A smart access control using an efficient license plate location and recognition approach

摘要

Nowadays license plate recognition became a key technique to many automated systems such as road traffic monitoring, automated payment of tolls on high ways or bridges, security access, and parking lots access control. Most of the previous license plate locating (LPL) approaches are not robust in case of low-quality images. Some difficulties result from illumination variance, noise, complex and dirty background. This paper presents a real-time and robust method for license plate location and recognition. Edge features of the car image are very important, and edge density and background color can be used to successfully detect a number plate location according to the characteristics of the number plate. The proposed algorithm can efficiently determine and adjust the plate rotation in skewed images. LP quantization and equalization has been applied as an important step for successful decryption of the LP. It finds the optimal adaptive threshold corresponding to the intensity image obtained after adjusting the image intensity values. An efficient character segmentation algorithm is used in order to segment the characters in the binary license plate image. An optical character recognition (OCR) engine has then been proposed. The OCR engine includes digit dilation, contours adjustment and resizing. Each digit is resized to standard dimensions according to a neural network dataset. The back-propagation neural network (BPNN) is selected as a powerful tool to perform the recognition process. Experiments have been conducted to corroborate the efficiency of the proposed method. Experimental results showed that the proposed method has excellent performance even in case of low-quality images or images exhibiting illumination effects and noise. Experimental results illustrate the great robustness and efficiency of our method.