# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 1 Introduction  
Currently, there are 1.3 billion people with different types of disabilities [1] who face significant challenges when it comes to using online platforms. From an accessibility point of view, many of these individuals encounter a multitude of barriers that limit their ability to access and interact with web content effectively. In some cases, the lack of proper accessibility features can make certain platforms completely unusable for individuals with specific needs, so they are excluded from the benefits and opportunities offered by the digital world. This situation not only impacts the personal and social lives of those affected but also limits their ability to participate fully in education, employment, and other essential activities. In order to address these issues, there are internationally recognized standards and guidelines that web developers and designers are expected to follow when creating online platforms. One of the most recognized and largely adopted sets of guidelines is known as the Web Content Accessibility Guidelines (WCAG) 2.1. These guidelines are developed bythe World Wide Web Consortium, and they provide a comprehensive framework aimed at making web content more accessible to a broader range of users, including individuals with disabilities. The WCAG 2.1 revolve around various principles and criteria designed to accommodate people with diverse needs such as blindness and low vision, deafness and hearing loss, limited mobility, speech disabilities, cognitive and neurological impairments, and others. By adhering to these guidelines, developers can create websites that are perceivable, operable, understandable, and robust, thus promoting a more inclusive digital environment [2]. Despite the existence of these standards, implementing effective accessibility solutions often requires the integration of advanced technologies such as ML algorithms. These algorithms play a crucial role in developing interfaces designed for specific disabilities by providing functionalities like text-to-speech (TTS) and speech recognition (SR) for those with visual impairments, speech-to-text (STT) for individuals with hearing loss, and sign language recognition (SLR) for those with speech or auditory disabilities. Additionally, other assistive technologies may include alternative navigation methods for those with limited mobility, predictive text input, and automated captioning for video content. The application of ML and AI in these areas has the potential to significantly enhance user experience (UX) by creating more intuitive, responsive, and personalized interactions for users with diverse accessibility needs. While there has been considerable progress in accessibility tools like STT, TTS, and voice recognition, several critical gaps remain, particularly in the area of SLR. Sign language recognition still lacks the precision necessary for consistent usability, especially in continuous video contexts [3]. As a result, accessibility solutions remain inconsistent, with many platforms still unable to accommodate users with specific needs fully. Through this systematic review, we aim to thoroughly explore and analyze the current state of the art in developing accessible online interfaces that incorporate ML algorithms, particularly analyzing the accuracy, precision, and word error rates of these algorithms. By examining the latest research and advancements in the field, we seek to identify gaps and limitations in existing technologies and methodologies. Our goal is to highlight areas where improvements are necessary to achieve better algorithm accuracy, enhance UX, or make previously inaccessible platforms more usable. This review will provide valuable insights for researchers, developers, and stakeholders involved in the creation of inclusive digital environments, guiding future efforts to optimize accessibility and promote digital equity.