Sign Language Recognition (SLR) system is a method which allow deaf people to communicate with society. In this study, Real-Time Sign Language recognition system was proposed by using the surface Electromyography (sEMG). To this purpose, sEMG data acquired from subject right forearm for all twenty six American Sign Language gestures. Raw sEMG data was filtered, feature extracted and fed into classification. Support Vector Machine (SVM) with one vs. all approach was used for multi class classification. The experiment result of offline system is reaching a recognition rate of 91.% accuracy and real-time system has a recognition rate of 82.3% accuracy. The results of the proposed system shows that sEMG signal can be used for Real-Time SLR systems.

Real-Time American Sign Language Recognition System Using Surface EMG Signal

Sign Language Recognition (SLR) system is a novel method that allows hard of hearing people to communicate with society. In this study, an American Sign Language (ASL) recognition system was proposed by using the surface Electromyography (sEMG). The objective of this study is to recognize the American Sign Language alphabet letters and allow users to spell words and sentences. For this purpose, sEMG signals are acquired from subject's right forearm for 27 American Sign Language gestures, 26 English alphabet letters, and one for home position. Time domain, frequency domain (band power), power spectral density (band power), and average power features were used as the feature extraction methods. After feature extraction, Principal Component Analysis (PCA) was applied to obtain uncorrelated features. As a classification method, Support Vector Machine and Ensemble Learning algorithm were used and their performances were compared with tabulated results. In conclusion, the results of this study show that sEMG signal can be used for SLR systems.

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American Sign Language Recognition system by using surface EMG signal

Sign language is most important tool of communication for dumb and deaf people. Sign language is a widely used and accepted standard for communication by people with hearing and speaking impairments. A sign language is a language which uses visually transmitted sign patterns, instead of acoustically conveyed sound patterns, to deliver the meaning. In this research paper a novel technique for Indian sign language recognition is proposed. Technique focuses on transformed content of sign language images. Important goal of proposed classification based recognition technique is faster retrieval with higher accuracy.

Noval approach of classification based Indian sign language recognition using transform features

Humans with hearing disabilities are highly dependent on non-verbal forms of communication involving hand gestures. A gesture recognition system capable of transforming gestures to other forms of communication can bring about a significant improvement in the quality of life of such human beings. A novel approach making use of Fourier Descriptors for shape extraction followed by Dynamic Time Warping for sequence matching in order to recognize gestures representing English alphabets present in the Indian Sign Language (ISL) has been proposed in this paper. A feature vector constituting the top 220 coefficients of the Fourier Descriptor is calculated for all images present in the training and test dataset. The test sequences are matched with the training sequences using Dynamic time Warping algorithm in order to calculate the similarity between the test image and all the images of the training dataset. The results are stored in the increasing order of the cost between the two sequences which is a measure of similarity between the two sequences. K-nearest neighbor algorithm is then applied to a cost vector containing 15 gestures having the minimum cost with the test image to recognize the most dominant gesture. A data set constituting of 338 images is used for training the algorithm for classifying 26 different characters. The algorithm was found to have an accuracy of 96.15 % when tested on a dataset of 78 images.

http://ieeexplore.ieee.org/iel7/7403799/7414696/07414750.pdf

A DTW and Fourier Descriptor based approach for Indian Sign Language recognition

The development of a sign language recognition system can have a great impact on the daily lives of humans with hearing disabilities. Recognizing gestures from the Indian Sign Language (ISL) with a camera can be difficult due to complexity of various gestures. The motivation behind the paper is to develop an approach to successfully classify gestures in the ISL under ambiguous conditions from static images. A novel approach involving the decomposition of gestures into single handed or double handed gesture has been presented in this paper. Classifying gesture into these subcategories simplifies the process of gesture recognition in the ISL due to presence of lesser number of gestures in each subcategory. Various approaches making use of Histogram of Gradients (HOG) features and geometric descriptors using KNN and SVM classifiers were tried on a dataset consisting of images of all 26 English alphabets present in the ISL under variable background. HOG features when classified with Support Vector Machine were found to be the most efficient approach resulting in an accuracy of 94.23%.

Indian Sign Language gesture classification as single or double handed gestures

Sign language is a widely used and accepted standard for communication by people with hearing and speaking impairments. A sign language is a language which uses visually transmitted sign patterns, instead of acoustically conveyed sound patterns, to deliver the meaning. Several ways to generalize the American sign language recognition have been proposed in the past [14] [15]. In this paper, novel method of American sign language recognition with Shape and Texture features has been proposed. Many of the existing systems require the person gesticulating to use special data acquisition devices like data gloves which are expensive and difficult to handle. We proposes a more flexible vision based approach where the person is free from additional equipment. The Shape features and Texture feature are more unique, so a novel technique based on combination of these is derived and proposed here. For extracting shape features standard gradient operator such as Robert, Prewitt, Sobel, Canny, Freichein, Kirsh and Laplace are used and for texture feature vector quantization techniques are used. The gradient mask images of the character images are obtained and then LBG vector quantization algorithm is applied on these gradient images to get the codebooks of various sizes. These obtained LBG codebooks are considered as shape texture feature vectors for American sign language recognition. The database includes 26 for American sign language alphabets taken by 12 different people. The images are saved in a jpeg file format and stored in separate folder. Thus there are total 312 images were use for our project and 8 code book sizes (from 4 to 512). The nearest neighbour (KNN) algorithm is considered as performance comparison criteria for proposed character recognition techniques. The best performance is observed in LBG for codebook size 8 of Canny operator and the next best is seen for codebook sizes 4 of freichein gradient mask for feature extraction. The LBG VQ [11] design algorithm is an iterative algorithm which requires an initial codebook C. This initial codebook is obtained by the splitting method. In this method, an initial code vector is set as the average of the entire training sequence. This code vector is then split into two. The iterative algorithm is run with these two vectors as the initial codebook. The final two code vectors are splitted into four and the process is repeated until the desired number of code vectors is obtained.

Krupali Suresh Raut

Papers

Recognition of American sign language using LBG vector quantization