## 2 Related Work  
It should be noted that all gesture recognition systems consist of several stages such as those mentioned in this work: data acquisition, segmentation and pre-processing, feature extraction and classification. According to the criterion of the authors, each stage uses a technique or method according to the application of the recognition system. For example, there are sign language applications using gesture recognition known as Sign Language Recognition (SLR). [PERSON] [18] presented a framework to recognize manual signs and finger spellings using Leap motion sensor. This sensor includes the associated Application Programming Interface (API) that allows the capture of the 3D position of the fingertips. Subsequently, the raw data are pre-processed with a zero-mean (z-score) based normalization by computing the standard deviation and mean, and the features extracted are the fingertip positions and three angular direction. Then, these data are classified using SVM to differentiate between manual and finger spelling gestures, and two BLSTM-NN classifiers for the recognition of manual signs and finger-spelling gestures using sequence classification and sequence-transcription based approaches. The database of this proposal is 2240 gestures with the participation of 10 subjects. As a result, the system has an accuracy of 100% by using the SVM classifier but in general terms has an accuracy of 63.57% for both types of gesture classes in real time. Another study on SLR was reported by [PERSON] [19], who developed a SLR system using a depth-sensor device (Kinect) that, when capturing 3D points of body skeleton, recognizes gestures independently to the signer's position or rotation with respect to the sensor. Its process is as follows: The signer's skeleton information is captured through Kinect and the data are processed through affine transformation. This transformation is used to cancel out the effect of signer's rotation and position while performing the gestures. Then, the features (angular features, velocity and curvature features) from the 3D segmented hands are extracted. Furthermore, gesture recognition is carried out with the HMM classifier in three modes: single-handed(64 Gaussians components and 3 HMM states), double-handed and combined setup (128 Gaussians components, 4 HMM and 5 HMM states). The latter (combined setup) presents an accuracy of 83.77% on occluded gestures. Its database consists of 2700 gestures performed by 10 different signers. As noted in the description of the previous work, a gesture recognition system has various applications according to each project. Each research proposal applies various classifiers according to the best accuracy results; the features extracted depend on the data acquisition sensor and the input variables of the classifier; and the database is customized for the training of the proposed algorithm. Much work on gesture recognition has focused on detecting the hand as well as its features such as position and size of the hand. Thus, many algorithms have been proposed to identify hand gestures with PCA. PCA is used to reduce the dimension of the data and helps the process of classification the features obtained [20]. [PERSON] [21] presented a real time system that includes the detection and tracking of the hand with non-uniform backgrounds using a skin segmentation and an algorithm of the hand contour position that implies a subtraction of the face and a base of recognition of gestures of the hand with PCA. [PERSON] [22] used the PCA to determine the characteristics of all frames in the video sequence and then these are stored in the database. Then, the gestures are classified using an artificial neural network. [PERSON] [23] proposed a sign language recognition system using the PCA algorithm to compare the features obtained from HSV color space segmentation. After comparing the features of the sign captured with the test database, the minimal Euclidean distance is calculated for the recognition of the sign.