Javanese Document Image Recognition Using Multiclass Support Vector Machine

Sugianela, Yuna; Suciati, Nanik

doi:10.21512/commit.v13i1.5330

Cited by 7 publications

(9 citation statements)

References 14 publications

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“…The Javanese letters consist of 20 main characters known as Ha-Na Ca-Ra-Ka. The name Ha-Na-Ca-Ra-Ka comes from the first five letters of the Javanese letters [23]. The original data used to train the model uses the set data of the Javanese language alphabet image shown in Fig.…”

Section: E Proposed Javanese Letters Training Modelmentioning

confidence: 99%

A Classification Javanese Letters Model using a Convolutional Neural Network with KERAS Framework

Harjoseputro¹

2020

IJACSA

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One of the essential things in research engaged in the field of Computer Vision is image classification, wherein previous studies models were used to classify an image. Javanese Letters, in this case, is a basis of a sentence that uses the Javanese language. The problem is that Javanese sentences are often found in Yogyakarta, especially the use of name tourist attractions, making it difficult for tourists to translate these Javanese sentences. Therefore, in this study, we try to create a Javanese character classification model hoping that this model will later be used as a basis for developing research into the next stage. One of the most popular methods lately for dealing with image classification problems is to use Deep Learning techniques, namely using the Convolutional Neural Network (CNN) method using the KERAS framework. The simplicity of the training model and dataset used in this work brings the advantage of computation weight and time. The model has an accuracy of 86.68% using 1000 datasets and conducted for 50 epochs based on the results. The average inference time with the same specification mentioned above is 0.57 seconds, and again the fast inference time is because of the simplicity of the model and dataset toolbar. This model's advantages with fast and light computation time bring the possibility to use this model on devices with limited computation resources such as mobile devices, familiar web server interface, and internet-of-things devices.

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Section: E Proposed Javanese Letters Training Modelmentioning

confidence: 99%

A Classification Javanese Letters Model using a Convolutional Neural Network with KERAS Framework

Harjoseputro¹

2020

IJACSA

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“…Javanese script or better known as Hanacaraka is often used to write literature and daily writing in Javanese from the mid-15th century to the mid-20th century, but over time it is also used to write in various regional languages. Javanese script has written in the form of letters [1][2][3] [4]. Along with the development of the times, Javanese script seems to be forgotten and rarely recognized by the public, especially the younger generation today.…”

Section: Introductionmentioning

confidence: 99%

Javanese Character Recognition Based on K-Nearest Neighbor and Linear Binary Pattern Features

Susanto¹,

Mulyono

Sari³

et al. 2022

KINETIK

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Javanese script (Hanacaraka) is one of the cultures owned by Indonesia. Javanese script is found in temples, inscriptions, cultural and prehistoric sites, ancient Javanese manuscripts, Gulden series banknotes, street signage, and palace documents. Javanese script has a form with an article, and the use of reading above the script is a factor that affects the character detection process. Punctuation marks, clothing, Swara script, vowels, and consonants are parts of the script that are often found in Javanetest scripts. Preserving Javanese script in the digital era, of course, must use technology that can support the digitization of Javanese script through the script detection process. The concept of script image is the image of Javanese script in ancient manuscripts. The process of character detection using certain techniques can be carried out to extract characters so that they can be read. Detection of Javanese characters can be found by finding a testing image. Here, we had been used 10 words images consisting of 3 to 5 syllables with the vowel aiu. Dataset process by Linear Binary Pattern (LBP) feature extraction, which is used to characterize images and describe image textures locally. LBP has been used in r=4 and preprocessing is also done by thresholding with d=0.3. This process can be done using the K-Nearest Neighbor algorithm. In 10 datasets of Javanese script words, an average accuracy value of 90.5% was obtained. The accuracy value of 100% is the highest and 50% is the lowest.

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“…In addition, as a country consists of diverse culture and ethnicities, Indonesia also has traditional characters included in the non-Italic alphabets, e.g., Javanese, which are different from Roman alphabets. These tend to possess different structures, complexity and shapes, making them a challenge to the system [16]. Moreover, there are about 20 syllables construct, equipped with special signs, related to the pronunciation and other complementary characters with different functions [16] [17], although Javanese people rarely use them in their daily life [18].…”

Section: Introductionmentioning

confidence: 99%

“…In addition, several prior studies have already addressed these issues, utilizing several methods, encompassing the Hidden Markov Model [19], Artificial Neural Network [18], Multiclass Support Vector Machine [16], as well as CNN technique [17]. However, this study tends to focus on the means of applying the CNN algorithm in a mobile device platform without suffering computation cost, alongside maintaining an acceptable accuracy in recognizing Javanese character.…”

Section: Introductionmentioning

confidence: 99%

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

Harjoseputro

Handarkho

Adie

2019

Int. J. Interact. Mob. Technol.

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<p class="0abstract">the rapid development of mobile technologies allows platform devices to perform sophisticated tasks, including character recognition. These identification systems are notable techniques that required high computation cost, in order to achieve acceptable accuracy resulting from diversity in alphabet shape and method of writing, especially for the non-Latin alphabet, e.g., Javanese letter. In addition, numerous studies have attempted to address these issues by employing a Convolution Neural Network (CNN) due to its ability to provide high accuracy in character detection. However, the performance on mobile devices is possibly faced with problems resulting from the limitation of computation resource on the platform that also affect computation cost. This study, therefore, proposes a 2-tier architecture by placing the mobile app as a client that invokes a Javanese letters classifier service, which is based on CNN, and implemented in the web-server through the Application Program Interface (API). The results show that the letter classification was successfully implemented in a mobile platform, with an accuracy rate of 86.68%, utilizing training for 50 epochs, and an average time of 1935 ms.</p>

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Javanese Document Image Recognition Using Multiclass Support Vector Machine

Cited by 7 publications

References 14 publications

A Classification Javanese Letters Model using a Convolutional Neural Network with KERAS Framework

A Classification Javanese Letters Model using a Convolutional Neural Network with KERAS Framework

Javanese Character Recognition Based on K-Nearest Neighbor and Linear Binary Pattern Features

The Javanese Letters Classifier with Mobile Client-Server Architecture and Convolution Neural Network Method

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