IMPLEMENTATION OF HAMMING NETWORK ALGORITHM TO DECIPHER THE CHARACTERS OF PIGPEN CODE IN SCOUTING

International Research Journal of Computer Science (IRJCS) ISSN: 2393-9842
Issue 01, Volume 5 (January 2018) www.irjcs.com
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IMPLEMENTATION OF HAMMING NETWORK
ALGORITHM TO DECIPHER THE CHARACTERS OF
PIGPEN CODE IN SCOUTING
Endra Yunianto*
Department of Informatics, Faculty of Computer Science,
Universitas MercuBuana, Indonesia
41513110134@student.mercubuana.ac.id
Sabar Rudiarto
sabar.rudiarto@mercubuana.ac.id
Devi Fitrianah
devi.fitrianah@mercubuana.ac.id
Manuscript History
Number: IRJCS/RS/Vol.05/Issue01/JACS10091
DOI: 10.26562/IRJCS.2018.JACS10091
Received: 08, December 2017
Final Correction: 18, December 2017
Final Accepted: 08, January 2018
Published: January 2018
Citation: Yunianto, Rudiarto & Fitrianah (2018). IMPLEMENTATION OF HAMMING NETWORK ALGORITHM TO
DECIPHER THE CHARACTERS OF PIGPEN CODE IN SCOUTING. IRJCS:: International Research Journal of Computer
Science, Volume V, 30-41. doi: 10.26562/IRJCS.2018.JACS10091
http://www.irjcs.com/volumes/Vol5/iss01/04.JACS10091.pdf
Editor: Dr.A.Arul L.S, Chief Editor, IRJCS, AM Publications, India
Copyright: ©2018 This is an open access article distributed under the terms of the Creative Commons Attribution
License, Which Permits unrestricted use, distribution, and reproduction in any medium, provided the original author
and source are credited
Abstract— This study will produce an application that will recognize characters in Pigpen code using Hamming
network method. First, a Pigpen code image will undergo preprocessing stages, which are grayscale and
thresholding stages. Then, this image will be processed under the Hamming network so that it can be recognized
and translated into alphabet letters. Hamming network method can recognize images well. This study result
shows that the success percentage reaches 85% in recognizing handwriting images. However, Hamming network
method is also very sensitive toward the pixel shift of the image. The more the pixel shift, the less the level of
success is.
Keywords— preprocessing; grayscale; threshold; hamming network;
I. INTRODUCTION
In Scouting, we are already familiar with the Pigpen code since in the scouting training it is commonly used to
communicate. Every character in the Pigpen code has its own corresponding meaning in the alphabet. The
problem that frequently occurs is that these characters can be recognized by individuals who have knowledge
about codes in Scouting. Individuals that do not know the way to decipher this code will be curious of what it says,
or even regard it as normal scribble that is meaningless. Thus, socialization is needed so that the Pigpen code can
be more recognized and derives more benefit for the general public. To socialize and introduce the Pigpen code to
the public, the writers created an application to read the characters of Scouting Pigpen code by making use of
Hamming Network algorithm to recognize the pattern in the Pigpen code.

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Hamming Network is a neural network model that is specially designed to solve the problem of pattern
recognition with inputs from neural network in a bipolar form. Therefore, every element from vectors of neural
network input only has two possibilities of input value. This study is expected to prove that Hamming Network
method can determine the pattern of Pigpen code inserted so that the meaning can be deciphered.
II. RELATED STUDIES
A. Pigpen Code
In Scouting, the Pigpen code is often utilized as one of the communication means. The Pigpen code consists of
alphabet characters from A to Z. See the list of Pigpen code characters bellow in Fig. 1.
Fig. 1 Pigpen Code
B. Hamming Network
Hamming network is a neural network mode that is specifically designed to address the pattern recognition with
inputs from neural network in a bipolar form. In the process, Hamming network use the hamming distance as a
similarity indicator between two vectors, and Maxnet serves as a subnet to determine the unit that has the biggest
net input. The architecture of Hamming Network can be seen in Fig. 2.
Fig. 2 Architecture of Hamming Network and Maxnet [5]
The following is the learning algorithm using the hamming network method:
n is the value of input node.
m is the value of output node.
( )is the sample vector j
Step 0:
Weight initialization based on sample vector:
=
( )
, = 1, … , , = 1, … , 2.1
Bias initialization:
= , = 1, … , 2.2
Step 1:
For every vector x, do steps 2-4.
Step 2:
Count the input for every unit y
y_ = b + ∑ x w , ( j = 1, … , m) 2.3
Step 3:
Initialization Maxnet input:
y (0) = y , (j = 1, … m) 2.4
Step 4:
Use Maxnet procedure to produce a vector with positive value. In the calculation, Maxnet will look for y with the
biggest value. Result with the biggest value is the one that has the most similarity with the input image and that is
regarded as the input image to be displayed as the output.
C. Maxnet
Maxnet is one of the artificial neural networks based on competition [6]. Generally, it is used for pattern
identification. Maxnet can be used by another model of artificial neural network such as Hamming Network to get
neuron with the biggest input. The following is the procedure of Maxnet operation:

_________________________________________________________________________________________________
Step 0.
Activation and weight initialization:
ϵ = 0 < < 2.5
( ) =

0
≥ 0;

2.6
Step 1.
While stopping condition is false, do steps 2-4.
Step 2.
Update the activation of every node: For j = ², …, m
( ) = [ ( ) − ϵ ∑ ( )] 2.7
Step 3.
Save activation for the next iteration
( ) = ( ), = 1, … , 2.8
Step 4.
Test stopping condition.
If there is more than one node whose activation is not equal to 0, continue iteration; if not, stop.
The writers summarize several existing studies, among them are:
Detection of fingerprint pattern slope curvature using Hamming Net is the basis of the classification. The study
results in the classification with 63% accuracy from 80 fingerprint pattern samples [8].
Character introduction uses the Hamming network. The result of the test shows that for this application, Hamming
Network is really good in recognizing data input consisted in the training data and quite robust toward noise. The
Times New Roman characters produce better identification level [9].
The implementation of Hamming artificial neural network is conducted in the case of number and letter pattern
identification. In that study, the success of certain JST in recognizing pattern highly depends on the training
package given to the network. JST algorithm is quite reliable in re-recognizing the patterns on which it has trained
even though that pattern has gotten quite numerous noises [3].
From the existing studies, the authors planned on creating a system to translate the characters of Pigpen code into
alphabet letters. This result of this study will recognize certain Pigpen code patterns taken from the image file as
the input and will compare it with the pattern of Pigpen code on which it has trained before, similar to what being
processed in the research of SAR image processing [15]. It is expected that this study can help determining the
meaning of the Pigpen code characters.
III.DATA AND METHODS
A. Data
The data used in this study is images of Pigpen code with 100x100 pixels, among which are shown in Fig. 3.
Fig. 3 The images of Pigpen code [4]
Then, these data will be saved into the database as binary images, among which are shown in Figures 4 and 5.
Fig. 4 The Pattern of Pigpen Code, Letter A

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Fig. 5 The Pattern of Pigpen Code, Letter B
B. The Methods of GUI Application Design for Pigpen Code Character Reading
GUI application in this study consists of the establishment of the database and the identification. See the
identification process in GUI application in Fig. 6.
Fig. 6 The flowchart of GUI Application for Pigpen Code Character Reading
C. Grayscale
Grayscale is grayish image or image that only has one part value RED = GREEN = BLUE. The colors consist of black,
gray, and white [7].A colorful image can be changed into grayscale by taking the average value of r, g, and b values
from that image [2] See the grayscale image processing in Fig. 7.
Fig. 7 The Flowchart of Grayscale Processing

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D. Threhold
After the image undergoes the grayscale processing, threshold processing will be applied on that image. Threshold
will produce binary image or black and white image.
Every pixel from the image will be compared with the threshold value between 0 and 255. Thus, pixel value above
the threshold value will be converted into 1, and the pixel value under it will be converted into 0. See the image
threshold processing in Fig. 8.
Fig. 8 The Flowchart of Threshold Processing
E. Database Establishment
Table 1 shows the table structure in the database, which consists of 4 fields. Field id is the identity of the pattern.
This field is used as the primary key. The field patterncode is used to save the preprocessing results of the Pigpen
code and to identify the pattern. The field alphabet letter is used to save alphabet letters of Pigpen code
characters. Field description is used to save additional information.
TABLE I - STRUCTURE OF THE DATABASE
No Field Names Data Types Description
1 id Int Primary Key Not Null
2 polasandi Text Not Null
3 hurufabjad Varchar Not null
4 keterangan Varchar Not null
In the establishment of database, the system begins with inputting image. This image will undergo preprocessing
stages, which are grayscale and threshold. Then, the data from that image will be saved in the database as the
sample pattern. See the process of database establishment in Fig. 9.
Fig. 9 The Flowchart of database establishment

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F. Pattern Identification
For the pattern identification, the input image that has undergone the preprocessing stage will be processed using
hamming network method, which compares the characteristics of every input pattern and sample pattern in the
database. The results will be shown by the output as the translation (alphabet letters) from the inputted code
characters. See the identification process in Fig. 10.
Fig. 10 The Flowchart of Pattern Identification
G. Pattern Identification
In order to get the highest value from the pattern identification process, Maxnet method is used. See theMaxnet
method process in Fig. 11.
Fig. 11 The Flowchart of Acquiring the Biggest Value using the Maxnet Method

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IV. RESULTS AND DISCUSSION
A. GUI Application
GUI Application in this study consists of 2 forms, including Pattern Database and Identification. See the form of
Pattern Database in Fig. 12. In the form of Pattern Database, the first process is image inputting. Then, image
cropping can be done or omitted. After that, apply grayscale and threshold preprocessing. Binary image will
appear. Enter alphabet letters from the image and its description in the available columns, then save. See the
identification form in Fig. 13.In the Identification form, the first step is the same as the process of the Pattern
Database form. After the binary image appears, click “PROCESS” button, then the system will run the Hamming
Network method to search for the pattern most similar to the input pattern in the database.
Fig. 12 The Form of Pattern Database
Fig. 13 Identification Form
B. Analysis of Testing Result
This is the stage in which analysis is done on the testing result of Pigpen code image identification. The images of
Pigpen code will be tested in terms of the threshold values, pixel shift, and handwriting identification.
 The Test against the Threshold Values
The first test is conducted against the threshold values. Every Pigpen code image will be tested in terms of
the 3 threshold values, which are 0, 100 and 255. See the result of the test based on the threshold values
in Fig. 14.
Fig. 14 Results of the Identification Based on the Threshold Values
Fig. 14 shows that the biggest percentage (100%) happens on the threshold value 100. Meanwhile, the
smallest percentage (0%) happens on the threshold value 0. It can be concluded that in the Pigpen code
identification. Threshold values affect the level of success in the identification. The smaller and the bigger
the threshold values, the more the image is not recognized. The threshold value should be in the middle
between 0 – 255 so that the image is recognized 100%.

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 The Test against the Pixel Shift
In the second test, the authors applied the pixel shift in the Pigpen code. The pixel shift includes the 4-
pixel, 6-pixel, 8-pixel, and 10-pixel shifts toward the right. See the result of the test against the pixel shift
in Fig. 15. Figure 15 shows that the highest percentage of the success in the identification is 60%. From
this test, it can be concluded that pixel shift in the image has a high influence on the identification process.
The Hamming network is very sensitive toward the pixel shift. The more the pixel shifts, the less success
percentage is achieved.
Fig. 15 The Result of the Test against Pixel Shift
 The Test against Handwriting Identification
As the third test, the authors conducted a test towards handwriting identification. See some of the
handwriting examples in Fig. 16.
Fig. 16 Pigpen Code Image with the Use of Handwriting
Meanwhile, the results of the handwriting identification can be seen in Table 2.
Table 2 shows the results of the handwriting image identification. From 26 tested images, there were 4
that failed to be recognized with the failure percentage of 85%. It can be concluded that the Hamming
Network method can recognize handwriting well.
TABLE III- THE RESULTS OF THE HANDWRITING IMAGE IDENTIFICATION
A Right N Right
B False O False
C Right P Right
D Right Q Right
E Right R Right
F Right S Right
G Right T False
H Right U Right
I Right V Right
J Right W Right
K Right X Right
L False Y Right
M Right Z Right
V. CONCLUSION AND FURTHER STUDIES
A. Conclusion
From this study, some conclusions can be drawn as follows:
 Threshold values have effects on the process of Pigpen code image identification. The smaller or bigger
the threshold values, the more the Pigpen code image is not recognized.
 The Hamming network method is able to recognize Pigpen code image well. The results of this study
show that the level of success reaches 85% in recognizing image using handwriting.
 The Hamming network method is very sensitive towards the pixel shift of the image. The result of this
study shows that the bigger the image shift, the less the level of success.
0%
20%
40%
60%
80%
4px 6px 8px 10px

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B. Further Studies
For the further studies, it is expected that the Hamming Network method is not only able to detect certain
characters, but also able to detect certain syllables or even sentences. The result of this study shows that it is not
impossible for syllables to be detected in terms of the patterns. Further studies can also implement the method in
mobile devices so that the image taking is easier by using the camera in the mobile devices. Finally, it is hoped that
this study can serve as the basis for the further studies.
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