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

A Survey of Applications and Human Motion Recognition with Microsoft Kinect

Microsoft Kinect, a low-cost motion sensing device, enables users to interact with computers or game consoles naturally through gestures and spoken commands without any other peripheral equipment. As such, it has commanded intense interests in research and development on the Kinect technology. In this paper, we present, a comprehensive survey on Kinect applications, and the latest research and development on motion recognition using data captured by the Kinect sensor. On the applications front, we review the applications of the Kinect technology in a variety of areas, including healthcare, education and performing arts, robotics, sign language recognition, retail services, workplace safety training, as well as 3D reconstructions. On the technology front, we provide an overview of the main features of both versions of the Kinect sensor together with the depth sensing technologies used, and review literatures on human motion recognition techniques used in Kinect applications. We provide a classification of motion recognition techniques to highlight the different approaches used in human motion recognition. Furthermore, we compile a list of publicly available Kinect datasets. These datasets are valuable resources for researchers to investigate better methods for human motion recognition and lower-level computer vision tasks such as segmentation, object detection and human pose estimation

Analysis of Image Classification Deep Learning Algorithm

This study explores the use of TensorFlow 2 and Python for image classification problems. Image categorization is an important area in computer vision, with several real-world applications such as object identification/recognition, medical imaging, and autonomous driving. This work studies TensorFlow 2 and its image categorization capabilities. We also demonstrate how to construct an image classification model using Python and TensorFlow 2. This analysis of image classification neural network problems with the use of Convolutional Neural Network (CNN) on the German and the Chinese traffic sign datasets is an engineering task. Ultimately, this work provides step-by-step guidance for creating an image classification model using TensorFlow 2 and Python, while also showcasing its potential to tackle image classification issues across various domains

American Sign Language Recognition System by Using Surface EMG Signal

Sign Language Recognition (SLR) system is a novel method that allows hard of hearing to communicate with general society. In this study, 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 data are acquired from subject right forearm for twenty-seven American Sign Language gestures of twenty-six English alphabets and one for home position. Time and frequency domain (band power) information used in the feature extraction process. As a classification method, Support Vector Machine and Ensemble Learning algorithm were used and their performances are compared with tabulated results. In conclusion, the results of this study show that sEMG signal can be used for SLR systems

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

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

New deep learning approaches to domain adaptation and their applications in 3D hand pose estimation

This study investigates several methods for using artificial intelligence to give machines the ability to see. It introduced several methods for image recognition that are more accurate and efficient compared to the existing approaches

AMERICAN SIGN LANGUAGE FINGERSPELLING USING HYBRID DISCRETE WAVELET TRANSFORM-GABOR FILTER AND CONVOLUTIONAL NEURAL NETWORK

American Sign Language (ASL) is widely used for communication by deaf and mute people. In fingerspelling, the letters of the writing system are represented using only hands. Generally, hearing people do not understand sign language and this creates a communication gap between the signer and speaker community. A real-time ASL fingerspelling recognizer can be developed to solve this problem. Sign language recognizer can also be trained for other applications such as human-computer interaction. In this paper, a hybrid Discrete Wavelet TransformGabor filter is used on the colour images to extract features. Classifiers are evaluated on signer dependent and independent datasets. For evaluation, it is very important to consider signer dependency. Random Forest, Support Vector Machine and K-Nearest Neighbors classifiers are evaluated on the extracted set of features to classify the 24 classes of ASL alphabets with 95.8%, 94.3% and 96.7% accuracy respectively on signer dependent dataset and 49.16%, 48.75% and 50.83% accuracy respectively on signer independent dataset. Lastly, Convolutional Neural Network was also trained and evaluated on both, which produced 97.01% accuracy on signer dependent and 76.25% accuracy on signer independent dataset

A Survey on Metric Learning for Feature Vectors and Structured Data

The need for appropriate ways to measure the distance or similarity between data is ubiquitous in machine learning, pattern recognition and data mining, but handcrafting such good metrics for specific problems is generally difficult. This has led to the emergence of metric learning, which aims at automatically learning a metric from data and has attracted a lot of interest in machine learning and related fields for the past ten years. This survey paper proposes a systematic review of the metric learning literature, highlighting the pros and cons of each approach. We pay particular attention to Mahalanobis distance metric learning, a well-studied and successful framework, but additionally present a wide range of methods that have recently emerged as powerful alternatives, including nonlinear metric learning, similarity learning and local metric learning. Recent trends and extensions, such as semi-supervised metric learning, metric learning for histogram data and the derivation of generalization guarantees, are also covered. Finally, this survey addresses metric learning for structured data, in particular edit distance learning, and attempts to give an overview of the remaining challenges in metric learning for the years to come.Comment: Technical report, 59 pages. Changes in v2: fixed typos and improved presentation. Changes in v3: fixed typos. Changes in v4: fixed typos and new method

Application of Computer Vision and Mobile Systems in Education: A Systematic Review

The computer vision industry has experienced a significant surge in growth, resulting in numerous promising breakthroughs in computer intelligence. The present review paper outlines the advantages and potential future implications of utilizing this technology in education. A total of 84 research publications have been thoroughly scrutinized and analyzed. The study revealed that computer vision technology integrated with a mobile application is exceptionally useful in monitoring students’ perceptions and mitigating academic dishonesty. Additionally, it facilitates the digitization of handwritten scripts for plagiarism detection and automates attendance tracking to optimize valuable classroom time. Furthermore, several potential applications of computer vision technology for educational institutions have been proposed to enhance students’ learning processes in various faculties, such as engineering, medical science, and others. Moreover, the technology can also aid in creating a safer campus environment by automatically detecting abnormal activities such as ragging, bullying, and harassment