Real-time Sign Recognition System Research Articles

Ionic liquid-reinforced transparent, stretchable, conductive organic ionic gel with ultra-high sensory capability and ultra-robust impact-resistance

Traditional conductive hydrogels face challenges in environmental stability and mechanical limitations, constraining their applications in wearable devices. Here, we present a novel organic ionic gel by the construction of PVA/EG double polymer network and ionic liquid, named POIG. It showcases outstanding tensile strength (3.01 MPa) and extensibility (820 %). POIG excels in both low and high-speed impacts, dissipating over 90 % of energy in low-speed impacts and outperforming commercial foam five times in high-speed impacts (1.4 kJ/m). The addition of an ionic liquid in POIG creates a stable, uniform, and highly ordered internal conductive structure, leading to ultra-low strain responsiveness (0.2 %), ultra-fast response times (≈10 ms), high linearity (R2 = 0.999), and excellent sensing performance. High stability is also confirmed through 4000 cycles of both tensile and compression testing. Additionally, we developed a real-time sign language recognition system using deep learning, accurately identifying ten gestures with 98.5 % accuracy. This research offers a durable and innovative organic material for future wearable technology and smart devices.

Virtual Assistant With Sign Language

This topic is all about developing a successful Sign language recognition and translation system which is helpful for people with certain disabilities who can’t use present voice enabled Virtual Assistants. Communication is important with people as it helps us understand them and gain knowledge. In case of Deaf & mute people, this becomes a problem. It is solved by creating a virtual assistant built to recognize hand gestures and translate them in normal language to perform tasks ordered by the user. This certainly helps other normal people to successfully communicate with deaf & mute people Without worrying about any misunderstanding at the listener’s end. There are more than 300 sign languages used by various cultural groups worldwide. In this article, we provide a technique for generating a sign language dataset using a camera, followed by the training of a TensorFlow model using transfer learning to produce a real-time sign language recognition system. Despite the small amount of the dataset, the system still performs well.

Sign Language Recognition System using Flex Sensor Network

This journal work on sign language recognition system using flex sensor network aims to x-ray the implementation of a sign language recognition system using smart gloves, raspberry pi, python and C language. Therefore, research into sign language interpretation using gestures has been explored progressively during recent decades to serve as an auxiliary tool for deaf and mute people to blend into society without barriers. In this work, a smart sign language interpretation system using a wearable hand device. This wearable system utilizes five flex-sensors, then using a PIC microcontroller, raspberry pi, python and C language to create a real-time Sign Language Recognition system. In this journal work, a sensor-based sign language recognition can serve as a key for overcoming many difficulties and providing convenience for human life. The ability of machines to understand human activities and their meaning can be utilized in a vast array of applications. This work provides a thorough technique in recent hand gesture and sign language recognition. After implementation, the system was able to output up to thirty-one phrases successfully. This paper was focused on improving the accuracy and usability of the system by optimizing the sensor placement and developing a user-friendly interface.

Real-time sign language recognition system

One of the more natural ways of interaction between the machine and human is offered by Human gestures which are useful for sign language recognition (SLR). Sign language is the speaking tongue of a segment of people who are known as Deaf people. they are the most people who benefit from the (SLR) by the Human-Computer Interaction (HCI).Most natural people cannot understand these kinds of languages, so Deaf people fail to communicate with them without this helper which achieves by the (HCI). This study introduced a proposed system that offered SLR in real-time for some gestures from the American sign language (ASL), by using one of the most suitable deep learning-based architectures that were called Convolutional neural networks (CNN) and choosing the Squeezenet module After comparing it with a traditional machine learning system that relied on extracting [HOG] features. Squeezenet presented the best result in accuracy reached to (100%) in the off-time testing and about (97.5%) in the real-time with a competition time of about 3.3sec for capturing the image, predicting it, and converting it to a text and spoken sentence.The system achieved this result without making any preprocessing for the image, which gave it simplicity and low computing time.

Text to Speech and Language Conversion in Hindi and English Using CNN

Abstract: Deaf and dumb persons use sign language to communicate with other people in their society. Because sign language is the only means of communication for persons who are deaf or hard of hearing, it is mostly utilized by them. Ordinary folks are unfamiliar with this language. A real-time sign language recognition system has been developed in this article to allow those who do not know sign language to communicate with hearing-impaired people more readily. In this case, we employed American Sign Language. We have used American Sign Language in this paper. We introducing the development and implementation of an American Sign Language (ASL) derived from convolutional neural network. Deep Learning Method is used to train a classifier to recognize Sign Language and Convolutional Neural Network (CNN) is used to extract features from the images. We have also used Text-To-Speech Synthesis to convert the detected output into speech format. With use of MATLAB function the obtained text is converted into voice. In our system we are converting text to speech in Hindi language. Therefore hand gesture made by deaf and dumb people has been anatomized and restated into text and voice for better communication. Keywords: Sign Language Recognition, Image Processing, Text-To-Speech, American Sign Language, Classification, Convolutional Neural Network, Deep Learning real-time

American sign language recognition and training method with recurrent neural network

Though American sign language (ASL) has gained recognition from the American society, few ASL applications have been developed with educational purposes. Those designed with real-time sign recognition systems are also lacking. Leap motion controller facilitates the real-time and accurate recognition of ASL signs. It allows an opportunity for designing a learning application with a real-time sign recognition system that seeks to improve the effectiveness of ASL learning. The project proposes an ASL learning application prototype. The application would be a whack-a-mole game with a real-time sign recognition system embedded. Since both static and dynamic signs (J, Z) exist in ASL alphabets, Long-Short Term Memory Recurrent Neural Network with k-Nearest-Neighbour method is adopted as the classification method is based on handling of sequences of input. Characteristics such as sphere radius, angles between fingers and distance between finger positions are extracted as input for the classification model. The model is trained with 2600 samples, 100 samples taken for each alphabet. The experimental results revealed that the recognition rate for 26 ASL alphabets yields an average of 99.44% accuracy rate and 91.82% in 5-fold cross-validation with the use of leap motion controller.

Based on wearable sensory device in 3D-printed humanoid: A new real-time sign language recognition system

This study proposes a new real-time sign recognition system based on a wearable sensory glove, which has 17 sensors with 65 channels. We introduce the DataGlove to recognize various, possibly complex hand gestures of the Malaysian Sign Language (MSL). With 65 data channels, DataGlove can satisfy the requirement suggested by the analysis of hand anatomy, kinematic and gestures. Four groups of sensors were tested to select the optimal sensors that can capture hand gesture information. Also, a 3D-printed humanoid arm is used to validate the sensor mounted on the glove. In an extensive set of experiments to test our system, five well-known MSL participants were chosen to perform 75 gestures, all taken from the MSL numbers, alphabet, and words. The error rate was used to check system performance. Discussion confirms that the proposed system competes well with an advanced benchmark of previous works is up 100% based on 14 criteria in term of the type of captured signals, recognised gestures, and solved issues. Results show that our system is capable of recognizing the wide range of gestures with recognition accuracies of 99%, 96% and 93.4% for numbers, alphabet letters and words of MSL, respectively. This research contributes to enhancing the lifestyle of people with disabilities, and bridge the gap between people with hearing impairment and ordinary people.

Kinect-based Taiwanese sign-language recognition system

Gesture-recognition is an important component for many intelligent human---computer interaction applications. For example, a realtime sign-language recognition system would detect and interpret hand gestures. Many vision-based sign-language recognition methods have been proposed over the years with mix results of usability. Some system are limited to recognize only a few gestures, while others require the use of 3D camera to provides depth information to improve recognition accuracy. In this paper, a Kinect-based Taiwanese sign-language recognition system is proposed. Three main features are extracted from the signing gestures, namely hand positions, hand signing direction, and hand shapes. The hand positions are readily available through the input sensor. The signing direction is determined using HMM on trajectory of the hand movement, and a SVM is trained and used to recognize the hand shapes. Experimental results show that the proposed system achieved an 85.14 % recognition rate.