## 2 Related Work  
First and foremost, for an image classification task such as sign gesture recognition, a standard approach in practice is to apply a convolutional neural network (CNN) to segregate the distinct image classes. For instance, [PERSON], [PERSON] et al. [6] feed RGB images of the Cambridge hand gesture dataset directly to a CNN comprising a convolutional layer of 16 filters of size 4 \(\times\) 4 followed by a rectified linear unit (ReLU) activation, cross-channel normalization and max pooling. The pooled output when passed through a fully-connected layer equipped with softmax classification gives an accuracy of 96.66% when trained using stochastic gradient descent for 100 epochs. Similarly, [PERSON], [PERSON] et al. [7] also use a CNN but it feeds the RGB images in gray scale in which the white hand is retained against the black background. Compared to [PERSON], in [PERSON] et al. [6], the convolutional layers comprise 32 filters of size 3 \(\times\) 3. When trained on 10 American Sign Language (ASL) alphabets--A, B, C, D, H, K, N, O, T, Y--it achieves an overall accuracy of 98%. On the other hand, [PERSON], [PERSON] et al. [8] utilize depth information of RGB images from the ASL finger-spelling dataset for hand segregation. These depth images are fed to a special type of CNN known as the principal component analysis network (PCANet) for feature extraction rather than classification. It consists of two convolutional layers. The first one contains L1 filters to learn low-level features, and the second one contains L2 filters to learn high-level features. When these features are passed through a support vector machine (SVM) classifier, the leave-one-out accuracies thus obtained are 88.70% and 84.50%, respectively, for the single PCANet model and the user-specific PCANet model. Secondly, instead of a CNN, artificial neural networks (ANNs) are also used for classifying hand gestures. However, to train them, numerical features are required. [PERSON], [PERSON] et al. [9], for example, make use of seven annotated upper body joint locations as numerical inputs to two types of ANNs with transfer learning weights from pose estimation. The first one uses the context of body joints by solving a regression equation to increase accuracy via a cascade of pose regressors, while the second method utilizes heatmap regression for pose estimation. [PERSON], [PERSON] et al. [10] also employ an ANN, but it needs to extract numerical features before it can be passed as input. The features extracted include an orientation histogram (OH); six statistical parameters (ST), namely mean, standard deviation, variance, coefficient of variation, range and root mean square of successive difference; a combination of the previous two (COHST); and a 2D discrete wavelet transform (DWT) using a Haar wavelet. When the ANN is trained on the ASL digits, COHST gives an accuracy of 87.94%,outperforming its individual components, and the Wavelet features acquire an accuracy of 98.17%. The former two types of neural networks deal with sign images, but to handle sign language videos that consist of sequential images, a recurrent neural network (RNN) such as long short-term memory (LSTM) is necessary. One example is the hierarchical attention network with latent space (LS-HAN), as described by [PERSON], [PERSON] et al. [11], which comprises three components: a two-stream CNN which maps one-hot encoded words in a sentence to semantic space along with video features, a latent space (LS) that uses the dynamic time warping (DTW) algorithm for semantic bridging, and a HAN that computes log-probability of the sentences using bidirectional LSTM equipped with a softmax function. Another example that elaborates the usage of LSTM for classifying sign videos is in [PERSON], [PERSON] et al. [12] which maps sign video sequences to gloss labels with the assistance of bidirectional LSTM by representing the input as a spatio-temporal feature via a recurrent neural network.