EDP Sciences logo
Web of Conferences logo
Search
Menu
All issues Volume 309 (2020) MATEC Web Conf., 309 (2020) 03034 References
Open Access
Issue
MATEC Web Conf.
Volume 309, 2020
2019 International Conference on Computer Science Communication and Network Security (CSCNS2019)
Article Number 03034
Number of page(s) 9
Section Smart Algorithms and Recognition
DOI https://doi.org/10.1051/matecconf/202030903034
Published online 04 March 2020
  1. Satoshi Suzuki, Keiichi A be .Topological structural analysis of digitized binary images by border following. 1985, 10.1016/0734-189X(85)90016-7. [Google Scholar]
  2. F. Yang and Z. Ma, “Vehicle license plate location based on histogramming and mathematical morphology,” in Proc. 4th IEEE Workshop Autom. Identification Advanced Technol., 2005, pp. 89–94. [Google Scholar]
  3. P. V Suryanarayana, S. K. Mitra, A. Baneree, and A. K. Roy, “A mor-phology based approach for car license plate extraction,” in Proc. IEEEIndicon, Chennai, India, Dec. 2005, pp. 24–27. [Google Scholar]
  4. H. Mahini, S. Kasaei, F. Dorri, and F. Dorri, “An efficient features-based license plate localization method,” in Proc. 18th ICPR, Hong Kong, 2006, vol. 2, pp. 841–844. [Google Scholar]
  5. S. Kim, D. Kim, Y. Ryu, and G. Kim, “A robust license-plate extraction method under complex image conditions,” in Proc. 16th Int. Conf.Pattern Recog., Quebec, QC, Canada, vol. 3, pp. 216–219. [Google Scholar]
  6. F. Martin, M. Garcia, and J. L. Alba, “New methods for automatic reading of VLP’s (Vehicle License Plates),” in Proc. IASTED Int. Conf.SPPRA, 2002. [Google Scholar]
  7. B. Hongliang and L. Changping, “A hybrid license plate extraction method based on edge statistics and morphology,” in Proc. ICPR, 2004, pp. 831–834. [Google Scholar]
  8. D. Zheng, Y. Zhao, and J. Wang, “An efficient method of license plate location,” Pattern Recognit. Lett., vol. 26, no. 15, pp. 2431–2438, Nov. 2005. [CrossRef] [Google Scholar]
  9. S. Wang and H. Lee, “Detection and recognition of license plate characters with different appearences,” in Proc. Conf. Intell. Transp. Syst., 2003, vol. 2, pp. 979–984. [Google Scholar]
  10. S. Draghici, “A neural network based artificial vision system for license plate recognition,” Int. J. Neural Syst., vol. 8, no. 1, pp. 113–126, Feb. 1997. [CrossRef] [Google Scholar]
  11. A. Broumandnia and M. Fathy, “Application of pattern recognition for Farsi license plate recognition,” in Proc. Int. Conf. GVIP, Cairo, Egypt, 2005. [Google Scholar]
  12. T.-H. Wang, F.-C. Ni, K.-T. Li, and Y.-P. Chen, “Robust license plate recognition based on dynamic projection warping,” in Proc. IEEE Int. Conf. Netw., Sens. Control, 2004, pp. 784–788. [Google Scholar]
  13. J. Cano and J. C. Perez-Cortes, Vehicle License Plate Segmentation in Natural Images, vol. 2652, F. J. Perales et al., Eds. New York: Springer-Verlag, 2003, pp. 142–149. [Google Scholar]
  14. Sagar, B.M., Shobha, G., Kumar, P.R., “Complete Kannada Optical Character Recognition with syntactical analysis of the script”, IEEE 1-4, Dec. 2008. [Google Scholar]
  15. Gita Sinha Rajneesh Rani Renu Dhir, “Handwritten Gurmukhi Numeral Recognition using Zone-based Hybrid Feature Extraction Techniques”, International Journal of Computer. Applications (0975-8887), Volume 47- No. 21 June 2012. [Google Scholar]
  16. Jalal Uddin Mahtnud, Mohammed Feroz Raihan and Chowdhury Mofizur Rahman, “A Complete OCR System for Continuous Bengali Characters”,IEEE 1372-1376 Vol. Oct. 2003. [Google Scholar]

Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.

Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.

Initial download of the metrics may take a while.