# YOLO-Type Neural Networks in the Process of Adapting Mathematical Graphs to the Needs of the Blind
## 1 Introduction  
The adaptation of traditional educational materials to digital form is an important direction of development in the modern era. With the rapid advancement of electronic devices, an important aspect of everyday life is the adaptation of printed graphical information into interactive virtual form. This is particularly significant for blind and visually impaired people, for whom the accessibility of visual graphics can sometimes be challenging. Digital materials need to be adapted to meet their needs, particularly in terms of how information is conveyed through the other senses to compensate for the lost sense of sight. There are currently numerous solutions to help visually impaired people, but the adaptation of graphics is still a major challenge in many disciplines. One of these is the area of education, where the sciences very often rely on the use of images with graphs and diagrams. In the field of mathematics, for example, it is possible to specify a number or even a dozen items that can be placed on graphs to convey information concisely and intuitively. Most frequently, these elements take the form of additional descriptions on the graph, or details written below specific objects such as points or parabolas. Unfortunately, this is a significant barrier for blind people who cannot read this information, making the whole process of acquiring knowledge provided in this format increasingly difficult. Over the years, researchers have proposed many solutions to this problem, and among them was using tactile printouts and audio-tactile displays, but the basis for their positive impact is still the aspect of presenting educational material in a specified adapted way [1]. Another alternative approach is the DotPad--a small tactile graphic display that uses braille pinsand AI (Artificial Intelligence) to present images in real time [2]. The last approach worth mentioning in the context of this article's background is 3D shapes that can be used as real-life object representations [3]. In all of the described cases, the main issues still are the process of correctly adapting the fundamental material used so far and the complexity of this process. An ideal solution would involve creating a tool that allows for any graph or diagram to be adapted to an accessible form without needless effort. This adaptation includes the removal of redundant information contained in the graphics, such as theoretical notes, which are incomprehensible to a blind person in this way and need to be provided in another form, such as an audio description. The answer to these challenges may lie in the use of machine learning algorithms, which are becoming increasingly important in modern technological applications. Modern society is increasingly relying on AI to solve basic everyday tasks, as evidenced by the popular generative models that are used every day [4]. Their rapid development opens many avenues of potential opportunities to automatically or semi-automatically adapt graphics to convey all the information in an accessible way to a blind person. The described aspect is highly important because, in today's society, there is a strong emphasis on equal development opportunities for people with and without disabilities. In recent years, machine learning has become one of the key areas in which information technology continues to evolve. It has applications in a wide range of fields, from the aforementioned generative models for information retrieval to scientific research and, last but not least, the automation of basic everyday activities. Some specific cases include text classification [5], speech recognition [6], and weather forecasting [7]. In all of these cases, different AI algorithms are used as the standard. In the context of the main focus of this article, it must be said that machine learning is a general term that can be divided into several distinct subsections of algorithms that are used for particular purposes.