Model CNN LeNet dalam Rekognisi Angka Tahun pada Prasasti Peninggalan Kerajaan Majapahit

CNN LeNet Model for Year Digit Recognition on Relic Inscriptions of Majapahit Kingdom

Tri Septianto -  Sekolah Tinggi Teknik Surabaya, Indonesia
Endang Setyati -  Sekolah Tinggi Teknik Surabaya, Indonesia
Joan Santoso* -  Sekolah Tinggi Teknik Surabaya, Indonesia
Open Access Copyright (c) 2018 Jurnal Teknologi dan Sistem Komputer
The object of the inscription has a feature that is difficult to recognize because it is generally eroded and faded. This study analyzed the performance of CNN using LeNet model to recognize the object of year digit found on the relic inscriptions of Majapahit Kingdom. Object recognition with LeNet model had a maximum accuracy of 85.08% at 10 epoch in 6069 seconds. This LeNet's performance was better than the VGG as the comparison model with a maximum accuracy of 11.39% at 10 epoch in 40223 seconds.
LeNet recognition performance; inscription digit recognition; CNN performance comparison; VGG recognition performance

How to cite:

Full Text:

Article Metrics:

Article Info
Submitted: 2018-06-04
Published: 2018-07-31
Section: Articles
Language: ID
Statistics: 137 19
  1. L. Shang, Q. Yang, J. Wang, S. Li, and W. Lei, "Detection of Rail Surface Defects Based on CNN Image Recognition and Classification," in 2018 20th International Conference on Advanced Communication Technology (ICACT), Chuncheon-si Gangwon-do, Korea, Feb. 2018, pp. 45–51.
  2. X. Zhao, X. Shi, and S. Zhang, "Facial Expression Recognition via Deep Learning," IETE Technical Review, vol. 32, no. 5, pp. 347–355, 2015.
  3. Y. Yang, D. Li, and Z. Duan, "Chinese Vehicle License Plate Recognition using Kernel-based Extreme Learning Machine with Deep Convolutional Features," IET Intelligent Transport Systems, vol. 12, no. 3, pp. 213–219, 2017.
  4. H. D. Nguyen and M. Nakagawa, "Deep Neural Networks for Online Handwritten Mathematical Characters," in 18th Meeting on Image Recognition and Understanding, 2015, pp. 1–2.
  5. S. Lee, S. J. Son, J. Oh, and N. Kwak, "Handwritten Music Symbol Classification Using Deep Convolutional Neural Networks," in 2016 International Conference on Information Science and Security, Pattaya, Thailand, Dec. 2016, pp. 1-5.
  6. S. Nagaraj, B. Muthiyan, S. Ravi, V. Menezes, K. Kapoor, and H. Jeon, "Edge-based Street Object Detection," in 2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI), Jun. 2017, pp. 1–4.
  7. A. R. F. Quiros et al., "A kNN-based Approach for the Machine Vision of Character Recognition of License Plate Numbers," in 2017 IEEE Regional 10 Conference (TENCON 2017), Nov. 2017, pp. 1081–1086.
  8. D. Singh, M. A. Khan, A. Bansal, and N. Bansal, "An Application of SVM in Character Recognition with Chain Code," in 2015 Communication, Control, and Intelligent Systems (CCIS), Mathura, India, Nov. 2015, pp. 167–171.
  9. M. Y. W. Teow, "Understanding Convolutional Neural Networks Using A Minimal Model for Handwritten Digit Recognition," in 2017 IEEE 2nd International Conference on Automatic Control and Intelligent Systems (I2CACIS), Kota Kinibalu, Malaysia, Oct. 2017, pp. 167–172.
  10. S. Albawi, T. A. Mohammed, and S. Al-Zawi, "Understanding of a Convolutional Neural Network," in 2017 International Conference on Engineering and Technology (ICET), Antalya, Turkey, Aug. 2017, pp. 1–6.
  11. P. P. Nair, A. James, and C. Saranavan, "Malayalam Handwritten Character Recognition using Convolutional Neural Network," in 2017 International Conference on Inventive Communication and Computational Technologise (ICICCT), Coimbatore, India, Mar. 2017, pp. 278–281.
  12. Z. Shokoohi, A. M. Hormat, F. Mahmoudi, and H. Badalabadi, "Persian Handwritten Numeral Recognition using Complex Neural Network and Non-linear Feature Extraction," in 2013 1st Iranian Conference on Pattern Recognition and Image Analysis (PRIA). Birjand, Iran, Mar. 2013.
  13. M. A. H. Akhand, M. M. Rahman, P. C. Shill, S. Islam, and M. M. H. Rahman, "Bangla Handwritten Numeral Recognition using Convolutional Neural Network," in 2015 International Conference on Electrical Engineering and Information Communication Technology (ICEEICT), Dhaka, Bangladesh, May 2015, pp. 1–5.
  14. M. A. Wibowo, M. Soleh, W. Pradani, A. N. Hidayanto, and A. M. Arymurthy, "Handwritten Javanese Character Recognition using Descriminative Deep Learning Technique," in 2017 2nd International Conference on Information Technology, Information Systems and Electrical Engineering (ICITISEE), Yogyakarta, Indonesia, Nov. 2017, pp. 324–329.
  15. M. He, S. Zhang, H. Mao, and L. Jin, "Recognition Confidence Analysis of Handwritten Chinese Character with CNN," in 2015 13th International Conference on Document Analysis and Recognition (ICDAR), 2015, pp. 61–65.
  16. T. Datta, B. Purkaystha, and M. S. Islam, "Bengali Handwritten Character Recognition Using Deep Convolutional Neural Network," in 2017 20th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, Dec. 2017, pp. 22–24.
  17. M. D. Zeiler, "ADADELTA: An Adaptive Learning Rate Method," arXiv: 1212.5701 [cs.LG], Dec. 2012.
  18. K. Simonyan, and A. Zisserman, "Very Deep Convolutional Networks for Large-Scale Image Recognition," arXiv: 1409.1556 [cs.CV], Sept. 2014.