Methodology for an automatic license plate recognition system using Convolutional Neural Networks for a Peruvian case study

Abstract

In Peru, the number of vehicles increased in the last decade, therefore, the automatic control requires a long database with a specific license plate model. It should be used for registration, evaluation and information extraction associated to the cars to control access parking with a particular license plate characteristic, especially for government buildings, therefore, an automatic evaluation of the license plate should be more dynamic and effective than European systems due to quantity of characters, specific segmentation and additional information in the Peruvian plate. License plate recognition is a widely applied system in artificial vision for the automatic obtaining of car license plates. This research article seeks to design a Peruvian license plate recognition system to reduce the time of vehicle registration and it involves accurate recognition of the plate location and extraction. Image processing techniques are used using the Python programming language, together with the OpenCV library. In addition, with YoloV4 a neural network is trained to locate the area where the license plate is located to facilitate the application of an Optical Character Recognizer (OCR). Our findings are a new improvement and evaluation in the traditional license plate software, with a new Peruvian database, so it allowed extracting the registration information instantly with high accuracy evaluated with 200 to 1000 images; therefore, the new contribution is the improvement in the false positive values with an accuracy of 100%, rate of failure of 0% and the sensibility of 100% with a specificity of 100% (neural network trained with 1000 images); besides it is the database for the future works for Peruvian cars.