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Rekonstruksi citra kendaraan menggunakan SRCNN untuk peningkatan akurasi pengenalan pelat nomor kendaraan

Vehicle images reconstruction using SRCNN for improving the recognition accuracy of vehicle license plate number

Department of Informatics, Universitas Ma Chung, Indonesia

Received: 24 Apr 2020; Revised: 11 Sep 2020; Accepted: 13 Oct 2020; Available online: 19 Oct 2020; Published: 31 Oct 2020.
Open Access Copyright (c) 2020 Jurnal Teknologi dan Sistem Komputer under

Citation Format:
Low-resolution images can be reconstructed into high-resolution images using the Super-resolution Convolution Neural Network (SRCNN) algorithm. This study aims to improve the vehicle license plate number's recognition accuracy by generating a high-resolution vehicle image using the SRCNN. The recognition is carried out by two types of character recognition methods: Tesseract OCR and SPNet. The training data for SRCNN uses the DIV2K dataset consisting of 900 images, while the training data for character recognition uses the Chars74 dataset. The high-resolution images constructed using SRCNN can increase the average accuracy of vehicle license plate number recognition by 16.9 % using Tesseract and 13.8 % with SPNet.
Keywords: SPNet; SRCNN; super resolution; Tesseract OCR; image reconstruction; license plate recognition
Funding: Universitas Ma Chung

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  1. L. Yue, S. Huanfeng, J. Li, Q. Yuan, H. Zhang, and L. Zhang, "Image super-resolution: the techniques, applications, and future," Signal Processing, vol. 128, pp. 389-408, 2016. doi: 10.1016/j.sigpro.2016.05.002
  2. J. W. Hwang and H. S. Lee, "Adaptive image interpolation based on local gradient features," IEEE Signal Processing Letters, vol. 11, no. 3, pp. 359-362. 2004. doi: 10.1109/LSP.2003.821718
  3. R. Fattal, "Image upsampling via imposed edge statistics," ACM Transactions on Graphics, vol. 26, no. 3, pp. 95-102, 2007. doi: 10.1145/1275808.1276496
  4. T. Peleg and M. Elad, "A statistical prediction model based on sparse representations for single image super-resolution," IEEE Transactions on Image Processing, vol. 23, no. 6, pp. 2569-2582, 2014. doi: 10.1109/TIP.2014.2305844
  5. J. Huang and D. Mumford, "Statistics of natural images and models," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Fort Collins, USA, Jun. 1999, pp. 541-547. doi: 10.1109/CVPR.1999.786990
  6. D. Glasner, S. Bagon, and M. Irani, "Super-resolution from a single image," in 12th International Conference on Computer Vision, Kyoto, Japan, Oct. 2009, pp. 349-356. doi: 10.1109/ICCV.2009.5459271
  7. G. Freedman and R. Fattal, "Image and video upscaling from local self-examples," ACM Transactions on Graphics (TOG), vol. 30, no. 2, pp. 1-11, 2011. doi: 10.1145/1944846.1944852
  8. M. Bevilacqua, A. Roumy, C. Guillemot, and M. L. Alberi-Morel, "Low-complexity single-image super-resolution based on nonnegative neighbor embedding," in British Machine Vision Conference, Surrey, UK, Sept. 2012, pp. 135.1-135.10. doi: 10.5244/C.26.135
  9. H. Chang, Y. Dit-Yan, and Y. Xiong, "Super-resolution through neighbor embedding," in IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Washington, USA, Jul. 2004. doi: 10.1109/CVPR.2004.1315043
  10. S. Schulter, C. Leistner, and H. Bischof, "Fast and accurate image upscaling with super-resolution forests," in IEEE Conference on Computer Vision and Pattern Recognition, Boston, USA, Jun. 2015, pp. 3791-3799. doi: 10.1109/CVPR.2015.7299003
  11. C-Y. Yang, C. Ma, and M-H. Yang, "Single-image super-resolution: A benchmark," in European Conference on Computer Vision, Zurich, Switzerland, Sept. 2014, pp. 372-386. doi: 10.1007/978-3-319-10593-2_25
  12. C. Dong, C. C. Loy, K. He, and X. Tang, "image super-resolution using deep convolutional networks," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 38, no. 2, pp. 295-307, 2015. doi: 10.1109/TPAMI.2015.2439281
  13. R. Smith, "an overview of the tesseract ocr engine," in Ninth International Conference on Document Analysis and Recognition, Parana, Brazil, Sept. 2007, pp. 629-633. doi: 10.1109/ICDAR.2007.4376991
  14. H. Ji, Z. Gao, T. Mei, and B. Ramesh, "Vehicle detection in remote sensing images leveraging on simultaneous super-resolution," IEEE Geoscience and Remote Sensing Letters, vol. 17, no. 4, pp. 676-680, 2019. doi: 10.1109/LGRS.2019.2930308
  15. D-G. Ko, S-H. Song, K-M. Kang, and S-W. Han, "Convolutional neural networks for character-level classification," IEIE Transactions on Smart Processing & Computing, vol. 6, no. 1, pp. 53-59, 2017. doi: 10.5573/IEIESPC.2017.6.1.053
  16. E. Agustsson and R. Timofte, "Ntire 2017 challenge on single image super-resolution: dataset and study," in IEEE Conference on Computer Vision and Pattern Recognition Workshops, Honolulu, USA, Jul. 2017, pp. 126-135. doi: 10.1109/CVPRW.2017.150
  17. T. E. De Campos, B. R. Babu, and M. Varma, "Character recognition in natural images," in Fourth International Conference on Computer Vision Theory and Applications, Lisboa, Portugal, Feb. 2009, pp. 1-8
  18. A. Hore and D. Ziou, "Image quality metrics: PSNR vs. SSIM," in International Conference on Pattern Recognition, Istanbul, Turkey, Aug. 2010, pp. 2366-2369. doi: 10.1109/ICPR.2010.579
  19. K. Balhaf, M. A. Alsmirat, M. Al-Ayyoub, Y. Jararweh, and M. A. Shehab, "Accelerating Levenshtein and Damerau edit distance algorithms using GPU with unified memory," in 8th International Conference on Information and Communication Systems, Irbid, Jordan, Apr. 2017, pp. 7-11. doi: 10.1109/IACS.2017.7921937
  20. G. Lin et al., "Image super-resolution using a dilated convolutional neural network," Neurocomputing, vol. 275, pp. 1219-1230, 2018. doi: 10.1016/j.neucom.2017.09.062
  21. L. Bottou, "Large-scale machine learning with stochastic gradient descent," in 19th International Conference on Computational Statistics, Paris, France, Aug. 2010, pp. 177-186. doi: 10.1007/978-3-7908-2604-3_16

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