Green Design
VI. CONCLUSION
Based on the experiment results on the lexicon-based sentiment analysis, hourglass of emotions and situational variables we can conclude that :
1. Indentifying emotion polarity through lexicon-based sentiment analysis for word detection and hourglass of emotions for detecting emotions polarity for each word has been done successfully. The results of the
identification process using the provided dataset and test data with hidden labels has shown that if the sentiment analysis process wasn’t done at the lowest level, namely emotional polarity (positive, negative, and neutral) it can cause a lot of bias, especially on positive emotions. The bias of positive emotions are at 19.7% which means that a lot of supossed positive emotion is actually a neutral emotion. This happens because the previous research assumes that all sentences that have positive emotions must also have positive polarity. Based on the research conducted it can be concluded that this is not always true.
2. Negative emotion detection based on the proposed framework and the result of the previous studies is surprisingly very similar, with only 2.2% or 97 Tweets differences. This show that negative sentiments has lesser bias than positive sentiments.
3. This research shows that the process of extracting a situation variable such as location and time can be done using social media post especially Tweet. If the data is provided by the post.
4. Based on the result of this research location situational variable analysis, it can be concluded that location didn’t have a significant effect on one’s emotions sentimets, where most of the positive and negative emotions sentiments came from Bandung.
5. Based on the result of this research time situational variable analysis, it can be concluded that time has a significat effect on one’s emotions sentiment, where there were certain times that affect the user's emotions for positive and neutral sentimets tweets most of it written around 11:00 AM and and for negative sentiments the tweet were were written around 12:00 PM.
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2, Article 25, 2017.
.
Implementation of Template Matching Correlation Method in the Conversion System of Ancient Greek
Letter Image into Modern Latin Letters
1st Jasman Pardede Department of Infomatics Engineering
Institut Teknologi Nasional Bandung Bandung, Indonesia [email protected]
2nd Irma Amelia
Department of Infomatics Engineering Institut Teknologi Nasional Bandung
Bandung, Indonesia [email protected]
3rd Rifqi Finaldy
Department of Informatics Engineering Institut Teknologi Nasional Bandung
Bandung, Indonesia [email protected]
Abstract— Greek cultural heritage became one of the important objects in the development of technology, culture, government systems and state administration. Unfortunately these relics were written using ancient Greek characters which are not well understood by the general public. The letter pattern recognition system using the template matching correlation algorithm is one way to overcome this problem. The first stage of the system is the process of taking digital images into the system by the user, followed by the preprocessing stage which includes several sub-processes, i.e : grayscaling, low pass filter, and otsu thresholding. The system then performs the stages of letter segmentation and normalization. The normalized image is then classified according to the label which has the largest correlation value using the template matching correlation method. The test results in the case study of test images with a distance between letters greater than 2 pixels obtained an accuracy value of 91.67%, in the case study of test images with a distance between letters 1-5 pixels obtained an accuracy value of 83.75%, in the case study of test images with sentences of more than 2 lines obtaining an accuracy value of 85.48%, and in the case study of test images by taking 20% of the training data obtained an accuracy value of 73.20%. Based on the four case studies obtained an average accuracy value of 83.525%. These results indicate that the template matching correlation is sufficient to build an ancient Greek alphabet recognition system.
Keywords— Image Processing, Letter Pattern Recognition, Greek Alphabet.
I. INTRODUCTION
A. Background
Ancient writings such as script are a form of cultural heritage that can still be felt until now (Winoto, Sukaesih, Rusmana, & Kurnaesih, 2016). One of the manuscripts/
writings that can still be felt is ancient Greek writing. In this modern era the use of ancient Greek characters in the writing system is lessen. This is due to the Romans who adopted ancient Greek characters and develop new writing patterns, namely Latin letters. Modern latin has a characteristic quite and effectively which is a representation of the need for identity as part of the era of modernity [1]. Modern Latin letters have become the dominant letters used throughout the world. The dominance of Latin letters in various countries has led to a decline in people who recognize letters that have been left by their ancestors. One of them is the use of Greek characters.
Optical Character Recognition by using Template Matching is a system prototype that useful to recognize the character or alphabet by comparing two images of the alphabet [2]. It is expected that with the system being built it can introduce Greek characters to the general public.
B. Problem Statement
The research problem proposed is :
How to design a system that can separate each pattern of ancient Greek letters contained in the image?
How can the template matching correlation method be implemented on the system?
How to design a system that can convert ancient Greek characters into Latin letters?
C. Objective
This study aims to implement the Template Matching Correlation method and measure the percentage of the system accuracy, precission, recall and F-Measure in recognizing ancient Greek characters contained in the image.
D. Scope
The system used is desktop based aplication
The system can only convert images containing ancient Greek characters which only contain text in the form of ancient Greek writing.
The converted text is capital Latin letters without the pronunciation of the letters
II. LITERATURE STUDY
A. Ancient Greek alphabet
Fig 1. Greek Alphabet
It is estimated that the Greek alphabet has been in use since the eighth century BC and although there were debates regarding the establishment of either ‘Demotic’ (closer to the everyday language used by most of the population) or
‘Katharevousa’ (closer to ancient Greek), the Greek writing system had not undergone significant changes since ancient times [14]. In both ancient and modern forms, the Greek alphabet has 24 letters from alpha to omega in which there are vowels. Alphabet letters from Greece are also still used in several countries, one of which is the Greek state itself.
Figure 1 represents a table of Greek characters that is still used today.
B. Image acquisition
Image acquisition is the initial stage to obtain digital images [3] At this stage digital images are entered into a system to obtain the value of information contained in the image.
C. Preprocessing
Image preprocessing aims to improve the quality of image [4]. At this stage the image quality is improved such as eliminating noise or separating objects and backgrounds.
Some sub-processes that can be performed at the preprocessing stage are : grayscaling, low pass filter, and otsu thresholding. an explanation of the sub-process will be explained in the next section.
1) Grayscaling
Grayscaling process reduces dimensions owned by the image, by mapping the image of three color channels to only one color channel, namely gray color [5]. So that the 3 components in an RGB image (Red, Green, Blue) after being converted to a grayscale image only have 1 color component, namely gray. The process of converting an RGB image into grayscale requires a calculation process. There are various ways to do the grayscaling process, including using the formula shown in Equation (1):
Grayscale = 0.21*R + 0.72*G + 0.07*B
2) Low Pass Filter
Low Pass Filter is a filter used to select pixels from an image. This filter has the property of passing the low frequency and eliminating the high frequency [6]. Low pass filter produces smoother images so that the small noise
contained in the image at the time of thresholding is not detected as an object. Low Pass Filter works by convoluting pixel values in the image using the kernel below :
3) Otsu Thresholding
Thresholding process aims to change the grayscale image which has a range of pixel values from 0 to 255 to a threshold image that has only 2 pixel values, namely 0 and 255 (Black & White) by determining a threshold (Threshold). One well-known and widely used method in determining threshold is to use the Otsu method [7].
Threshold value of this method is obtained using equation (2):
After getting the Threshold value, then the image can be grouped into two classes, namely: black and white (0 &
255) using equation (3):
(3) 4) Mathematical Morphology Segmentation
The stages of segmentation are needed to separate each character in the image. Segmentation with mathematical morphology utilizes dilation operations to find the location of lines and the location of characters from the text. The two main processes utilize vertical and horizontal strel matrices [8]. The following is a further explanation of the two matrices:
a) Horizontal strel matrix
Horizontal adjustment matrix thicken the object with respect to the x-axis. The result is that each object which is aligned and separated by a pixel value of 0 (black) will be seen as one part after it has been dilated using a horizontal matrix as shown in Figure 2.
Fig 2. Horizontal strel matrix b) Vertical strel matrix
The vertical stratified matrix thicken the object with respect to the y axis. The result is the same as horizontal matrix except that this matrix is needed to combine each object vertically. Figure 3 is a representation of the vertical strel matrix.
Fig 3. Vertical Structure Matrix
After dilation using the two matrices, segmentation is carried out by tracking the vertical and horizontal boundaries of the binary image [8]. So that the pixels can be grouped into regions that represent an object.
5) Normalization
Normalization aims to adjust the input image data with image data in the database [9]. This process is done by resizing the image size and adjusting the thickness of the letters using the Thinning process. So the final result of the normalization process is a normal image that has the same size as the image in the training and has a thickness of 1-2 pixels.
a) Thinning
Thinning is a process for making lines in an image into simpler shapes [10]. The purpose of this process is to reduce redundant pixels so as to produce the information needed by the system. One thinning algorithm is to use the Hilditch's Algorithm method. The algorithm works by using the 3x3 window as in Table 1.
TABLE 1. 3x3 window representation
P9 P2 P3
P8 P1 P4
P7 P6 P5
Hilditch's algorithm will convert p1 if it meets the following four conditions [11] :
• 2 < = B(p1) < = 6
• A(p1)=1
• p2.p4.p8=0 or A(p2) ≠ 1
• p2.p4.p6=0 or A(p4) ≠ 1 Where :
• B (p1) is the number of neighbors with a pixel value of 1 from p1.
• A (p1) is the number of transition pixels from 0 to 1 in the order p2, p3, p4, p5, p6, p7, p8, p9, p2.
D. Template Matching Correlation
Template matching Correlation is a statistical technique used to look for two or more quantitative variables / matrices [12]. For recognition to occur, the input characters are compared with each template to find the right match, or the template with the closest representation of the input character [2]. The level of match is represented as a correlation value which is the output of the Template Matching Correlation method. Correlation values are obtained using equation (4):
(4) Where :
r: is the correlation value between two matrices (range of values -1 and +1)
: is the pixel value to k in the matrix i
: is the pixel value to k in the matrix j
: is the average value of the matrix pixel i
: is the average value of the matrix pixel j
n : is the number of pixels in a matrix