License Plate Recognition using Deep learning

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Automatic license plate recognition (ALPR) is the extraction of vehicle license plate information from an image. The system model uses already captured images for this recognition process. First, the recognition system starts with character identification based on number plate extraction, Splitting characters, and template matching. ALPR is a real-life application that has to quickly and successfully process license plates under different environmental conditions, such as daytime. It plays an important role in numerous real-life applications, such as automatic toll collection, traffic law enforcement, parking lot access control, and road traffic monitoring. The system uses different templates for identifying the characters from the input images. After character recognition, an identified group of characters will be compared with database number plates for authentication. The proposed model has low complexity and is less time-consuming in terms of number plate segmentation and character recognition. This can improve the system performance and make the system more efficient by taking relevant samples. at the same time compared their advantages and disadvantages, which provide the basis for license plate recognition


The tasks of managing and using cars well, cracking theft and robbery of motor vehicles, as well as maintaining the normal order of urban transport have become increasingly heavy. Currently, it has become an important issue for the public security department to tom static management into dynamic change management and to tumor manual management into automation. There is an urgent need to employ an Intelligent Transportation System (ITS) so as to make effective management. ITS can perform efficient and reliable management of ambient vehicles under various circumstances. As one of the core technologies of ITS, Vehicle Feature Recognition Technology is an important link to police enforcement systems, automated highway toll collection systems, Urban Traffic Surveillance Systems Intelligent Parking Management systems, etc. Thus employing image processing technology to recognize the vehicle license plate number of various kinds of vehicles is not only an important issue for information process technology but also a research issue that is of great importance in modem transportation management.


Pattern Recognition uses Local Binary patterns and classifies by support vector machine but there is not much accuracy


The number plate is a pattern with very high disparities of contrast. If the number plate is very similar to the background it’s challenging to identify the location. Illumination and contrast changes as light fall changes to it. the morphological operations are used to eliminate the contrast feature within the plateIn this paper, vehicle license plate detection using a Morphological ROI map was proposed in the complex vehicle images. The ROI map is made by using the standard deviation of morphological open and close images, and the threshold value is calculated using the distribution of the ROI map to effectively detect the candidate region. After detecting candidate regions, those are verified using the features of the license plate.


  • Low complexity
  • High accuracy


  • Tracking analysis
  • Security analysis


Licncense Plate
License Plate


An efficient less time-consuming vehicle number plate detection method is projected which is performed on a multifaceted image. By using, the Sobel edge detection method here detects edges and fills the holes less than 8 pixels only. To remove the license plate we remove connected components less than 1000 pixels. Our anticipated algorithm is mainly based on the Indian automobile number plate system. Extraction of number plate accuracy may be increased for the low ambient light images.


[1] Zheng, D., Zhao, Y., & Wang, J. (2005). An efficient method of license plate location. Pattern Recognition Letters, 26(15), 2431-2438.

[2] Lee, E. R., Kim, P. K., & Kim, H. J. (1994, November). Automatic recognition of a car license plate using color image processing. In Image Processing, 1994. Proceedings. ICIP-94., IEEE International Conference (Vol. 2, pp. 301-305). IEEE.

[3] Anagnostopoulos, C. N. E., Anagnostopoulos, I. E., Loumos, V., & Kayafas, E. (2006). A license plate-recognition algorithm for intelligent transportation system applications. IEEE Transactions on Intelligent transportation systems, 7(3), 377-392

. [4] Gilly, D., & Raimond, K. (2013). A survey on license plate recognition systems. International Journal of Computer Applications, 61(6).

[5] Ktata, S., & Benzarti, F. (2012, March). License plate detection using mathematical morphology. In Sciences of Electronics, Technologies of Information and Telecommunications (SETIT), 2012 6th International Conference on (pp. 735-739). IEEE.

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