balinese Character
Base on Localized Arc Pattern Method
In final implementation these model pattern in Balinese Print Character Recognition System,
performance of the system is measured by two types of errors, namely: the rejection error
(false rejection) and reception errors (false acceptance). The system developed has a
minimum percentage of error in all combinations of the constant multiplier threshold Cd 2.0:
3.0: 4.0: 5.0 and the constant of cutting q-value of Eigen value 3, with an average system error
is 3.6% to obtain a success rate of 96.4%.
4. CONCLUTION
Based on trial and analysis results that have been done can be concluded as follows:
4.1. Aksara Bali print recognition is emphasized in the process of feature extraction that is
performed with a special pattern based on Localized Arc Pattern Method. Model
Proceedings of the 2nd International Conference on Sustainable Technology Development
selection is done from the implementation of the pattern founding in Aksara Bali
image databases, based on the accumulated frequency of occurrence of each pattern
model. As can be seen from the percentage of errors and processing time, this method
proved quite effective and produces better performance for the Aksara Bali
recognition, as compared with the pattern of Indonesia Signature models.
4.2. The special pattern base on Localized Arc Pattern Method for Balinese image
character, are formed by the characteristic point in a square 5 x 5 produces 125 pieces
of possible initial patterns that can be grouped into an 103 patterns early models.
4.3. Reduction of processing time is done by selection of 125 patterns. The selection
patterns are performed by using computer program to calculate the frequency of each
pattern appeared on a number of binary image of the Balinese character. Sample data
that is used to establish the pattern of the model are 600 pieces of Aksara Bali image
which is taken from some books and the internet. Patterns are obtained from the
model selection process as many as 23 pieces pattern
4.4. Performance of the system is measured by two types of errors, namely: the rejection
error (false rejection) and reception errors (false acceptance). The system developed
has a minimum percentage of error in all combinations of the constant multiplier
threshold Cd 2.0: 3.0: 4.0: 5.0 and the constant of cutting q-value of Eigen value 3,
with an average system error is 3.6% to obtain a success rate of 96.4%.
REFERENCE
Agung BW, Rudy Hermanto I Gede, Retno Novi D ang. (2009). Pengenalan Huruf Bali
dengan Menggunakan metode Modified Direction Feature (MDF) dan Learning Vector
Quantization (LVQ). Konferensi Nasional Sistem dan Informatika 2009. Institut
Teknologi Telkom, Bandung. yudiagusta.files.wordpress.com/.../007-012-knsi09-002pengenalan-huruf-bali-menggunakan-metode-modified-direction-feature-_mdf
Oka Sudana, AA. K. (2006). Rancang Bangun Sistem Verifikasi Tandatangan dan Pengenalan
Tulisan Tangan dengan Metode Pola Busur Terlokalisasi. Proceeding of the Research
and Studies III. TPSDP – DIKTI 2006.
Oka Sudana, AA.K. (2007). Implementasi Pola Model Tandatangan Jepang dan Tandatangan
Indonesia untuk Verifikasi Tandatangan Latin. Jurnal Pakar, Vol 7, No 4, Yogyakarta.
Shin-ichi Kikuchi, Takehiro Furuta, Takako Akakura. (2008). Periodical Examinees
Identification in e-Test Systems Using the Localized Arc Pattern Method. Distance
Learning and the Internet Conference 2008. p.213-220. Waseda University, Japan.
Suamba Dharmayasa, I Komang Gede. (2009). Pengenalan Karakter Bali Cetak
Menggunakan Metode Moment dan Jaringan Syaraf Tiruan Learning Vector
Quantization; Teknik Elektro Udayana, Jimbaran, Bali.
Yoshimura, I., Shimizu, T. dan Yoshimura, M.. (1993). A Zip Code Recognition System
using the Localized Arc Pattern Method. Proceedings of 2nd International Conference
on Document Analysis & Recognition. IEEE Computer Society. p183-186.
Yoshimura, M. dan Yoshimura, I., (1988), “Writer Identification Based on the Arc Pattern
Transformation”,Proceedings of 9th International Conference on Pattern Recognition,
November 14-17, 1993, IEEE Computer Society, Washington, p.353-361.
Yoshimura, I. dan Yoshimura, M., (1994), “Arc Pattern Method for Writer Recognition as an
Aid for Person Identification”, Nagoya University p.71-82.
___.___. 2010. Aksara Bali.
http://wapedia.mobi/id/Aksara_Bali . Diakses tanggal 09
Oktober 2010.
Special Pattern Development for Feature Extraction
In Balinese Print Character Recognition System
Base on Localized Arc Pattern Method
AA. K. Oka Sudana a); Ni Kadek Ayu Wirdianib); Gusti Agung Ayu Putri c);
a
Lecture at Program Study of Information Technology , Udayana University, Bali
Email: agungokas@unud.ac.id
b
Lecture at Program Study of Informatics , STIKI Indonesia, Denpasar, Bali
Email: ayu_wirdi@yahoo.com
c
Lecture at Program Study of Information Technology , Udayana University, Bali
Email: dongdek@yahoo.com
ABSTRACT: One of pattern recognition that people usually know is character recognition.
Object of character recognition in this research is Balinese print character recognition system.
Balinese character is unique, the form of one and the other is almost same and some character
is differentiated by one line.
Feature extraction of character is conducted by special pattern that is formed from Localized
Arc Pattern Methods. Model selection based on apparition each model frequency is got from
Balinese character database image. The patterns is formed by the characteristic point in a
square 5 x 5 produces 125 pieces of possible initial patterns that can be grouped into an 103
patterns early models. Reduction of processing time is done by selection of 125 patterns that
are frequently come up in Aksara Bali. The selection patterns are performed by using
computer program to calculate the frequency of each pattern appeared on 600 pieces sample
of binary image the Balinese character. Patterns are obtained from the model selection process
as many as 23 pieces pattern.
The features of image tester are compared with reference feature. These comparisons yield
dissimilarity value. Then this value is sorted and the smallest dissimilarity value is used to
define whether the character test is recognized or not, through a critical value comparison.
The experiment achieved a success rate of 96.4%.
Key words: pattern recognition, Balinese character recognition, Localized Arc Pattern
Methods, special model pattern of Aksara Bali, feature extraction.
1. INTRODUCTION
Technological developments in the field of informatics and computer are very fast. Computer
system was developed to perform as a pattern recognition process of human ability. Pattern
recognition systems are widely used today, for example, fingerprint and hand palm of images
recognations, voice recognition, until the handwriting recognation. One of the common
pattern recognition is the handwriting recognition. Writing has unique properties that result in
an exciting new problem to be investigated.
Wealth of diverse cultures in Indonesia has its own advantages in terms of literary writings is
known to a wide range of areas. Writing in each region has a variety of typefaces and has its
Proceedings of the 2nd International Conference on Sustainable Technology Development
own uniqueness. Handwriting recognition is used as the object in this research area is
Balinese simbol as know as Aksara Bali. It has a unique writing of a similar shape to one
another and some writings are distinguished only by a single line sketch (Agung BW et al,
2009). Aksara Bali also have different properties with the Latin, Japan, Korea and China
writings characteristic. It becomes a problem in recognizing the Balinese writing. Therefore,
here is built a system for the Balinese writing recognation, which will help people to be easier
reading balinese writing (Aksara Bali). Development of this system is expected to provide an
alternative method for the recognition of a computerized image of Balinese writing simbol,
that it can attract the younger generation to learn it which is one of Bali's cultural heritages.
Feature extraction applies a specific pattern based on the Localized Arc Pattern method,
which is compatible to the Aksara Bali. It is chosen because this method takes the
characteristics of Balinese writing which is expected to give better recognition results. This
method has proven quite successful in terms of image signature verification and handwriting
recognition of Latin, Japanese, Korean and Chinese. Measurement of accuracy levels of the
method for Balinese Character Recognition by calculating the percentage of success, the
average error and the complexity of the system.
2. RESEARCH METHOD
2.1.Aksara Bali
Orthography of Aksara Bali in the form of Latin letters is adjusted to the Indonesian language
orthography, which the spelling is as simple as possible and its phonetic, that is correct or
close to the actual utterance. The letters that is used to write the Aksara Bali in Latin letters
form is divided into two, namely: Aksara Suara (vocals alphabet) and Aksara Wianjana
(consonants alpahabet) as shown in Table 1 and Table 2.
Table 1. List of Aksara Suara (Vocals Alphabet)
Nomor
Aksara Bali
Bali Latin
A
Ê
I
U
E
O
Proceedings of the 2nd International Conference on Sustainable Technology Development
Table 2. List of Aksara Wianjana (Consonants Alphabet)
Nomer Aksara Bali
Bali Latin
Nomer Aksara Bali Bali Latin
h/a
l
n
m
c
g
r
b
k
ng
d
p
t
j
s
y
w
ny
2.2. Data
Source of data as Aksara Bali samples that is used to build models of pattern formation and
testing of the character recognition is an image of Aksara Bali from the study of I Komang
Gede Suamba Dharmayasa (Dharmayasa, 2009). Balinese simbol samples are obtained from
the scan results of Balinese language textbooks are extracted using the characters
segmentation per block and also from the internet.
2.3. Step of Character Recognition of Aksara Bali
Generally, in image processing, there are usually a precursor prosesses to obtain the
information that is contained on it. After that, the subsequent processes will be done with
respect to what information is desired to be processed. Similarly, it is implemented in the
Aksara Bali recognation. Steps that performs Balinese Character Recognition system,
particularly the Localized Arc Pattern Method, are as follows:
Proceedings of the 2nd International Conference on Sustainable Technology Development
i. Data acquisition is a data conversion process. Here, scanner is used to convert analog data
Aksara Bali to the digital image. It is stored in bitmap file format of the raw data and will
be processed on the next step.
ii. Pre-processing, if the resulting bitmap file in the data collection phase has not been
shaped in two colors (black and white) then, that image must be converted into image data
in two colors. Next, elimination was done to data that is not required, to ensure that the
data which will be processed on the next step is a valid data.
iii. Feature extraction. Characteristics extractions apply a special model for the pattern of
Aksara Bali Localized Arc Pattern Method as shown in Figure 2. Aksara Bali that have
shaped the binary image will be processed to obtain the frequency of occurrence of each
pattern. Patterns that have the same model number but with different serial number,
frequency occurrence summed to obtain the frequency of occurrence of the pattern model.
iv. Enrollment. Steps of Aksara Bali reference registration are done by extracting the
characteristics of some of the Balinese reference, and the results obtained are stored in a
database file reference.
v. Comparison. The comparison step is the core of the whole recognition process. Here, the
characteristic image of Aksara Bali input will be compared to the reference characteristics
that exist in the database. At this stage, the calculations of the frequencies obtained in the
process of feature extraction will be done. Based on it, the dissimilarity (dissimilarity
measure) of each reference to the input image is obtained. Dissimilarity values are applied
as the basic of the recognition decision. Reference database record is read one character
reference data.
vi. Reference database design. Database design is the process of establishing a reference
database file to be used as a reference during the recognition process. In the execution of
this recognition system used 6 pieces of Aksara Bali samples for each character, with the
details: 3 for the reference and the remaining 3 as a comparison to determine the threshold
value. Reference database design phase consists of two main points of reference, namely
Aksara Bali registration and determination of threshold values to be stored in one record
with the ID numbers of keywords. Afterwards, it was continued by comparing the Aksara
Bali that will be used to determine the threshold value. Based on a comparison of three
Aksara Bali then are obtained value of each inequality. The median value of inequalities is
stored in the reference database complements the previous sample frequency, and used as
the threshold value (threshold) or the critical value (Cc) is multiplied by a constant Cd.
vii.
Decision making, it is the final step. This phase intend to give the decision of the
benchmarking process that has been done. Dissimilarity values obtained in the previously
is sequenced. The identity reference with the smallest dissimilarity value and meet the
threshold value (threshold) are decided as a alphabet of Balinese simbol corresponding to
the entered image Aksara Bali. If the smallest dissimilarity values obtained are above the
threshold value, it is concluded that Balinese character input is not recognized. Threshold
values obtained with the previous tests. If d(Pj, Qi) is defined as the value of the
dissimilarity between the reference Balinese owned by a Balinese character Pj tested by
Balinese Qi, Ccj is the critical value has been obtained previously from a Balinese
character of Pj and Cd is a constant multiplier, then apply the relationship:
if d(Pj, Qi) ≤ Ccj x Cd then ‘RECOGNIZED’
else ‘NOT YET IN LIST’.
Proceedings of the 2nd International Conference on Sustainable Technology Development
2.4. Recognition System Modeling
System
Developer
Model pattern
development
Image Input:
Balinese
character
Image Input:
Balinese
character
Database of
Model pattern
Character
Enrolment
Comparison with All
Record in Reference
Database
Output: smallest
dissimilarity
value and ID
Aksara
Reference
Database
Searching the smallest
dissimilarity value
Threshold and
Critical Value
Decision
Making
Recognition
Result Report
Figure1. Balinese Character Recognition System Modeling
3. RESULT AND DISCUSION
New model of pattern formation is based on the constraints in the Localized Arc Pattern
Method for Japanese writing and Latin signature in order to reduce the number of pattern
models used. Therefore, the processing time of the system can be shortened. Its main
limitation is the localization problem in a defined pattern of the model in a small square
measuring 5 x 5; however, the election is based on a sample Aksara Bali.
3.1 Models Pattern Development
The patterns is formed by the characteristic point in a square 5 x 5 produces 125 pieces of
possible initial patterns that can be grouped into an 103 patterns early models. Reduction of
processing time is done by selection of 125 patterns (show in Figure 2) that are frequently
come up in Aksara Bali.
3.2 Models Pattern Selection
The selection patterns are performed by using computer program to calculate the frequency of
each pattern appeared on a number of binary image of the Balinese character. Sample data
that is used to establish the pattern of the model are 600 pieces of Aksara Bali image which is
taken from some books and the internet. Frequency of each selected pattern appeared as
shown in Table 3, and the final result by reorder of this frequency appeared and rename of
each selected model as shown at Table 4. Patterns are obtained from the model selection
process as many as 23 pieces pattern as shown in Figure 3.
Proceedings of the 2nd International Conference on Sustainable Technology Development
No.1 Model 1
No.2 Model 2
No.3 Model 3
No.4 Model 4
No.6 Model 6
No.5 Model 5
No.7 Model 7
z
z
No.9 Model 9
No.10 Model 10
z
No.8 Model 8
z
No.15 Model 15
z
z
No.22 Model 22
No.23 Model 23
No.29 Model 29
No.30 Model 30
No.19 Model 19
No.20 Model 20
No.21 Model 21
No.25 Model 25
z
z
No.32 Model 32
z
No.38 Model 38
No.39 Model 39
z
z
z
z
No.58 Model 58
z
z
z
No.59 Model 59
No.60 Model 59
z
z
z
No.80 Model 71
z
z
z
No.85 Model 74
No.86 Model 75
No.87 Model 76
No.81 Model 71
z
z
z
z
No.83 Model 73
No.88 Model 76
No.89 Model 77
z
z
No.41 Model 41
No.42 Model 42
No.99 Model 85
No.100 Model 86
z
z
z
No.55 Model 55
z
z
No.56 Model 56
z
No.62 Model 60
z
No.63 Model 61
z
z
No.106 Model 92
z
No.107 Model 92
z
z
No.120 Model 100 No.121 Model 101
z
z
No.102 Model 88
z
No.108 Model 92
No.109 Model 93
z
z
No.114 Model 96
z
No.113 Model 95
No.101 Model 87
z
No.115 Model 97
z
No.116 Model 98
z
z
No.103 Model 89
No.104 Model 90
z
No.111 Model 94
z
No.117 Model 98
z
z
z
No.125 Model 103
z
Table 3. The frequency of 23 selected pattern appeared from 600 binary image of Aksara Bali
Model
Freq
No
Model
Freq
1
2
3
4
5
6
7
8
9
10
11
12
58
1
63
46
49
4
83
2
6
5
3
8
58154
36365
17262
11319
8896
743
539
489
244
226
223
171
13
14
15
16
17
18
19
20
21
22
23
82
14
12
86
19
10
26
90
31
13
47
126
68
49
32
30
23
19
15
15
14
10
z
z
No.112 Model 94
Figure2. All pattern possibility from Localized Arch Pattern with matrix 5x5.
No
z
No.105 Model 91
No.118 Model 98
z
No.124 Model 102
z
No.110 Model 94
z
No.122 Model 101 No.123 Model 101
No.98 Model 84
z
z
z
z
z
z
z
z
No.84 Model 74
No.91 Model 78
No.90 Model 78
z
z
No.97 Model 83
z
No.77 Model 69
No.82 Model 72
No.76 Model 68
z
No.70 Model 64
z
z
No.96 Model 82
z
No.95 Model 81
No.75 Model 67
No.94 Model 81
z
No.61 Model 59
No.74 Model 66
z
z
No.79 Model 70
No.73 Model 66
No.93 Model 80
z
z
No.49 Model 49
No.54 Model 54
z
No.69 Model 64
No.92 Model 79
No.53 Model 53
No.78 Model 69
No.68 Model 64
z
z
z
No.67 Model 63
No.35 Model 35
No.48 Model 48
z
z
No.34 Model 34
No.47 Model 47
No.72 Model 66
z
No.46 Model 46
No.52 Model 52
z
No.66 Model 62
z
z
z
No.40 Model 40
z
No.51 Model 51
No.28 Model 28
No.45 Model 45
No.50 Model 50
z
z
z
z
No.33 Model 33
No.27 Model 27
z
No.57 Model 57
z
z
No.44 Model 44
No.26 Model 26
z
No.71 Model 65
z
z
z
z
z
No.43 Model 43
No.18 Model 18
No.37 Model 37
z
z
z
No.36 Model 36
No.14 Model 14
z
No.31 Model 31
No.13 Model 13
z
z
z
z
No.24 Model 24
No.12 Model 12
z
z
No.65 Model 61
z
z
No.11 Model 11
z
No.17 Model 17
z
z
z
No.16 Model 16
No.64 Model 61
z
No.119 Model 99
z
Proceedings of the 2nd International Conference on Sustainable Technology Development
Table 4. The reorder frequency and rename of 23 selected pattern appeared
No
Model
Freq
No
Model
Freq
1
2
3
4
5
6
7
8
9
10
11
12
1
2
3
4
5
6
8
10
12
13
14
19
36365
489
223
743
226
244
171
23
49
14
68
30
13
14
15
16
17
18
19
20
21
22
23
26
31
46
47
49
58
63
82
83
86
90
19
15
11319
10
8896
58154
17262
126
539
32
15
Figure 3. The 23 Selected Special Model Pattern of Balinese Character
Base on Localized Arc Pattern Method
In final implementation these model pattern in Balinese Print Character Recognition System,
performance of the system is measured by two types of errors, namely: the rejection error
(false rejection) and reception errors (false acceptance). The system developed has a
minimum percentage of error in all combinations of the constant multiplier threshold Cd 2.0:
3.0: 4.0: 5.0 and the constant of cutting q-value of Eigen value 3, with an average system error
is 3.6% to obtain a success rate of 96.4%.
4. CONCLUTION
Based on trial and analysis results that have been done can be concluded as follows:
4.1. Aksara Bali print recognition is emphasized in the process of feature extraction that is
performed with a special pattern based on Localized Arc Pattern Method. Model
Proceedings of the 2nd International Conference on Sustainable Technology Development
selection is done from the implementation of the pattern founding in Aksara Bali
image databases, based on the accumulated frequency of occurrence of each pattern
model. As can be seen from the percentage of errors and processing time, this method
proved quite effective and produces better performance for the Aksara Bali
recognition, as compared with the pattern of Indonesia Signature models.
4.2. The special pattern base on Localized Arc Pattern Method for Balinese image
character, are formed by the characteristic point in a square 5 x 5 produces 125 pieces
of possible initial patterns that can be grouped into an 103 patterns early models.
4.3. Reduction of processing time is done by selection of 125 patterns. The selection
patterns are performed by using computer program to calculate the frequency of each
pattern appeared on a number of binary image of the Balinese character. Sample data
that is used to establish the pattern of the model are 600 pieces of Aksara Bali image
which is taken from some books and the internet. Patterns are obtained from the
model selection process as many as 23 pieces pattern
4.4. Performance of the system is measured by two types of errors, namely: the rejection
error (false rejection) and reception errors (false acceptance). The system developed
has a minimum percentage of error in all combinations of the constant multiplier
threshold Cd 2.0: 3.0: 4.0: 5.0 and the constant of cutting q-value of Eigen value 3,
with an average system error is 3.6% to obtain a success rate of 96.4%.
REFERENCE
Agung BW, Rudy Hermanto I Gede, Retno Novi D ang. (2009). Pengenalan Huruf Bali
dengan Menggunakan metode Modified Direction Feature (MDF) dan Learning Vector
Quantization (LVQ). Konferensi Nasional Sistem dan Informatika 2009. Institut
Teknologi Telkom, Bandung. yudiagusta.files.wordpress.com/.../007-012-knsi09-002pengenalan-huruf-bali-menggunakan-metode-modified-direction-feature-_mdf
Oka Sudana, AA. K. (2006). Rancang Bangun Sistem Verifikasi Tandatangan dan Pengenalan
Tulisan Tangan dengan Metode Pola Busur Terlokalisasi. Proceeding of the Research
and Studies III. TPSDP – DIKTI 2006.
Oka Sudana, AA.K. (2007). Implementasi Pola Model Tandatangan Jepang dan Tandatangan
Indonesia untuk Verifikasi Tandatangan Latin. Jurnal Pakar, Vol 7, No 4, Yogyakarta.
Shin-ichi Kikuchi, Takehiro Furuta, Takako Akakura. (2008). Periodical Examinees
Identification in e-Test Systems Using the Localized Arc Pattern Method. Distance
Learning and the Internet Conference 2008. p.213-220. Waseda University, Japan.
Suamba Dharmayasa, I Komang Gede. (2009). Pengenalan Karakter Bali Cetak
Menggunakan Metode Moment dan Jaringan Syaraf Tiruan Learning Vector
Quantization; Teknik Elektro Udayana, Jimbaran, Bali.
Yoshimura, I., Shimizu, T. dan Yoshimura, M.. (1993). A Zip Code Recognition System
using the Localized Arc Pattern Method. Proceedings of 2nd International Conference
on Document Analysis & Recognition. IEEE Computer Society. p183-186.
Yoshimura, M. dan Yoshimura, I., (1988), “Writer Identification Based on the Arc Pattern
Transformation”,Proceedings of 9th International Conference on Pattern Recognition,
November 14-17, 1993, IEEE Computer Society, Washington, p.353-361.
Yoshimura, I. dan Yoshimura, M., (1994), “Arc Pattern Method for Writer Recognition as an
Aid for Person Identification”, Nagoya University p.71-82.
___.___. 2010. Aksara Bali.
http://wapedia.mobi/id/Aksara_Bali . Diakses tanggal 09
Oktober 2010.