# Sign Language Interpreting System Using Recursive Neural Networks
## 2 Related Works  
The research detailed in [7] focused on developing a sign language detection system to facilitate communication between the deaf community and banks. The main difficulty lies in the complexity of Indian Sign Language (ISL) gestures related to the banking domain. The lack of standardized datasets and the variability in gesture length were significant challenges. To address this, the proposed approach combines feature extraction using a Convolutional Neural Network (CNN) with an LSTM Recurrent Neural Network. This system was trained and tested on a customized ISL dataset. On the other hand, ref. [8] focused on developing an American Sign Language (ASL) recognition system based on computer vision and deep learning. It also presents a publicly available motion-based ASL dataset for future research which contains thirty phrases obtained from the book American Sign Language for Dummies along with phrases considered basic for communication. They consider three types of algorithms: ConvLSTM, Convolutional Neural Network (CNN) and LSTM, and Mediapipe and LSTM, and compared them, with two main parameters to observe: the number of sentences in the model and the precision of the sentences in each one. They used the the MediaPipe (an open source framework by Google useful for tasks such as face detection, hand tracking, and more)  
and LSTM model. They concluded with 97% accuracy in training and 79% accuracy within the tests carried out. Finally, they propose to use the Transformer algorithm to improve the performance of the system for future work. In the field of video interpretation, the authors of [9] implemented a methodology to evaluate expressiveness in facial videos captured of people responding to ads based on deep learning; specifically, they used an LSTM network. They detailed the structure of the LSTM model which includes a dropout layer which turns off neurons randomly selected to avoid overfitting the NN. The model was trained to determine expressiveness based on facial cues, and user interfaces were provided through a website with a camera connected to an IoT board to display the results. In turn, Ref. [10] proposed a sign language-to-text translation system using deep learning models, specifically CNN and LSTM. The system was trained on an enriched dataset that contains common phrases used in professional meetings. The results show that the LSTM model outperforms the CNN model. The authors of [11] proposed a model for video recognition using computer vision techniques for Chinese sign language which achieves an accuracy of 85%. They designed a model based on an LSTM neural network to capture skeletal tracking information. They ended the paper by suggesting further improvements to their model's accuracy by retraining using an expanded dataset and incorporating facial expression recognition. Also, in the research detailed in [12], a system focused on translating MSL using RGB-D information was introduced. The authors built a semantic corpus of 53 words belonging to Mexican Sign Language (MSL) from scratch. From this corpus, they captured the trajectory of hand movements using a Kinect sensor; then, a KNN-based algorithm eliminated inconsistencies and noise in the extracted patterns. They used a Multilayer Perceptron Neural Network for word classification and implemented the Backpropagation algorithm for training. Finally, they validated their system using the K-Fold Cross Validation method, obtaining an average accuracy percentage of 93.46%. [PERSON] [13] created a database containing Colombian Sign Language (CSL) signs and used it to train a CNN to recognize CSL gestures. They found several activation functions failed to respond adequately; however, Dropout improved the model's accuracy and loss. Additionally, they observed that the number of layers is unrelated to the model's accuracy. [PERSON] [14] proposed deep learning-based techniques to enhance sign language video recognition, translation, and generation.