CLPSafe: Mobile Application for Avoid Cloned of License Plates Using Deep Learning - Sánchez, Diego (2024)
The problem of cloning vehicle license plates in Peru is detailed, by criminals to sell vehicles at a lower price or commit crimes with the stolen vehicle. A mobile application is proposed that uses convolutional neural networks and deeplearning algorithms: TensorFlow, EasyOCR and OpenCV to identify the license plate and its alphanumeric code, obtain detailed information about the vehicle and its owner, and issue reports to the authorities in case of cloned plate or stolen. The objective of the project is to speed up identification, consultation, and issuance of reports regarding vehicular identity theft, thus contributing to improving citizen security missing results. The analyzed results indicate that 75% of the experts expressed favorable opinions regarding the validation of the proposed architecture diagram for CLPSafe. The positive evaluations received endorse the feasibility and effectiveness of the proposed architecture, affirming its potential to effectively tackle the problem of license plate cloning in Peru.