Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition
IEEE Transactions on Circuits and Systems for Video Technology
One of the major causes of poor results in license plate recognition is low quality of images affected by multiple factors, such as severe illumination condition, complex background, different weather conditions, night light, and perspective distortions. In this paper, we propose a new mathematical model based on Riesz fractional operator for enhancing details of edge information in license plate images to improve the performances of text detection and recognition methods. The proposed model performs convolution operation of the Riesz fractional derivative over each input image by enhancing the edge strength in it. To test the performance of the proposed model, we conduct experiments on benchmark license plate image databases, namely, UCSD and ICDAR 2015-SR competition text image databases. Experimental results on enhancement show that the proposed model outperforms the existing baseline enhancement techniques in terms of quality measures. Furthermore, experimental results on text detection and recognition show that text detection and recognition rates are improved significantly after enhancement compared with before enhancement.
Raghunandan, K. S.; Shivakumara, Palaiahnakote; Jalab, Hamid A.; Ibrahim, Rabha W.; Kumar, G. Hemantha; Pal, Umapada; and Lu, Tong, "Riesz Fractional Based Model for Enhancing License Plate Detection and Recognition" (2018). Journal Articles. 1249.