## 2 Related Work
A detection net employing stacked temporal convolution then selects those video segments that aligned maximally with the predicted gloss labels. This is followed by a three-stage optimization-connectionist temporal classification (CTC) for alignments between input and target, feature learning for building more training samples and sieving out unnecessary segments. Apart from using neural networks, Sign Language images can also be classified using simple machine learning algorithms. [PERSON], [PERSON] et al. [13], for instance, extract keypoint-related features such as SIFT (scale invariance feature transform), SURF (speed up robust features), ORB (oriented fast and rotated BRIEF) from gray scale depth images and then learns a new representative feature vector obtained from K-means clustering using na SVM and a K-nearest neighbors (KNN) classifier. Testing the system on the Indian Sign Language (ISL) digits, NUS I and II datasets give maximum accuracy of 93.26%, 80.6% and 85.6%, respectively. [PERSON], [PERSON] et al. [14] train an SVM as well but with hand-segmented binary images as input and Hu moments, fingertip detection and hand trajectory as features, giving a recognition rate of 97.5% on four hand signs. IN contrast, [PERSON], [PERSON]. et al. [15] compare two variations of an SVM, namely simplification of SVM (SimpSVM) and relevance vector machine (RVM) on the Auslan dataset and the American Sign Language Image Dataset (ASLID). When sign classification is carried out using 5-fold cross validation, the SimpSVM outperformed the RVM for both datasets. [PERSON], [PERSON] et al. [16], on the other hand, perform feature vector similarity using the nearest-neighbors classification on spatial features such as centroids of the face, manual hand and non-manual hand (C1,C2,C3) and the global centroid (GC) extracted from the UoM-ISL sign language dataset with an overall recognition rate in terms of F-measure being 58.82. Last but not the least, hand gestures can also be recognized by devising features that vary for each sign. This is demonstrated in [PERSON], [PERSON] et al. [17] where the roundness of the image and the peak offset are formulated from the gray scale sign images of ASL alphabets and digits for classification purposes without the usage of any machine-learning or deep-learning algorithm. [PERSON], [PERSON] et al. [18] also make use of a feature known as edge orientation histogram (EOH) extracted from regions of interest (ROI) of gray scale images of the ASL alphabet. When classification is performed based on similarity between the feature vectors formed from EOH coefficients, it gives a recognition rate of 88.26%. Based on the majority of works performed in this problem domain, it can be observed that the sign images are always reduced to numerical features which simplifies the classification process. This also means that the extracted features need to be perfectly representative of the hand gestures and hence complex. Keeping this in mind, the proposed system attempts to surpass the accuracy of the following literature in Table 1. To highlight the effectiveness of numerical features over a technique devoid of feature extraction, the papers that use a CNN are taken for comparison in Table 1. In addition, other state-of-the-art techniques that utilize numerical features also need to be compared. For this reason, a paper using keypoints is also taken for comparison in Table 1: