You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A Review on Indian Sign Language Recognition 
Chapter Title:  International Journal of Computer Applications (0975 -8887) Volume 73-No.22, July 2013 
1. Abstract of Cited Paper (BIB001):  Sign language helps the deaf and mute to communicate effectively. The paper demonstrates the evaluation of various feature extraction techniques for the dual -handed sign language alphabets. The efficiency of features like Histogram of Orientation Gradient (HOG) is discussed followed by the demonstration of the Histogram of Edge Frequency (HOEF) which overcomes the short coming of HOG. The evaluation of HOG accuracy is found to be 71.4% whereas with HOEF it is found to be 98.1%. The paper also demonstrate the overall system for the sign language recognition 
2. Abstract of Cited Paper (BIB002):  We proposed a system to automatically recognize gestures of sign language from a video stream of the signer. The developed system converts words and sentences of Indian sign language into voice and text in English. We have used the power of image processing techniques and artificial intelligence techniques to achieve the objective. To accomplish the task we used powerful image processing techniques such as frame differencing based tracking, edge detection, wavelet transform, image fusion techniques to segment shapes in our videos. It also uses Elliptical Fourier descriptors for shape feature extraction and principal component analysis for feature set optimization and reduction. Database of extracted features are compared with input video of the signer using a trained fuzzy inference system. The proposed system converts gestures into a text and voice message with 91 percent accuracy. The training and testing of the system is done using gestures from Indian Sign Language (INSL). Around 80 gestures from 10 different signers are used. The entire system was developed in a user friendly environment by creating a graphical user interface in MATLAB. The system is robust and can be trained for new gestures using GUI.

Literature Review:
mapping their inputs into high-dimensional feature spaces. The different approaches for solving multi-class problems with SVM are one against all, one against one, decision directed acyclic graphic approach etc Figure 2 shows the signs in ISL corresponding to the English alphabets A, B, C, D and E. ISL recognition is comparatively new in the field of sign language recognition. A practical framework for ISL gesture based human robot interaction had been proposed in BIB001 . ISL video had been used to compose several hand gestures to train the HOAP-2 robot in real time. Orientation histogram feature was extracted for real time classification using Euclidean distance metric. ISL video had been captured by selecting several dynamic gestures (i.e. sequence of frames) in real time using webcam. The classifier was trained using twenty ISL gestures. The work was carried out in WEBOTS platform using real time JAVA based software (developed by them). The classified ISL gestures with average accuracy of 90% were entirely mapped with specific gesture based applications on humanoid robot. In BIB002 , the authors took their dataset for dual-hand gesture from the 26 alphabets of ISL. The dataset consisted a total of 2340 (30×26× 3) images, 3 signers were requested to sign all 26 alphabets 30 times. Features were extracted from the images using Histogram of Orientation Gradient (HOG) and Histogram of Edge Frequency (HOEF). Finally, gestures were classified using Support Vector Machine (SVM). They had proposed a novel HOEF feature, using which they achieved a recognition rate of 98.1% of dual-handed gesture, and they have claimed in their paper that HOEF is better than HOG which is widely used by many for recognition. A system had been proposed BIB003 by P.V.V. Kishore and P. Rajesh Kumar to automatically recognize gestures of ISL from a video stream of the signer. Their system converts words and sentences of ISL into voice and text in English. To accomplish the task, they had used powerful image processing techniques such as frame differencing based tracking, edge detection, wavelet transform and image fusion techniques to segment shapes in the videos. Then Elliptical Fourier descriptors were used for shape feature extraction and PCA for feature set optimization and reduction. Database of extracted features were compared with input video of the signer using a trained fuzzy inference system. The proposed system converts gestures into text and voice message with 91% accuracy. Around 80 gestures from 10 different signers were used. The entire system was developed in a user friendly environment by creating a Graphical User Interface (GUI) in MATLAB.