# Sign Language Interpreting System Using Recursive Neural Networks
## 2 Related Works
The researchers utilized the MediaPipe library, a hybrid CNN, and a bidirectional long short-term memory (BLSTM) model for pose detail extraction and text generation. They utilized a hybrid machine translations neural network combined with MediaPipe and a dynamic generative adversarial network (GAN) to generate sign language gestures. The proposed model achieved over 95% classification accuracy and significantly improved visual quality. The dynamic GAN was evaluated using different multilingual datasets, and excellent results were produced. In contrast, [PERSON]. [15] presented a sign language recognition system called SignGest that uses acoustic signals from a smartphone to capture sign language gestures and a CNN model to distinguish between different gestures. In addition, it used a deep convolutional GAN to generate training data. They implemented the system on a server and an Androidsmartphone without additional hardware. The experimental results showed that SignGest achieved its target performance which they assessed using the Structural Similarity Index Measure (SSIM). Additionally, in [16], a method for recognizing words in Japanese Sign Language (JSL) based on the abstraction of hand signals was implemented; this was achieved using measuring the hand shape, position, and movement. The proposed system uses a contour-based method and template matching for hand shape recognition. They also explored a second method based on the Hidden Markov Model. The system was evaluated using a dataset of 400 JSL words, and results showed a recognition rate of 33.8%. In turn, Ref. [17] introduced an interactive Taiwanese sign language learning system based on Microsoft Kinect. They processed the color image, depth image, and skeleton points of the hands and body to extract relevant information. Then, they fitted an supporting vector machine (SVM) classification to identify the intended sign language word, and the experimental results showed a recognition rate of 90.2% and hand motion detection of 98%. In the paper [18], the authors presented research focused on the automatic recognition of Mexican Sign Language (MSL) by analyzing body movement and facial gestures. The proposed system employs multiple gestures, including hands, body, and face, using a dataset of 3000 samples of 30 different MSL signs. They used a depth camera to capture the coordinates of the movements while utilizing RNN for classification. The results of their study demonstrated a high accuracy of the system, reaching 97% on clean test data and 90% on data with a high level of noise, highlighting the robustness and effectiveness of the proposed approach. Another relevant work in MSL recognition is [19], the authors presented a system developed to recognize static signs of the dactylological alphabet. They utilized the MediaPipe framework and a CNN model to interpret the letters representing the hand signals captured by a camera. A database composed of 336 test images was used, their system achieved an accuracy of 83.63% with the CNN model. In addition, they evaluated samples of each letter and achieved an accuracy of 84.57%, a sensitivity of 83.33%, and a specificity of 99.17%. The advantage of their proposal lies in its implementation capacity on low-consumption equipment, allowing real-time classification and improving the accessibility and usability of the system. The authors of [20] proposed a method for the recognition of the static alphabet of the MSL capable of learning the shape of each of the 21 letters from examples. The method they proposed first normalizes each example of the training set into a 3D pose through utilizing the principal component analysis (PCA). The input data was captured using a 3D sensor, and the method generates three types of features representing each shape. The authors used a set of data acquired in a laboratory, their approach achieved 100% accuracy. In addition, the features used in this method have a clear and intuitive geometric interpretation.