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Javanese Character Recognition Based on K-Nearest Neighbor and Linear Binary Pattern Features
Corresponding Author(s) : Ajib Susanto
Kinetik: Game Technology, Information System, Computer Network, Computing, Electronics, and Control,
Vol. 7, No. 3, August 2022
Abstract
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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References
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Y. Sugianela and N. Suciati, “Javanese Document Image Recognition Using Multiclass Support Vector Machine,” CommIT (Communication and Information Technology) Journal, vol. 13, no. 1, p. 25, May 2019, doi: 10.21512/commit.v13i1.5330.
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L. D. Krisnawati and A. W. Mahastama, “Building Classifier Models for on-off Javanese Character Recognition,” in Proceedings of the 21st International Conference on Information Integration and Web-based Applications & Services, Dec. 2019, pp. 25–34. doi: 10.1145/3366030.3366050.
S. C. Satapathy, N. Sri Madhava Raja, V. Rajinikanth, A. S. Ashour, and N. Dey, “Multi-level image thresholding using Otsu and chaotic bat algorithm,” Neural Computing and Applications, vol. 29, no. 12, pp. 1285–1307, 2018, doi: 10.1007/s00521-016-2645-5.
S. Jansi and P. Subashini, “Optimized Adaptive Thresholding based Edge Detection Method for MRI Brain Images,” International Journal of Computer Applications, vol. 51, no. 20, pp. 1–8, Aug. 2012, doi: 10.5120/8155-1525.
M. H. Merzban and M. Elbayoumi, “Efficient solution of Otsu multilevel image thresholding: A comparative study,” Expert Systems with Applications, vol. 116, pp. 299–309, 2019, doi: 10.1016/j.eswa.2018.09.008.
N. Eslahi and A. Aghagolzadeh, “Compressive Sensing Image Restoration Using Adaptive Curvelet Thresholding and Nonlocal Sparse Regularization,” IEEE Transactions on Image Processing, vol. 25, no. 7, pp. 3126–3140, 2016, doi: 10.1109/TIP.2016.2562563.
Y. Chychkarov, A. Serhiienko, I. Syrmamiikh, and A. Kargin, “Handwritten Digits Recognition Using SVM, KNN, RF and Deep Learning Neural Networks,” 2021.
A. Lamba and D. Kumar, “Survey on KNN and Its Variants,” International Journal of Advanced Research in Computer and Communication Engineering, vol. 5, no. 5, pp. 430–435, 2016, doi: 10.17148/IJARCCE.2016.55101.
M. Rashad and N. A. Semary, “Using KNN and Random Forest Tree Classifiers,” Springer International Publishing Switzerland, pp. 11–17, 2014.
Rismiyati, Khadijah, and A. Nurhadiyatna, “Deep learning for handwritten Javanese character recognition,” in 2017 1st International Conference on Informatics and Computational Sciences (ICICoS), Nov. 2017, pp. 59–64. doi: 10.1109/ICICOS.2017.8276338.
C. A. Sari, M. W. Kuncoro, D. R. I. M. Setiadi, and E. H. Rachmawanto, “Roundness and Eccentricity Feature Extraction for Javanese Handwritten Character Recognition based on K-Nearest Neighbor,” in 2018 International Seminar on Research of Information Technology and Intelligent Systems (ISRITI), Nov. 2018, pp. 5–10. doi: 10.1109/ISRITI.2018.8864252.
I. F. Katili, F. D. Esabella, and A. Luthfiarta, “Pattern Recognition Of Javanese Letter Using Template Matching Correlation Method,” Journal of Applied Intelligent System, vol. 3, no. 2, pp. 49–56, Dec. 2018, doi: 10.33633/jais.v3i2.1954.
L. D. Krisnawati and A. W. Mahastama, “Building classifier models for on-off javanese character recognition,” Dec. 2019. doi: 10.1145/3366030.3366050.
F. T. Anggraeny, E. P. Mandyartha, and D. S. Y. Kartika, “Texture Feature Local Binary Pattern for Handwritten Character Recognition,” in 2020 6th Information Technology International Seminar (ITIS), Oct. 2020, pp. 125–129. doi: 10.1109/ITIS50118.2020.9320980.
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B. Yang and S. Chen, “A comparative study on local binary pattern (LBP) based face recognition: LBP histogram versus LBP image,” Neurocomputing, vol. 120, pp. 365–379, Nov. 2013, doi: 10.1016/j.neucom.2012.10.032.
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A. E. Minarno, F. D. Setiawan Sumadi, H. Wibowo, and Y. Munarko, “Classification of batik patterns using K-Nearest neighbor and support vector machine,” Bulletin of Electrical Engineering and Informatics, vol. 9, no. 3, pp. 1260–1267, Jun. 2020, doi: 10.11591/eei.v9i3.1971.
J. Kim, B.-S. Kim, and S. Savarese, “Comparing Image Classification Methods: K-Nearest-Neighbor and Support-Vector-Machines,” Applied Mathematics in Electrical and Computer Engineering, pp. 133–138, 2012.
M. Sudarma and I. W. A. Surya Darma, “The Identification of Balinese Scripts Characters based on Semantic Feature and K Nearest Neighbor,” International Journal of Computer Applications, vol. 91, no. 1, pp. 14–18, 2014, doi: 10.5120/15845-4727.
K. Chandel, V. Kunwar, S. Sabitha, T. Choudhury, and S. Mukherjee, “A comparative study on thyroid disease detection using K-nearest neighbor and Naive Bayes classification techniques,” CSI Transactions on ICT, vol. 4, no. 2–4, pp. 313–319, 2016, doi: 10.1007/s40012-016-0100-5.
I. U. W. Mulyono et al., “Parijoto Fruits Classification using K-Nearest Neighbor Based on Gray Level Co-Occurrence Matrix Texture Extraction,” Journal of Physics: Conference Series, vol. 1501, no. 1, 2020, doi: 10.1088/1742-6596/1501/1/012017.