Review on Classification Methods used in Image based Sign Language Recognition System

Sign language is the way of communication among the Deaf-Dumb people by expressing signs. This paper is present review on Sign language Recognition system that aims to provide communication way for Deaf and Dumb pople. This paper describes review of Image based sign language recognition system. Signs are in the form of hand gestures and these gestures are identified from images as well as videos. Gestures are identified and classified according to features of Gesture image. Features are like shape, rotation, angle, pixels, hand movement etc. Features are finding by various Features Extraction methods and classified by various machine learning methods. Main pupose of this paper is to review on classification methods of similar systems used in Image based hand gesture recognition . This paper also describe comarison of various system on the base of classification methods and accuracy rate

Efficient Kinect Sensor-based Kurdish Sign Language Recognition Using Echo System Network

Sign language assists in building communication and bridging gaps in understanding. Automatic sign language recognition (ASLR) is a field that has recently been studied for various sign languages. However, Kurdish sign language (KuSL) is relatively new and therefore researches and designed datasets on it are limited. This paper has proposed a model to translate KuSL into text and has designed a dataset using Kinect V2 sensor. The computation complexity of feature extraction and classification steps, which are serious problems for ASLR, has been investigated in this paper. The paper proposed a feature engineering approach on the skeleton position alone to provide a better representation of the features and avoid the use of all of the image information. In addition, the paper proposed model makes use of recurrent neural networks (RNNs)-based models. Training RNNs is inherently difficult, and consequently, motivates to investigate alternatives. Besides the trainable long short-term memory (LSTM), this study has proposed the untrained low complexity echo system network (ESN) classifier. The accuracy of both LSTM and ESN indicates they can outperform those in state-of-the-art studies. In addition, ESN which has not been proposed thus far for ASLT exhibits comparable accuracy to the LSTM with a significantly lower training time

Machine learning methods for sign language recognition: a critical review and analysis.

Sign language is an essential tool to bridge the communication gap between normal and hearing-impaired people. However, the diversity of over 7000 present-day sign languages with variability in motion position, hand shape, and position of body parts making automatic sign language recognition (ASLR) a complex system. In order to overcome such complexity, researchers are investigating better ways of developing ASLR systems to seek intelligent solutions and have demonstrated remarkable success. This paper aims to analyse the research published on intelligent systems in sign language recognition over the past two decades. A total of 649 publications related to decision support and intelligent systems on sign language recognition (SLR) are extracted from the Scopus database and analysed. The extracted publications are analysed using bibliometric VOSViewer software to (1) obtain the publications temporal and regional distributions, (2) create the cooperation networks between affiliations and authors and identify productive institutions in this context. Moreover, reviews of techniques for vision-based sign language recognition are presented. Various features extraction and classification techniques used in SLR to achieve good results are discussed. The literature review presented in this paper shows the importance of incorporating intelligent solutions into the sign language recognition systems and reveals that perfect intelligent systems for sign language recognition are still an open problem. Overall, it is expected that this study will facilitate knowledge accumulation and creation of intelligent-based SLR and provide readers, researchers, and practitioners a roadmap to guide future direction

Recognition of Bisindo Alphabets Based on Chain Code Contour and Similarity of Euclidean Distance

In Indonesia, there are two forms of sign language practiced in the community, i.e., Indonesian sign language or known as BISINDO, and Indonesian sign language system or known as SIBI. In this study, we conduct research about recognition of Bisindo alphabets using contour chain code for the method of feature extraction and similarity of Euclidean distance for the method of recognition. The features used are the probability of chain code generated from contour following and the formation of chain code. The proposed method in this study consisted of five section, i.e., input test image, segmentation, edge detection, feature extraction and matching process of the alphabet. In the testing of the proposed method, we used 52 images of hand gestures used as test images. The images are in the form of static images and 26 images of hand gestures used as reference images which represent 26 alphabets BISINDO from A to Z, where the images stored in the database. The test images of different shapes and sizes with image references. For recognition, we do the matching between the probability of the test image chain code with the probability of the reference image chain code using Euclidean distance. The measurement result of Euclidean distance in this study was generated average accuracy rate of similarity above 94%. This indicates that the method proposed in this study was effective and produce the level of similarity of BISINDO alphabets was accurate

Kurdish Sign Language Recognition System

Deaf people all around the world face difficulty to communicate with the others. Hence, they use their own language to communicate with each other. On the other hand, it is difficult for deaf people to get used to technological services such as websites, television, mobile applications, and so on. This project aims to design a prototype system for deaf people to help them to communicate with other people and computers without relying on human interpreters. The proposed system is for letter-based Kurdish Sign Language (KuSL) which has not been introduced before. The system would be a real-time system that takes actions immediately after detecting hand gestures. Three algorithms for detecting KuSL have been implemented and tested, two of them are well-known methods that have been implemented and tested by other researchers, and the third one has been introduced in this paper for the 1st time. The new algorithm is named Gridbased gesture descriptor. It turned out to be the best method for the recognition of Kurdish hand signs. Furthermore, the result of the algorithm was 67% accuracy of detecting hand gestures. Finally, the other well-known algorithms are named scale invariant feature transform and speeded-up robust features, and they responded with 42% of accuracy

Machine learning approaches to video activity recognition: from computer vision to signal processing

244 p.La investigación presentada se centra en técnicas de clasificación para dos tareas diferentes, aunque relacionadas, de tal forma que la segunda puede ser considerada parte de la primera: el reconocimiento de acciones humanas en vídeos y el reconocimiento de lengua de signos.En la primera parte, la hipótesis de partida es que la transformación de las señales de un vídeo mediante el algoritmo de Patrones Espaciales Comunes (CSP por sus siglas en inglés, comúnmente utilizado en sistemas de Electroencefalografía) puede dar lugar a nuevas características que serán útiles para la posterior clasificación de los vídeos mediante clasificadores supervisados. Se han realizado diferentes experimentos en varias bases de datos, incluyendo una creada durante esta investigación desde el punto de vista de un robot humanoide, con la intención de implementar el sistema de reconocimiento desarrollado para mejorar la interacción humano-robot.En la segunda parte, las técnicas desarrolladas anteriormente se han aplicado al reconocimiento de lengua de signos, pero además de ello se propone un método basado en la descomposición de los signos para realizar el reconocimiento de los mismos, añadiendo la posibilidad de una mejor explicabilidad. El objetivo final es desarrollar un tutor de lengua de signos capaz de guiar a los usuarios en el proceso de aprendizaje, dándoles a conocer los errores que cometen y el motivo de dichos errores

Read My Lips: Continuous Signer Independent Weakly Supervised Viseme Recognition

Abstract. This work presents a framework to recognise signer indepen-dent mouthings in continuous sign language, with no manual annotations needed. Mouthings represent lip-movements that correspond to pronun-ciations of words or parts of them during signing. Research on sign lan-guage recognition has focused extensively on the hands as features. But sign language is multi-modal and a full understanding particularly with respect to its lexical variety, language idioms and grammatical structures is not possible without further exploring the remaining information chan-nels. To our knowledge no previous work has explored dedicated viseme recognition in the context of sign language recognition. The approach is trained on over 180.000 unlabelled frames and reaches 47.1 % precision on the frame level. Generalisation across individuals and the influence of context-dependent visemes are analysed

Towards Subject Independent Sign Language Recognition : A Segment-Based Probabilistic Approach

Ph.DDOCTOR OF PHILOSOPH