[1].F. Utama, “Pengenalan Aksara Melalui Media,” Iqra’ J. Kaji. Ilmu Pendidik., vol. 2, no. 2, pp. 433–457, 2017
[2].D. K. Sharma, V. Emilia, B. Le, and H. Son, “Lecture Notes in Networks and Systems 106 Micro-Electronics and Telecommunication Engineering,” Micro-Electronics Telecommun. Eng., pp. 693–705, 2019.
[3].K. T. Y. Aditya, M. W. A. Kesiman, and G. A. Pradnyana, “Pengembangan Game Edukasi Tematik Aksara dan Bahasa Bali,” Kumpul. Artik. Mhs. Pendidik. Tek. Inform., vol. 8, no. 3, pp. 522–533, 2019, [Online]. Available: https://ejournal.undiksha.ac.id/index.php/KP/article/viewFile/522/14044.
[4].G. S. Damayanti, “Transformasi Cerita Prabu Watugunung dalam Motif Batik pada Kebaya,” 2020, [Online]. Available: http://digilib.isi.ac.id/id/eprint/7165.
[5].S. Maulidan, “TRADISI SEMBAHYANG UMAT BUDDHA,” Fak. USHULUDDIN DAN FILSAFAT Univ. Islam NEGERI AR-RANIRY, 2016.
[6].N. Gautam and S. S. Chai, “Optical character recognition for Brahmi script using geometric method,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 3–11, pp. 131–136, 2017.FIGURE 5. Classification Result
[7].R. A. Pangestu, B. Rahmat, and F. T. Anggraeny, “Implementasi Algoritma CNN untuk Klasifikasi Citra Lahan dan Perhitungan Luas,” Inform. dan Sist. Inf., vol. 1, no. 1, pp. 166–174, 2020.
[8].C. Zhang, W. Ding, G. Peng, F. Fu, and W. Wang, “Street View Text Recognition with Deep Learning for Urban Scene Understanding in Intelligent Transportation Systems,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4727–4743, 2021, doi: 10.1109/TITS.2020.3017632.
[9].Y. Zheng, Y. Cai, G. Zhong, Y. Chherawala, Y. Shi, and J. Dong, “Stretching deep architectures for text recognition,” Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, vol. 2015-November, pp. 236–240, 2015, doi: 10.1109/ICDAR.2015.7333759.
[10].I. G. and Y. B. and A. Courville, Deep learning, vol. 29, no. 7553. 2016.
[11].A. Shrivastava, J. Amudha, D. Gupta, and K. Sharma, “Deep Learning Model for Text Recognition in Images,” 2019 10th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2019, pp. 1–6, 2019, doi: 10.1109/ICCCNT45670.2019.8944593.
[12].N. Gautam, S. S. Chai, and J. Jose, “Recognition of Brahmi words by Using Deep Convolutional Neural Network,” no. May, 2020, doi: 10.20944/preprints202005.0455.v1.
[13].M. A. Pragathi, K. Priyadarshini, S. Saveetha, A. S. Banu, andK. O. Mohammed Aarif, “Handwritten Tamil Character Recognition UsingDeep Learning,” Proc. -Int. Conf. Vis. Towar. Emerg. Trends Commun. Networking, ViTECoN 2019, pp. 1–5, 2019, doi: 10.1109/ViTECoN.2019.8899614.
[14].K. Chellapilla et al., “High Performance Convolutional Neural Networks for Document Processing To cite this version : High Performance Convolutional Neural Networks for Document Processing,” 2006.
[15].H. Wang, “Garbage recognition and classification system based on convolutional neural network vgg16,”Proc. -2020 3rd Int. Conf. Adv. Electron. Mater. Comput. Softw. Eng. AEMCSE 2020, pp. 252–255, 2020, doi: 10.1109/AEMCSE50948.2020.00061
[16].Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, 2016, doi: 10.1016/j.neucom.2015.09.116.
[17].M. Yousef, K. F. Hussain, and U. S. Mohammed, “Accurate, data-efficient, unconstrained text recognition with convolutional neural networks,” Pattern Recognit., vol. 108, p. 107482, 2020, doi: 10.1016/j.patcog.2020.107482.
[18].K. P. Danukusmo, “Implementasi Deep Learning Menggunakan Convolutional Neural Network untuk Klasifikasi Citra Candi Berbasis GPU,” Univ. Atma Jaya Yogyakarta, 2017.
[19].M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, “A new deep convolutional neural network for fast hyperspectral image classification,” ISPRS J. Photogramm. Remote Sens., vol. 145, pp. 120–147, 2018, doi: 10.1016/j.isprsjprs.2017.11.021.
[20].Y. Gultom, A. M. Arymurthy, and R. J. Masikome, “Batik Classification using Deep Convolutional Network Transfer Learning,” J. Ilmu Komput. dan Inf., vol. 11, no. 2, p. 59, 2018, doi: 10.21609/jiki.v11i2.507.
[21].K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 -Conf. Track Proc., pp. 1–14, 2015.
[22].X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem,” Inf. Sci. (Ny).,vol. 340–341, pp. 250–261, 2016, doi: 10.1016/j.ins.2016.01.033.
[23].S. Ruuska, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle,” Behav. Processes, vol. 148, pp. 56–62, 2018, doi: 10.1016/j.beproc.2018.01.004.
[24].E. Lashgari, D. Liang, and U. Maoz, “Data augmentation for deep-learning-based electroencephalography,” J. Neurosci. Methods, vol. 346, no. July, p. 108885, 2020, doi: 10.1016/j.jneumeth.2020.108885.
[25].L. Taylor and G. Nitschke, “Improving Deep Learning with Generic Data Augmentation,” Proc. 2018 IEEE Symp. Ser. Comput. Intell. SSCI 2018, pp. 1542–1547, 2019, doi: 10.1109/SSCI.2018.8628742.
[2].D. K. Sharma, V. Emilia, B. Le, and H. Son, “Lecture Notes in Networks and Systems 106 Micro-Electronics and Telecommunication Engineering,” Micro-Electronics Telecommun. Eng., pp. 693–705, 2019.
[3].K. T. Y. Aditya, M. W. A. Kesiman, and G. A. Pradnyana, “Pengembangan Game Edukasi Tematik Aksara dan Bahasa Bali,” Kumpul. Artik. Mhs. Pendidik. Tek. Inform., vol. 8, no. 3, pp. 522–533, 2019, [Online]. Available: https://ejournal.undiksha.ac.id/index.php/KP/article/viewFile/522/14044.
[4].G. S. Damayanti, “Transformasi Cerita Prabu Watugunung dalam Motif Batik pada Kebaya,” 2020, [Online]. Available: http://digilib.isi.ac.id/id/eprint/7165.
[5].S. Maulidan, “TRADISI SEMBAHYANG UMAT BUDDHA,” Fak. USHULUDDIN DAN FILSAFAT Univ. Islam NEGERI AR-RANIRY, 2016.
[6].N. Gautam and S. S. Chai, “Optical character recognition for Brahmi script using geometric method,” J. Telecommun. Electron. Comput. Eng., vol. 9, no. 3–11, pp. 131–136, 2017.FIGURE 5. Classification Result
[7].R. A. Pangestu, B. Rahmat, and F. T. Anggraeny, “Implementasi Algoritma CNN untuk Klasifikasi Citra Lahan dan Perhitungan Luas,” Inform. dan Sist. Inf., vol. 1, no. 1, pp. 166–174, 2020.
[8].C. Zhang, W. Ding, G. Peng, F. Fu, and W. Wang, “Street View Text Recognition with Deep Learning for Urban Scene Understanding in Intelligent Transportation Systems,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 7, pp. 4727–4743, 2021, doi: 10.1109/TITS.2020.3017632.
[9].Y. Zheng, Y. Cai, G. Zhong, Y. Chherawala, Y. Shi, and J. Dong, “Stretching deep architectures for text recognition,” Proc. Int. Conf. Doc. Anal. Recognition, ICDAR, vol. 2015-November, pp. 236–240, 2015, doi: 10.1109/ICDAR.2015.7333759.
[10].I. G. and Y. B. and A. Courville, Deep learning, vol. 29, no. 7553. 2016.
[11].A. Shrivastava, J. Amudha, D. Gupta, and K. Sharma, “Deep Learning Model for Text Recognition in Images,” 2019 10th Int. Conf. Comput. Commun. Netw. Technol. ICCCNT 2019, pp. 1–6, 2019, doi: 10.1109/ICCCNT45670.2019.8944593.
[12].N. Gautam, S. S. Chai, and J. Jose, “Recognition of Brahmi words by Using Deep Convolutional Neural Network,” no. May, 2020, doi: 10.20944/preprints202005.0455.v1.
[13].M. A. Pragathi, K. Priyadarshini, S. Saveetha, A. S. Banu, andK. O. Mohammed Aarif, “Handwritten Tamil Character Recognition UsingDeep Learning,” Proc. -Int. Conf. Vis. Towar. Emerg. Trends Commun. Networking, ViTECoN 2019, pp. 1–5, 2019, doi: 10.1109/ViTECoN.2019.8899614.
[14].K. Chellapilla et al., “High Performance Convolutional Neural Networks for Document Processing To cite this version : High Performance Convolutional Neural Networks for Document Processing,” 2006.
[15].H. Wang, “Garbage recognition and classification system based on convolutional neural network vgg16,”Proc. -2020 3rd Int. Conf. Adv. Electron. Mater. Comput. Softw. Eng. AEMCSE 2020, pp. 252–255, 2020, doi: 10.1109/AEMCSE50948.2020.00061
[16].Y. Guo, Y. Liu, A. Oerlemans, S. Lao, S. Wu, and M. S. Lew, “Deep learning for visual understanding: A review,” Neurocomputing, vol. 187, pp. 27–48, 2016, doi: 10.1016/j.neucom.2015.09.116.
[17].M. Yousef, K. F. Hussain, and U. S. Mohammed, “Accurate, data-efficient, unconstrained text recognition with convolutional neural networks,” Pattern Recognit., vol. 108, p. 107482, 2020, doi: 10.1016/j.patcog.2020.107482.
[18].K. P. Danukusmo, “Implementasi Deep Learning Menggunakan Convolutional Neural Network untuk Klasifikasi Citra Candi Berbasis GPU,” Univ. Atma Jaya Yogyakarta, 2017.
[19].M. E. Paoletti, J. M. Haut, J. Plaza, and A. Plaza, “A new deep convolutional neural network for fast hyperspectral image classification,” ISPRS J. Photogramm. Remote Sens., vol. 145, pp. 120–147, 2018, doi: 10.1016/j.isprsjprs.2017.11.021.
[20].Y. Gultom, A. M. Arymurthy, and R. J. Masikome, “Batik Classification using Deep Convolutional Network Transfer Learning,” J. Ilmu Komput. dan Inf., vol. 11, no. 2, p. 59, 2018, doi: 10.21609/jiki.v11i2.507.
[21].K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” 3rd Int. Conf. Learn. Represent. ICLR 2015 -Conf. Track Proc., pp. 1–14, 2015.
[22].X. Deng, Q. Liu, Y. Deng, and S. Mahadevan, “An improved method to construct basic probability assignment based on the confusion matrix for classification problem,” Inf. Sci. (Ny).,vol. 340–341, pp. 250–261, 2016, doi: 10.1016/j.ins.2016.01.033.
[23].S. Ruuska, W. Hämäläinen, S. Kajava, M. Mughal, P. Matilainen, and J. Mononen, “Evaluation of the confusion matrix method in the validation of an automated system for measuring feeding behaviour of cattle,” Behav. Processes, vol. 148, pp. 56–62, 2018, doi: 10.1016/j.beproc.2018.01.004.
[24].E. Lashgari, D. Liang, and U. Maoz, “Data augmentation for deep-learning-based electroencephalography,” J. Neurosci. Methods, vol. 346, no. July, p. 108885, 2020, doi: 10.1016/j.jneumeth.2020.108885.
[25].L. Taylor and G. Nitschke, “Improving Deep Learning with Generic Data Augmentation,” Proc. 2018 IEEE Symp. Ser. Comput. Intell. SSCI 2018, pp. 1542–1547, 2019, doi: 10.1109/SSCI.2018.8628742.
- Abstract viewed - 341 times
- PDF downloaded - 253 times
Copyright
© Computer Engineering and Applications Journal, 2022
Affiliations
Vincen Vincen
Universitas Sriwijaya
Samsuryadi Samsuryadi
Department of Computer System, Faculty of Computer Science, Universitas Sriwijaya
Brahmi Script Classification using VGG16 Architecture Convolutional Neural Network
Klasifikasi Tulisan Aksara Brahmi menggunakan Convolutional Neural Network Arsitektur VGG16
Abstract
Many Indonesians have difficulty reading and learning the Brahmi script. Solving these problems can be done by developing software. Previous research has classified the Brahmi script but has not had an output that matches the letter. Therefore, letter classification is carried out as part of the process of recognizing Brahmi script. This study uses the Convolutional Neural Network (CNN) method with the VGG16 architecture for classifying Brahmi script writing. Training results from various amounts of image data. Smooth model. The requested image data is a 224x224 binary image. This study has the highest quality, accuracy is 96%, highest recall is 98% and highest precision is 98%.