Smart Insights on the Move: Deep Convolution Neural Network and Segmentation for License Plate Recognition

In the modern world, the highway is becoming an increasingly significant part of the entire transportation sector. Due to the widespread interest in Intelligent Transport System technology, numerous systems are being developed and implemented globally. The Intelligent Transport System relies heavily on license plate recognition. Modern technological advancements enable the automatic identification and interpretation of license plate details from images and video streams facilitated by sophisticated license plate recognition systems. These devices are able to reliably read alphanumeric characters from license plates, even in difficult situations like changing angles, lighting, or occlusion. This paper suggests a deep convolutional neural network-based, effective system for license plate recognition. The methodology comprises multiple significant stages, commencing with the acquisition and preprocessing of images via methods like grayscale conversion and thresholding. After that, morphological processes like dilation and erosion are employed to boost the quality of the image, and segmentation is used to separate the area of interest. Next, contour extraction within this segmented area is used to estimate the character. The features that were extracted from the segmented regions are then used to drive a CNN model that accurately recognizes the characters on a license plate. The simulation results confirm that the suggested methodology is effective in correctly identifying and decoding license plate numbers from images, with an impressive recognition accuracy of 99.54%. This method provides an effective and dependable solution for automated license plate recognition tasks. It shows great promise for real-world applications in traffic management, law enforcement, and intelligent transport systems.