License plate Detection with object classification for Road Vehicles (LPDOBV)

Video surveillance plays a significant role in the domain of intelligent transportation systems. Intelligent transportation system aims to utilize advanced technologies to enhance the efficiency and safety of transportation system. Video surveillance has several uses, including identifying the reason behind an accident, locating a particular vehicle, and figuring out the best routes between important places. Object identification and shadow removal are the primary objectives of intelligent transportation systems. Moreover, video surveillance has other difficulties, such as text recognition. This work proposes an inner outer outline profile line (IOOPL) method for recognizing the set of objects boundary layers based on shadow elevation. It also tackles the issue of object shadows in-vehicle image segmentation not being recognized as a component of the item itself. Utilizing the delta learning algorithm, often known as the Widrow-Hoff learning rule, this work suggests a procedure for identifying and classifying cars by removing their shadow counterparts. The system is trained using a variety of vehicle kinds based on their look, colors, and construction types. Furthermore, this work employs an artificial neural networks trained approach using the high-performance delta learning technique, to classify cars and gather data on their journeys. Additionally, it introduces a strategy for license plate identification through edge dilation and text correlation. Recognizing number plates poses a challenging task in the realm of video text recognition.