# A Systematic Review of Accessibility Techniques for Online Platforms: Current Trends and Challenges
## 3 Results of the Systematic Review
### Advanced Solutions on Sign Language Recognition  
SLR is a newer and less mature field compared to other recognition technologies, often limited by expensive datasets and language variations [22]. Current applications are mainly in experimental settings, but some real-world implementations are emerging, like translating sign language for customer service desks (e.g., SignAll's software). Challenges include capturing nuanced hand and facial expressions, high computational requirements, and adapting to different sign languages, which limit SLR's broader adoption and user satisfaction. Recent advancements in SLR have demonstrated impressive accuracy levels across various models and approaches. For instance, a model trained on 54,049 images of Arabic sign language achieved a precision rate of 95% [23]. In another study, the SAM-SLR framework integrated multiple modalities and reported recognition rates of 98.42% for RGB images and 98.53% for RGBD [24]. Additionally, a hierarchical model for ISL gestures reached 98.52% accuracy for one-hand gestures [25], while a wearable SLR device using electromyography and inertial sensors achieved an average accuracy of 96.13% [26]. These achievements highlight the potential of advanced techniques in enhancing the reliability of SLR systems. [PERSON], [PERSON] et al. trained a model using 54,049 images with Arabic sign language. The model had a precision rate of 95% [23]. Also, [PERSON], [PERSON] et al. conducted a survey that reviews recent vision-based sign language recognition models utilizing deep learning, highlighting accuracy improvements and ongoing challenges. It categorizes models for isolated and continuous recognition, discussing applications, datasets, hybrid approaches, and future research directions [27]. [PERSON], [PERSON] et al. propose a novel multi-modal framework, SAM-SLR, for SLR that integrates skeleton-based, RGB, and depth modalities to enhance recognition accuracy. The model achieved a good performance, with recognition rates of 98.42% for RGB and 98.53% for RGBD, making it the top performer in the 2021 SLR Challenge [24]. [PERSON], [PERSON] et al. developed an AI-enabled sign language recognition system combining sensing gloves, deep learning, and a virtual reality interface. Their model recognized 50 words and 20 sentences, achieving an 86.67% accuracy for new sentence constructions by recombining word elements [28]. [PERSON], [PERSON] et al. present a transformer-based model that jointly learns sign language recognition and translation, achieving state-of-the-art results on the PHOENIX14T dataset. Their approach significantly improves translation performance, with BLEU-4 scores rising from 9.58 to 21.80, and sets new baselines for sign language translation tasks [29]. [PERSON], [PERSON] et al. present a deep-learning-based convolutional neural network (CNN) model for Indian sign language (ISL) recognition, using 35,000 images of 100 static signs. The system, evaluated on 50 CNN models, achieves a top training accuracy of 99.72% for colored images and 99.90% for grayscale images [30]. [PERSON], [PERSON] et al. introduce the large-scale Word-Level American Sign Language (ASL) dataset, featuring over 2000 words performed by more than 100 signers, the largest public ASL dataset available. The authors evaluate two deep learning models for word-level sign recognition: a holistic visual appearance-based approach and a 2D human pose-based approach, achieving a top-10 result of up to 62.63% accuracy [31]. A robust analysis of gesture recognition techniques was performed by [PERSON], [PERSON] et al. for ISL using three models: fine-tuned VGG16, transfer learning VGG16, and a hierarchical neural network. The hierarchical model achieved the best accuracy, with 98.52% for one-hand gestures and 97% for two-hand gestures [25]. [PERSON], [PERSON] et al. present a wearable low-cost SLR device using surface electromyography and inertial measurement unit sensors to enhance accuracy.