# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 2 Related Work  
Many works have been developed about the classification and recognition of sign language through sensors and techniques based on computer vision in recent years. In the literature, there are different approaches some based on translating gloves [5; 6; 7], whose main advantage is the reduction in computational cost and consequently a real-time performance, in addition to being portable and low-cost devices, but with the disadvantage that they are invasive for the user. On the other hand, other proposals use specialized sensors and techniques of machine learning that allow the translation of sign language accurately, for example 3D data acquired from leap motion [8; 9] whose primary function is to accurately detect hands and recognize hand gestures to interact with applications. However, its use is limited to hand processing. Other works are based on the use of RGB cameras [10; 11; 12; 13; 14; 15; 16]. These approaches have been the most widely used due to their low cost and ease of acquisition, representing an advantage when implementing a system in a real setting, but they have some limitations in their practical implementation, this is due to the image processing technique used, the amount of light, the focus, and the direction of the image; which are complex factors to control and that can directly affect the results. Another approach is the use of RGB-D cameras [17; 18; 19; 20; 21] that provide 3D information to more accurately estimate the position and orientation of the object to be measured. For these reasons, they represent an attractive solution for the recognition of sign language worldwide. For example [PERSON] et al. [22] proposed a system that uses the skeleton information obtained from the Kinect to translate Turkish signs into text words. They used a Convolutional Neural Network (CNN) as the classifier. While in the United States, [PERSON] et al. [23] developed a method based on 3D CNNs to recognize American Sign Language, where they obtained an accuracy of 92.88%. In 2020, [PERSON] et al. [24] which presents a translator from Indian Sign Language to English phrases. They extracted hand features using local binary patterns and HOG. They used a support vector machine (SVM) as the classifier, achieving a recognition rate of 78.85%. While, in Pakistan, [PERSON] et al. [25]presented a translator from Indo-Pakistani Sign Language to English or Urdu/Hindi audio, using color and depth information obtained from the Kinect. On the other hand, in China, [PERSON] et al. [26] made the bidirectional translation of Chinese Sign Language, from the person with hearing problems to the listener and vice versa. They used the body keypoints provided by the Kinect, and a long short-term memory (LSTM), obtaining a recognition rate of 79.12%. This work concentrates on the use of RGB-D cameras. For this reason, a detailed description of the works related to this type of approach is presented below: [PERSON] et al. [17] developed a real-time system for recognizing the vowels and the letters L and B of the Mexican Sign Language (MSL). They used a random forest for feature extraction and a three-layer neural network for the signs' recognition, obtaining a system precision of 76.19%. [PERSON] et al. [18] built a bimodal cognitive vision system; as input data for recognition, they used 2D images followed by a preprocessing to obtain the geometric moments that, together with the 3D coordinates, are used to train a Hidden Markov Models (HMM) for signal classification. [PERSON] et al. [19] implemented a real-time system that recognized 20 words of the MSL, classified into six semantic categories: greetings, questions, family, pronouns, places, and others. Using the Kinect v1 sensor, they acquired depth images and tracked the 3D coordinates of the body's joints. They collected 700 samples of 20 words expressed by 35 people using the MSL and applied the Dynamic Time Warp (DTW) algorithm to interpret the hand gestures. [PERSON] et al. [20] proposed an alphanumeric recognition system for MSL.