# Automatic Recognition of Mexican Sign Language Using a Depth Camera and Recurrent Neural Networks
## 2 Related Work
They created a database with ten alphanumeric categories (five letters: A, B, C, D, E; and five numbers: 1, 2, 3, 4, and 5), each with 100 samples; 80% of the data was used for training and 20% for testing. They used morphological techniques for the extraction of 3D Haar-type features from the depth images. The signs' classification was done using the AdaBoost algorithm, obtaining an efficiency of 95%. [PERSON] et al. [21] developed a system to classify static signs using an RGB-D camera in Java. The software captures 22 points of the hand in 3D coordinates and stores them in CSV files for training a classifier consisting of a multilayer perceptron neural network. The results obtained with the network display an accuracy of 80.1%. [PERSON] et al. [27] introduced a system that recognizes 53 words corresponding to eleven semantic fields of the MSL from the 3D trajectory of the hand's motion using a Kinect sensor. For the classification of the words, they used a multilayer perceptron. They achieved an average accuracy of 93.46%. [PERSON] et al. [28] made a system to recognize part of the MSL static-alphabet from 3D data acquired from a leap motion and a Kinect sensor. The features extracted from the data are composed of six 3D affine moment invariants. The precision obtained in the experiments with the leap motion sensor dataset and linear discriminant analysis was 94%, and the precision obtained using data from the Kinect sensor was 95.6%. Table 1 summarizes the articles found referring to the use of cameras RGB-D to recognize MSL. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Author, Reference and Year** & **Acquisition Mode** & **One/Two Hands** & **Static/Dynamic** & **Type of Sing** & \begin{tabular}{c} **Preprocessing** \\ **Technique** \\ \end{tabular} & **Classifier** & \begin{tabular}{c} **Recognition Rate** \\ **(Accuracy)** \\ \end{tabular} \\ \hline [PERSON] et al. [17] (2015) & Kinect & Both & Static & Letters & \begin{tabular}{c} Feature extraction: \\ Random Forest \\ \end{tabular} & Neural networks & 76.1\% \\ \hline [PERSON] [18] (2017) & Kinect & Both & Dynamic & Words and phrases & \begin{tabular}{c} Color filter, \\ binarization contour \\ extraction \\ \end{tabular} & \begin{tabular}{c} Hidden Markov \\ Model (HMM) \\ \end{tabular} & \begin{tabular}{c} Specificity: 80\% \\ Sensitivity: 80\% \\ Sensitivity: 80\% \\ \end{tabular} \\ \hline [PERSON] et al. [19] & Kinect & Both & Dynamic & Words & & \begin{tabular}{c} Dynamic Time \\ Warping (DTW) \\ \end{tabular} & 98.5\% \\ \hline [PERSON] et al. [20] (2016) & Kinect & One hand & Static & Letters and numbers & \begin{tabular}{c} 3D Haar feature \\ extraction \\ \end{tabular} & \begin{tabular}{c} Adaboost \\ \end{tabular} & 95\% \\ \hline [PERSON] et al. [21] & Intel RealSense E200 & One hand & Static & Letters and words & 3D hand coordinates & Neural Networks & 80.11\% \\ \hline [PERSON] et al. [27] & Kinect & Both & Both & Words and phrases & \begin{tabular}{c} 3D motion path \\ K-Nearest Neighbors \\ \end{tabular} & Neural Networks & 93.46\% \\ \hline [PERSON] et al. [25] (2021) & \begin{tabular}{c} Leap motion and \\ Kinect \\ \end{tabular} & One hand & Static & Letters & \
\begin{tabular}{c} 3D affine moment \\ invariants \\ \end{tabular} &
\begin{tabular}{c} Linear Discriminant \\ Analysis, Support \\ Vector Machine, \\ Naive Bayes \\ \end{tabular} & 94\% (leap motion) & 95.6\% (Kinect) \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of systems using RGB-D cameras for MSL recognition.