This dataset includes the images of vehicles captured in different lighting and weather conditions such as sunny, rainy, cloudy, blurred, low illumination, foggy, and night.
Four benchmark datasets such as Stanford Cars, FZU Cars, HumAIn 2019 Challenge datasets, and Application-Oriented License Plate (AOLP) dataset, as well as our own collected dataset, were used for the validation of our proposed algorithm. Here we have not used any segmentation technique to split the characters. Finally, with the aid of the proposed Xception model with transfer learning, the characters in LPs are recognised. License Plates (LPs) are detected using the Improved Bernsen Algorithm (IBA) with Connected Component Analysis(CCA). The proposed approach presents the concept of Generative Adversarial Networks (GAN) with Discrete Cosine Transform (DCT) Discriminator (DCTGAN), a joint image super resolution and deblurring approach that uses a discrete cosine transform with low computational complexity to remove various types of blur and complexities from licence plates.
XEOMA LICENSE PLATE RECOGNITION SENSITIVITY SERIES
Our research provides a series of different approaches to execute different steps in the character recognition process. Our proposed approach is capable of handling not only the motion-blurred LPs but also recognizing the characters present in different types of low resolution and blurred license plates, illegible vehicle plates, license plates present in different weather and light conditions, and various traffic circumstances, as well as high-speed vehicles. The aim of our research is to provide an effective and robust solution for recognizing characters present in license plates in complex environmental conditions. Many existing methods have been used to deal with different environmental complexity factors but are limited to motion deblurring. One of the most actively researched areas is Vehicle Licence Plate Recognition (VLPR), concerned with determining the characters contained in a vehicle’s Licence Plate (LP). The current breakthroughs in the highway research sector have resulted in a greater awareness and focus on the construction of an effective Intelligent Transportation System (ITS).
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