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). 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). Many existing methods have been used to deal with different environmental complexity factors but are limited to motion deblurring. The aim of our research is to provide an effective and robust solution for recognizing characters present in license plates in complex environmental conditions. 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. Our research provides a series of different approaches to execute different steps in the character recognition process. 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. License Plates (LPs) are detected using the Improved Bernsen Algorithm (IBA) with Connected Component Analysis(CCA). Finally, with the aid of the proposed Xception model with transfer learning, the characters in LPs are recognised. Here we have not used any segmentation technique to split the characters. 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. 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. The suggested strategy does better than the current best practices in both numbers and quality.

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Deep learning’s emergence and continued advancement have significantlychanged and redefined computer vision. Scene text identification andrecognition, a significant area of computer vision research, has unavoidablybeen impacted by this wave of innovation, and consequently hasentered the era of deep learning.It has a wide range of practical uses,from semantic natural scene analysis to navigation for those with visualimpairments. The complexity of the background, the image quality, thetext orientation, the text size, etc. present obstacles to experts in thefield of scene text analysis. Our proposed model provides a solution forscene text localization, detection, and refinement by integrating featurebasedand deep learning-based approaches. To obtain high quality andclear images, our model super-resolves images and deblurs them usingan Edge Attention Network (EAN). It is followed by a candidate regionproposal module that uses Maximally Stable Extremal Region (MSER)and Stroke Width Transform (SWT) to create text region suggestions.The detection result is then improved using a post-processing methodthat uses the idea of a Generative Adversarial Network (GAN). Standarddatasets including ICDAR 2013, ICDAR 2015, and the Street View Text(SVT) have been used for experiments verification which concludes thatour proposed model consistently produces acceptable results.