The Identification of Balinese Scripts&amp;apos; Characters based on Semantic Feature and K Nearest Neighbor

Sudarma, Made; Darma, I Wayan Agus Surya

doi:10.5120/15845-4727

Cited by 7 publications

(6 citation statements)

References 1 publication

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“…The K-Nearest Neighbor (KNN) algorithm is a method for carrying out the object classification process where the results of a new object sample are taken based on the majority of which are close to the old object in question. The purpose of the KNN algorithm [25]- [27] is to classify new objects based on characteristics and training data [14]. The KNN algorithm performs the work process based on the shortest distance from the new object data to the old object data to determine the KNN value as the predictive value of the new instance value.…”

Section: K-nearest Neighbors (K-nn)mentioning

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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Section: K-nearest Neighbors (K-nn)mentioning

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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“…Semantic features are used to look for semantic characteristics of Balinese characters such as number of black strokes, loops, endpoints, character lengths, character widths and openings of characters. From the pixels formed to a vector, it can be seen that the stroke of the pixel will form an image of the Balinese script character [7]. 0 0 2 2 1 0 2 3 0 3 3 1 0 0 2 2 1 0 1 4 1 3 2 1 0 0 1 1 1 2 2 3 2 1 2 1 0 0 1 1 1 2 3 2 1 1 2 1 0 0 1 1 1 2 2 2 1 1 2 1 0 0 1 2 1 2 2 2 1 1 2 1 0 0 1 1 1 2 1 3 1 2 …”

Section: Semantic Featurementioning

confidence: 99%

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

Darma¹

2018

Preprint

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Balinese character recognition is a technique to recognize feature or pattern of Balinese character. Feature of Balinese character is generated through feature extraction process. This research using handwritten Balinese character. Feature extraction is a process to obtain the feature of character. In this research, feature extraction process generated semantic and direction feature of handwritten Balinese character. Recognition is using K-Nearest Neighbor algorithm to recognize 81 handwritten Balinese character. The feature of Balinese character images tester are compared with reference features. Result of the recognition system with K=3 and reference=10 is achieved a success rate of 97,53%.

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“…Wrésastra script is a Balinese script that popularly referred to as anacaraka consisting of 18 characters as shown in Figure 1. [2] based on the special characteristics of Balinese script. A study that provides an overview of the special feature extraction of Balinese script using the zoning method also has been carried out [3].…”

Section: Introductionmentioning

confidence: 99%

Implementation of Zoning and K-Nearest Neighbor in Character Recognition of Wrésastra Script

Darma¹

2019

LKJITI

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Balinese script is an important aspect that packs the Balinese culture from time to time which continues to experience development along with technological advances. Balinese script consists of three types (1) Wrésastra, (2) Swalalita and (3) Modre which have different types of characters. The Wrésastra and Swalalita script are Balinese scripts which grouped into the script criteria that are used to write in the field of everyday life. In this research, the zoning method will be implemented in the feature extraction process to produce special features owned by Balinese script. The results of the feature extraction process will produce special features owned by Balinese script which will be used in the classification process to recognize the character of Balinese script. Special features are produced using the zoning method, it will divide the image characters area of ??Balinese scripts into several regions, to enrich the features of each Balinese script. The result of feature extractions is stored as training data that will be used in the classification process. K-Nearest Neighbors is implemented in the special feature classification process that is owned by the character of Balinese script. Based on the results of the test, the highest level of accuracy was obtained using the value K=3 and reference=10 with the accuracy of Balinese script recognition 97.5%.

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The Identification of Balinese Scripts' Characters based on Semantic Feature and K Nearest Neighbor

Cited by 7 publications

References 1 publication

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

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

Handwritten Balinesse Character Recognition using K-Nearest Neighbor

Implementation of Zoning and K-Nearest Neighbor in Character Recognition of Wrésastra Script

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