оптичне розпізнавання символів

Mobile Application for Text Translation and Visualization in Augmented Reality Using Neural Networks

The study explores the development of a mobile application for text translation in augmented reality (AR). The primary goal is to integrate modern technologies to ensure accurate text recognition, high-quality translation, and proper visualization of the output directly on the original plane. This tool aims to simplify access to information and improve interaction with foreign-language texts in real time.

SMART PARKING SYSTEM FOR LICENSE PLATE RECOGNITION BASED ON YOLO NEURAL NETWORK AND OPTICAL CHARACTER RECOGNITION

This paper describes a license plate recognition method, exemplified by training and deploying a machine learning model. The study uses the YOLO (“You Only Look Once”) neural network architecture and optical character recognition (OCR) techniques to extract license plate characters for real-time license plate recognition. Experimental tests, including model training, validation, and evaluation, demonstrate the effectiveness of these methods in enhancing automated access control in smart parking systems