# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 2 Research Methodology
### Developing the Research Strategy  
In our study, we put together a solid collection of research papers on our topic to obtain a well-rounded view and an in-depth understanding of the latest progress and advancements in accessibility technologies. The articles we collected were published between 2018 and 2024, representing the most recent developments in this field. To ensure a wider scope, we used papers from multiple reputable databases such as Google Scholar, IEEE Xplore, Elsevier, and Springer. Our search strategy involved using various combinations of keywords and key phrases, such as \"Sign Language Recognition\", \"Developing Accessible Interfaces\", \"Speech Recognition\", \"Speech to Text Interfaces\", and \"Text to Speech Interfaces\". This extensive search initially returned 2819 articles.  
We applied the PRISMA and PEDro methodologies to rigorously filter the large volume of studies identified in our initial search. PRISMA provided a structured approach for organizing and documenting the selection process. By detailing each step of the study selection process, PRISMA allowed us to systematically narrow down the results to the studies that were most relevant to our focus on online accessibility technologies. Meanwhile, the PEDro scale was essential for assessing the methodological quality of these studies.  
Given the large number of articles received, we implemented a multi-stage research strategy that involved both quantitative and qualitative filtering to identify the most relevant and high-quality studies. One of the first challenges we encountered in our filtering process was the large number of duplicate articles. In order to solve this, we had to aggregate all the results from the searches through an Excel file and use Microsoft Excel features to identify the duplicate results and remove them. After this step, the number of articles was reduced by 446 articles, leaving us with 2373 unique papers.  
Following this, we applied a quantitative filter based on specific paper characteristics. We required each article to have been cited at least ten times, indicating a certain level of recognition and impact within the community. After applying these criteria, we reduced the pool to 2285 articles.  
We conducted a more targeted filtering process by reviewing the titles and abstracts of the remaining articles. During this phase, we filtered the title and abstract with additional keywords, including \"Accessible Interface\", \"Speech to text\", \"Text to speech\", \"accessibility\", \"Speech recognition\", and \"sign language recognition\". This step allowed us to exclude 2176 articles that did not align closely with our main research focus. After this extensive screening, 109 articles appeared directly relevant to our study.  
From these 109 articles, we applied a qualitative filtering based on a set of questions that will be described in the following subsection. After this step, 35 articles remained. Figure 1 [4] shows how the research strategy steps were applied to the results. On the other hand, in Table 1 are the results from the research databases based on the queries used for the search.  
\begin{table}
\begin{tabular}{c c c c c c} \hline
**Query** & **Elsevier** & **IEEE** & **Springer** & **Google Scholar** & **Total** \\ \hline Developing accessible interfaces & 49 & 37 & 80 & 332 & 497 \\ Text to speech interfaces & 46 & 174 & 122 & 151 & 493 \\ Sign language recognition & 137 & 124 & 123 & 436 & 820 \\ Speech to text interfaces & 37 & 176 & 120 & 158 & 491 \\ Speech recognition & 142 & 149 & 91 & 136 & 518 \\ \hline
**Total** & 411 & 659 & 536 & 1213 & 2819 \\ \hline \end{tabular}
\end{table}
Table 1: Number of results returned by queries from each database.