An Embedded Automatic License Plate Recognition System using Deep Learning

  • Diogo Izidio UFPE
  • Antonyus Ferreira CETENE
  • Edna Barros UFPE


An automatic system to recognize vehicle license plates is a growing need to improve safety and traffic control, specifically in major urban centers. The License Plate Recognition task is generally a computational-intensive task, where the entire input image frame is scanned, the found plates are segmented, and character recognition is then performed. Frequently, this processing is made using general purposes GPUs. This paper proposes an embedded solution to detect and recognize Brazilian license plates using convolutional neural networks (CNN). The system was implemented in a Raspberry Pi3 with a Pi NoIR v2 camera module, which was used to obtain the images of vehicles. The proposed 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. The proposed system has demonstrated to be robust to angle, lightning and noise variations. The system was validated using real license plate images under different environmental conditions reached a detection rate of 99.37% and an overall recognition rate of 97.00% while showing an average time of 2.70 seconds to process 1024x768 images with a single license plate.

Palavras-chave: Embedded systems, automatic license plate recognition (ALPR), image processing, deep learning, neural networks


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IZIDIO, Diogo; FERREIRA, Antonyus; BARROS, Edna. An Embedded Automatic License Plate Recognition System using Deep Learning. In: SIMPÓSIO BRASILEIRO DE ENGENHARIA DE SISTEMAS COMPUTACIONAIS (SBESC), 8. , 2018, Salvador. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2018 . p. 50-57. ISSN 2237-5430.