# 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
Data from ten volunteers performing 20 Persian sign language signs were collected, with 25 key features extracted and classified using a KNN classifier, achieving an average accuracy of 96.13% [26]. In the same context, [PERSON], [PERSON] et al. propose a system that recognizes ISL and converts it to speech and text in English and Malayalam, displaying the output on an Android device, with an accuracy of approximately 90% [32]. [PERSON], [PERSON] et al. address the challenge of recognizing ISL gestures using convolutional neural networks. A dataset was created featuring five subjects performing 200 signs from different angles and backgrounds, with each sign captured in 60 frames. The study involved training CNNs with various sample sizes and architectures, achieving a recognition rate of 92.88%, outperforming other classifiers on the same dataset. The proposed method enables independent use of a sign language recognition mobile application by hearing impaired individuals [33]. [PERSON], [PERSON] et al. propose LS-HAN, a framework for continuous sign language recognition that eliminates temporal segmentation. It uses a two-stream CNN and hierarchical attention network, achieving up to 82.7% accuracy [34]. [PERSON], [PERSON] et al. introduce an alignment network with iterative optimization for weakly supervised continuous sign language recognition. It combines a 3D ResNet for feature learning and an encoder-decoder network with CTC for sequence modeling, optimized iteratively. Evaluated on large datasets, the method achieved a precision of 93.9% [35]. [PERSON], [PERSON] et al. propose a modified LSTM model for continuous SLR, capable of recognizing sequences of connected gestures by splitting them into sub-units. Tested on 942 ISL sentences using 35 sign words, the system achieved an average accuracy of 72.3% for signed sentences and 89.5% for isolated words [3]. [PERSON], [PERSON] et al. emphasize the need for interdisciplinary collaboration to develop sign language recognition, generation, and translation systems. It summarizes a workshop with experts from various fields, reviewing the state of the art, identifying key challenges, and calling for more integrated research efforts in the community [22]. [PERSON], [PERSON] et al. introduce a system for recognizing ASL gestures using computer vision techniques. It captures hand gestures via a webcam, processes the images using the HSV color algorithm, and uses a CNN for classification. The model successfully recognizes 10 ASL alphabets with above 90% accuracy [36]. [PERSON], [PERSON] et al. developed an ASL recognition dataset and a CNN model to interpret ASL gestures into natural language. The dataset accounts for various conditions, marking a significant advancement in sign language recognition. The CNN achieved 99.38% accuracy with a low loss of 0.0250, outperforming two existing datasets. These results suggest that the system could be a promising solution for medical applications using deep learning while enhancing contributions to the SLR field [37]. [PERSON], [PERSON] et al. created a vision-based application that translates sign language into text, enhancing communication between signers and non-signers. It processes video sequences to extract features, using an inception CNN for spatial recognition and an RNN for temporal training, based on the ASL dataset. The accuracy using a Softmax layer was around 90% [38]. However, continuous sign language recognition from videos presents more significant challenges, often resulting in lower accuracy rates. While static recognition benefits from clear and defined gestures, dynamic recognition must contend with variations in speed, context, and environmental factors, which can limit performance. For instance, despite advancements, the overall effectiveness of recognizing continuous signing from videos has not reached the same levels of accuracy seen with individual images. Addressing these challenges will be crucial for improving real-time, continuous sign language recognition systems.