View of Classification of Tajweed Al-Qur’an on Images Applied Varying Normalized Distance Formulas

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Classification of Tajweed Al-Qur’an on Images Applied Varying Normalized Distance Formulas

Al-Khowarizmi

Univ. Muhammadiyah Sumatera Utara Jalan Mukhtar Basri No. 3 Medan

+62616619056

Akrim

Univ. Muhammadiyah Sumatera Utara Jalan Mukhtar Basri No. 3 Medan

+62616619056

Muharman Lubis

Telkom University

Jalan Telekomunikasi No. 1 Bandung +6282116934452

Arif Ridho Lubis

Politeknik Negeri Medan Jalan Almamater No. 1 Medan

+6285373332208

ABSTRACT

Al-Qur’an is a Muslim holy book which is written originally in Arabic where the way of reading it should be aligned with the pronunciation and spelling of the messenger and companion in the time of revelation. It is very important to follow the rule of its reading to avoid misinterpretation of the verses, which the existence of artificial neural networks and image processing can be used to classify various type of Tajweed as the reading discipline of Al-Quran in order to support the readers in term of pronunciation and interpretation. In this classification, the recitation of Al-Qur’an in the form of a number of normalized distance formulas in order to obtain the right optimization for the classification namely normalized Manhattan distance which is consistent at a value <0.5 and one that is not applied in the case of Tajweed Al-Qur’an is normalized hamming distance because the value generated is 1.

CCS Concepts

• Computing methodologies^➝Computer graphics^➝Image manipulation^➝Image processing

Keywords

Classification; Images; Tajweed; Pattern; Normalized Distance.

1. INTRODUCTION

As many people have been known, Al-Qur’an is a Muslim holy book which is written in the Arabic character due to several important reason such as motivation to preserve its original form to prevent loss of its richness of meaning in term of nuance, eloquence, literature and fluency. Thus, the person that want to

read is required to fulfill the requirement of clarity in Arabic language [1] [2] [3]. In this context, the method of reading Al- Qur’an is called Tajweed, as a discipline that contain set of specific rule for correct pronunciation of the letters with detailed of changes or transformation of the sound refer to the approach of the messenger recited [4]. If the person have been mispronounce or wrong in reciting the verses of the Al-Qur’an then the meaning achieved is also become wrong and can be misinterpreted [5].

Thus, it is obligatory for Muslims to know the rule of Tajweed at optimal level that allow them to read accordingly and rightfully.

Due to this reason, they can convey the verses as well as the meaning accurately in avoiding false understanding, which often presume by various community in relation of the messages of the Al-Qur’an [6]. Originally, the word of Tajweed has specific meaning, which are to improve, make better or betterment while based on terminology, it can be defined as one of the most honored discipline and sciences that considering the articulation of every letter from its spelling point and giving the letter its rights and dues of characteristics [2] [7]. In order for the delivery of the Al-Quran verses become accurate, it is certain that one person must have good ability in Tajweed, which required a lot of work and focus numerous times. In the process of recognizing the mistakes, transforming the collection of the word into audio form and identifying specific type of tajweed in specific verses based on the image; then the information technology (IT) can be used to advance and smooth the process with the relevant and proper techniques [8] [9]. Artificial Neural Networks (ANN) and Image Processing are techniques in IT that can be used to manage and organize the information systematically, which is inspired and has a performance resembling a biological neural network [10]. ANN as one of the applications of modern mathematical computing to solve problems by learning data patterns in accordance with data mined in the respective sources [11].

Some studies [12] has been conducted by making automatic rules on recitation of the Al-Qur’an but the Tajweed tested only some of the rules namely Idgham whereas the other rules such as Izhar, Iqlab and Ikhfa’ have their own challenges. Meanwhile certain research [13] has been conducted by using intelligence touring systems (ITS) to create rules in the Al-Qur’an recitation.

Therefore, based on the result, it was not optimal in the distinction of ح and ه. Meanwhile, this reference [14] makes a model in correcting the results of the Al-Qur’an mapping where the result is Permission to make digital or hard copies of all or part of this work for

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DOI:

https://doi.org/10.1145/3396730.3396739

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a new representation dimension (micro value) is useful to represent a miniature feature vector as a reading or error similarity.

Meanwhile, the other study [15] has been performed voice recording on Al-Qur’an readers by using Automatic Speech Recognition, but only the length of digits in the application is recognized in the Al-Qur’an. However, in the classification system, many have examined [16] to detect systems of recitation of the Al-Qur’an by using the distance formula but there is no comparison with other distance formulas. On the other hand, there is also study [17] that compared distance formulas using Z-Fisher Transform and Bray Curtis Distance that focused on the classification of Tajweed Al-Qur’an to optimize the use of distance formulas with some normalized distance formulas. Other research has been carried out in the application [18] to modify the method while the others [19] also emphasized on the process of optimization the distance formula used in the k-NN method where the best results and results are by using the normalized distance formulas.

Some studies have pointed that multi-class categorization issues with the distance compression on high-dimensional data can help classification and data analysis while for documented data, the cosine and normalized Euclidean, correlation and proportional city block metrics give a strong neighborhood recovery. Actually, it gives particularly good results for nearest neighbor recovery and should be used when using document data analysis techniques as the important assets. Therefore, for data generated from the uniform distribution, neighborhood recovery improves with an increase in the value of p [20]. The purpose of intensity normalization is to make uniform the mean and variance of intensity values in images applied as a first step in increasing the performance of automated image processing techniques. It has a significant effect on registration, longitudinal segmentation, cross sectional segmentation, longitudinal quantity and other measurements. It is also important to compare the MRI image, which is generally required to diagnose the disease and assess progressive disease during treatment. MRI image data sets, taken at different times from the same patient, are generally compared to detect or measure tissue changes, while time series analysis provides information about the stage or development of the disease [21]. In addition, the enhancement of clustering properties can be done through addition of extended coefficient associated with wavelet coefficient in every scale to obtain maximum likelihood for every dyadic square region at every scale in a segmented image [22].

Recently, other study, searching the valid configuration of object through interpretation and score comparison in term of both detection and segmentation [23] while the watershed transform has been offered to obtain information of the primitive region and boundaries as pre-segmentation of the image within initial partition [24]. Distance or similarity measurements are necessary to solve many pattern recognition problems, such as classification, grouping, and retrieval problems [25]. A great effort has been made to find the correct measures among several set of options among distance formula for classification because they are essential to categorize patterns, groups and information retrieval problems. The choice of distance or similarity scales depends on the type of measurement or the representation of things. On the other hand, the effectiveness and the choice of the algorithm depend on different lighting conditions, the environment and, finally, personal preferences [26].

Previous work proposed the data normalization to cancel out the geometric variability by mapping input images and gaze labels to

a normalized space [27]. Many computer vision tasks require an image pair comparison, since the distance scale is used to describe how the image is similar to another. In recording and stereo pair matching, the images are aligned to obtain the greatest similarity between them. In optical tracking and automatic navigation, the current video frame searches for predefined destination templates or features and a best-match site is mapped to the actual destination location [28]. Meanwhile, it is reported that Gabor filter within the neural network’s learning framework can increases the classification accuracy of the convolutional neural network [29]. They are integrated into the learning framework in two approach, which are to initialize the filters in the first convolutional layer and implemented in conjunction with the Gershgorin circle theorem to constrain the weights during back propagation.

2. METHODOLOGY 2.1 Dataset

The data used in this study is the image of the Al-Qur’an in the .bmp format. Image files used for training and testing are obtained through the help of Verse-v1.4_standard and Digital Qur’an Version 3 software.

2.2 General Architecture

The general architecture of this research is depicted in Figure 1.

From the training data and in that step the pattern blocking is done using the normalized hamming distance, normalized Manhattan distance and normalized Euclidean distance. Therefore, Nakhamani and Tannebaum [28] explained in detail that small distortions such as translations, rotations and scales should not greatly change the distance (if any). In tracking, when the destination location want to be achieved, the distance should be less tolerable for translation but strong for added and reproductive noise present in real-world scenes. But even in tracking there can be ambiguous requirements for distance measurement. If only the binary decision is needed, whether the target is present or not, then the distance must be zero at the correct location and infinite, otherwise, but if the distance is used to adjust the position by some iterative measurement algorithm current, then you want to gradually increase the distance when you leave the desired site.

Dataset Edge Detection

Process

Calculate Distance

Output

Edge Detection Process

Calculate Distance Pieces of the

Holy Qur'an

Edge Detection Process

Calculate Distance Learning Phase with Normalized

Hamming Distance

Output

Output Learning Phase with Normalized

Manhattan Distance

Learning Phase with Normalized Euclidean Distance

Figure 1. The general architecture.

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Thus, the explanation from step figure 1 as follows:

1. Input the value to the data set which is the Al Quran image from the Software.

2. Extracting images into patterns.

3. Conduct training data using normalized hamming distance.

a. Perform Edge Pattern Detection

b. Calculate with normalized hamming distance

4. Conduct training data using normalized Manhattan distance.

b. Calculate with normalized Manhattan distance 5. Conduct training data using normalized Euclidean distance.

b. Calculate with normalized Euclidean distance

6. Finding the optimal value from the calculation of some normalized distance formulas.

So that the overall research step that was built in this study is illustrated in Figure 2 for normalized Hamming distance, normalized Manhattan distance and normalized Euclidean distance.

Where in calculating the normalized Hamming distance as in equation (1):

𝐷

_𝑛

=

^{∑ |𝐼}^𝐾^𝑖 ^𝑖^−𝑊^𝑖^|

∑ |𝐼^𝐾_𝑖 _𝑖+𝑊_𝑖|

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Where K is the number of input nodes is the input vector, and W is the input weight matrix on the layer. Whereas in calculating with normalized Manhattan distance like equation (2):

𝐷

_𝑛

= ∑

^|𝐼^𝑖^−𝑊^𝑖^|

𝑘

𝐾𝑖 (2)

And equation (3) to calculate the normalized Euclidean distance as follows:

𝐷

_𝑛

= √∑

^|𝐼^𝑖^−𝑊^𝑖^|²

𝑘

𝐾𝑖 (3)

3. RESULT AND DISCUSSION

In this session, each recitation pattern has a vector pattern.

Tajweed pattern vector after pre-processing will get a value of 0 or 1, where the value of 0 in the vector pattern represents the value that is not included in the Tajweed feature but is still in the Tajweed pattern area, otherwise the value 1 is a value that represents the Tajweed feature pattern. Figure 3 shows the vector of tajweed patterns. The image can be retrieved from a large database by sending the match request to a specific pencil drawing.

Also, the image content can be extracted, analyzed and identified compared to predefined templates.

In analyzing the performance of the three algorithms both normalized Hamming distance, normalized Manhattan distance and normalized Euclidean distance when matching between training patterns ِن ْ and اِم ِ ِن ْ . Normalized hamming distance, normalized Manhattan distance and normalized Euclidean distance in calculating the similarity between the two images only by calculating the distance, weight of the reference pattern after pre-processing and producing a value between 0 as the background of an image and 1 as an object of an image then the matching process the pattern will be calculated using the normalized Hamming distance algorithm, normalized Manhattan distance and normalized Euclidean distance. Analysis of the three algorithms in recognizing Tajweed patterns first works as a distance counter between two image vectors.

Figure 2. Illustration of research.

Figure 3. Example of pattern vector tajweed.

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The distance between the two vector patterns above will be calculated using the normalized Hamming distance algorithm, normalized Manhattan distance and normalized Euclidean distance. The first analysis is calculated by normalized Hamming distance, which is the calculation through equation (1) and the results are visible

D =

|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+

|0−1|+|0−1|+|0−1|+|0−1|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+

|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+⋯

|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+

|0+1|+|0+1|+|0+1|+|0+1|+|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+

|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+|0+0|+⋯

D = ¹¹¹

111= 1 (4)

The introduction of normalized hamming distance results is 1.

Then the calculation is done with normalized Manhattan distance based on equation (2) and the results are seen:

D =

|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+

|0−1|+|0−1|+|0−1|+|0−1|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+

|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+⋯

676

D = ¹¹¹

676= 0,164 (5)

After the introduction with normalized Manhattan distance, the result is 0.164. Then a comparison is made with the normalized uclidean distance based on equation (3) and the results are visible

D = √

|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+

|0−1|+|0−1|+|0−1|+|0−1|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+

|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+|0−0|+⋯

676

D = √0,164 = 0,405 (6)

The results of the calculation of the three normalized distance formulas look so different that they are tested with other patterns to get the results in table 1 below.

Table 1. Training of data

Pattern

Applied Normalized

Hamming Distance

Manhattan Distance

Euclidean Distance

1 0,266 0,516

1 0,252 0,502

1 0,415 0,644

1 0,454 0,673

From table 1 it can be seen that using Normalized Hamming Distance does not work the algorithm in applying the classification of the Al-Qur’an recitation because some of the patterns calculated for the distance value are 1, while Normalized Manhattan Distance is consistent at <0.5 and calculations through Normalized Euclidean Distance are consistent in the middle which is 0.4 ≤ D ≤ 0.7 and the result. So from the discussion that the closer to the value of 0, the more accurate the results. But on the contrary if the value is close to 1 then the value is not accurate even if the value is 1 then the algorithm is not used in the Tajweed Al-Qur’an case. Seen in the normalized hamming distance table cannot be used in the Tajweed Al-Qur’an case. Meanwhile the normalized manhattan distance which is consistent at <0.5 is the optimal algorithm used in this case. Whereas Normalized Euclidean Distance which in other cases is very optimal is used, but in this case normalized euclidean distance is not optimal because the value of the exactly distance in the middle or 0.4 ≤ D

≤ 0.7 as the range.

4. CONCLUSION

The smallest value by testing on the Al-Qur’an recitation with normalized hamming distance is 1. Whereas with the normalized formula Manhattan distance the exactly value is <0.5 and normalized Euclidean distance is the value achieved exactly in the middle 0 to 1 which is 0.4 ≤ D ≤ 0.7 . Thus, the normalized hamming distance algorithm cannot function in the Tajweed Al- Qur’an classification case and the most optimal in this case is the normalized Manhattan distance algorithm.

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