# Sign Language Interpreting System Using Recursive Neural Networks
## 2 Related Works
Another interesting work is [21], in this work, the authors explored a new lexical video dataset of MSL, which includes the most frequently used dynamic gestures. Each gesture class was composed of different versions of videos recorded under uncontrolled conditions. This dataset, named MX-ITESO-100, contains a lexicon of 100 gestures and 5000 videos made by three participants, showing various grammatical elements. Furthermore, they evaluated the dataset using a two-step neural network model, reaching an accuracy of 99%, making it a useful benchmark for training future machine learning models in computer vision systems. Another work related to project we propose is [22]. The authors designed a bidirectional sign language translation system to bridge the communication gap between hearing and deaf people. They evaluated Several deep learning models, including RNN, bidirectional RNN (BRNN), LSTM, GRU, and Transformers to identify the most accurate sign language recognition and translation model. Also, they used a Keypoint (the spatial locations of important landmarks in the image) detection approach through MediaPipe to track and understand gestures. The system incorporates an intuitive graphical interface,which allows translation between MSL and Spanish in both directions, facilitating the entry of signs or text and obtaining the corresponding translations. The evaluation results demonstrated high accuracy, with the BRNN model standing out with an accuracy of 98.8%. The authors underlined the importance of hand characteristics in sign language recognition and offered a promising solution to improve communicative accessibility and promote the inclusion of people with hearing disabilities. In [23], the authors presented a model for sign language that combines spatial and temporal attention with a general neural network. The model they designed uses self-attention mechanisms to extract relevant information from node and edge features in both spatial and temporal domains. The architecture of their system is divided into three branches: a graph-based spatial branch that identifies spatial dependencies, a graph-based temporal branch the temporal branch that reinforces temporal dependencies in sequential hand skeleton data, and a general neural network branch that improves the generalization capability of the model increasing its robustness. They evaluated the system performance on the MSL, the Pakistani Sign Language (PSL) datasets, and the American Sign Language Large Video Dataset (ASLLVD), including 3D joint coordinates for face, body, and hands; the authors conducted experiments on both individual gestures and their combinations. The model achieved a 99.96% accuracy on the MSL dataset, 92.00% on PSL, and 26.00% on the ASLLVD dataset, covering over 2700 classes. On the other hand, the research presented in [24] focused on identifying lexical signs. This study proposed a method to identify phonetic units in MSL videos using an unsupervised model, to achieve this, they used an image thresholding approach based on the Motion-Retention Model of [PERSON]. The goal was to identify the smallest linguistic units containing relevant information, avoiding the loss of sublexical data that might go unnoticed by most natural language processing (NLP) algorithms. Furthermore, the method allows for removing noisy or redundant video frames, thus reducing computational costs. The authors tested their algorithm tested on a collection of MSL videos, and human judges assessed the relevance of the extracted segments. The results indicated that the frames selected by the algorithm contained enough information to be comprehensible for signers. In some cases, up to 80% of available frames could be discarded without loss of comprehensibility, which has direct implications for future electronic representation, transmission, and processing of sign language. The authors of [25] introduced an interactive platform for teaching MSL based on artificial intelligence (AI) models, which they called DAKTILOS.