As part of this work, an algorithm was developed for the detection of road signs in the scene and the understanding of the text on them, using our own database. The goal was to isolate the signposts from the scene and understand the text on them, including the direction they point to. The algorithm was built using deep learning methods, namely YOLOv7 to detect road signs and directions on them, combined with OCR tools for text understanding. The development of the algorithm was carried out in the Python programming environment. The tests were carried out on different photos, which were taken in different conditions. The results showed a high accuracy of the detection of road signs and the direction on them, as well as understanding of the text, but also room for improvement in situations where the road signs are partially obscured or when the image quality is low. The algorithm represents a good step towards the improvement of navigation systems through the detection and interpretation of road signs. Although the results are promising, further optimization is needed to improve real-time performance under different road conditions. As part of the testing, it is particularly important to highlight the application of the implemented algorithms on the Raspberry Pi 4 embedded computer platform. Although the initial results are promising, there is a noticeable need for further optimization to ensure reliable and fast real-time image processing due to the software and hardware limitations of the embedded computer platform. This includes the optimization of hardware resources, as well as the adaptation of algorithms to make the most efficient use of the limited resources of embedded computer platforms. The fact that supports this conclusion comes from a comparison of the performance of work on a personal computer and on an embedded computer platform, where the personal computer shows better performance and the spectrum of algorithms that can be used is greater.