The proposed architecture has demonstrated to be robust to angle, lightning, and noise variations while requiring a single forward pass for each network, therefore allowing faster processing compared to other deep learning approaches. The resulting system detects license plates in the captured image using Tiny YOLOv3 architecture and identifies its characters using a second convolutional network trained on synthetic images and fine-tuned with real license plate images. This paper presents a methodology for engineering a system to detect and recognize Brazilian license plates using convolutional neural networks (CNN) that is suitable for embedded systems. However, the license plate recognition task is generally computationally intensive, where the entire input image frame is scanned, the found plates are segmented, and character recognition is then performed for each segmented character. A system to automatically recognize vehicle license plates is a growing need to improve safety and traffic control, specifically in major urban centers.
