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1
USHA, M. NITHIN GOWDA H. SANTHOSH M. NANDA KUMAR S. NUTHAN PAWAR E. "REAL-TIME HAND SIGN TRAINING AND DETECTION." International Journal For Technological Research In Engineering 11, no. 5 (2024): 149–51. https://doi.org/10.5281/zenodo.10554229.
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Real-time hand sign recognition and detection are major for applications in human computer interaction, sign language interpretation, and gesture-based control systems.This project focuses on creating realtime hand gesture and finger gesture annotations using the MediaPipe framework in Python.The hand gestures and finger gestures using keypoints and finger coordinates found by the MediaPipe framework.The system offers two machine learning models: one for recognizing hand signs and another for detecting finger gestures. It provides resources, including sample programs, model files, and training data, allowing users to utilize pre-trained models. Key Dependencies: MediaPipe, OpenCV, TensorFlow tf-nightly for TFLite models with LSTM, scikit for confusion matrix display, matplotlib (for visualization The system's structure consists of sample programs, model files, and training data, offering users flexibility in training and utilizing the models. A demo program is also provided for realtime use with a webcam, complete with options for customization. this project offers a robust solution training and detection. Users can effectively recognize hand signs and finger gestures through the integrating the MediaPipe framework and learning models, enabling applications in human-computer interaction and beyond.
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Yu, Myoungseok, Narae Kim, Yunho Jung, and Seongjoo Lee. "A Frame Detection Method for Real-Time Hand Gesture Recognition Systems Using CW-Radar." Sensors 20, no. 8 (2020): 2321. http://dx.doi.org/10.3390/s20082321.
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In this paper, a method to detect frames was described that can be used as hand gesture data when configuring a real-time hand gesture recognition system using continuous wave (CW) radar. Detecting valid frames raises accuracy which recognizes gestures. Therefore, it is essential to detect valid frames in the real-time hand gesture recognition system using CW radar. The conventional research on hand gesture recognition systems has not been conducted on detecting valid frames. We took the R-wave on electrocardiogram (ECG) detection as the conventional method. The detection probability of the conventional method was 85.04%. It has a low accuracy to use the hand gesture recognition system. The proposal consists of 2-stages to improve accuracy. We measured the performance of the detection method of hand gestures provided by the detection probability and the recognition probability. By comparing the performance of each detection method, we proposed an optimal detection method. The proposal detects valid frames with an accuracy of 96.88%, 11.84% higher than the accuracy of the conventional method. Also, the recognition probability of the proposal method was 94.21%, which was 3.71% lower than the ideal method.
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Zheng, Zhuowen. "Gesture recognition real-time control system based on YOLOV4." Journal of Physics: Conference Series 2196, no. 1 (2022): 012026. http://dx.doi.org/10.1088/1742-6596/2196/1/012026.
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Abstract With the development of industrial information technology in recent years, gesture control has attracted wide attention from scholars. Various gesture control methods have emerged, such as visual control, wearable device control, magnetic field feature extraction control. Based on one of the visual gesture control methods, this paper proposes a visual gesture control method applied to music box control by combining YOLOv4 object detection network. We design seven main gestures, reconstruct gesture datasets, and retrain the YOLOv4 object detection network by the means of the self-built datasets, further build a music box gesture control system. In this paper, we obtain the recognition accuracy of 97.8% for the object detection network in the gesture control system after a series of experiments, and recruit eight volunteers to conduct experimental tests on the self-built gesture-controlled music box system, mainly to quantify the time of executing a single command, attention concentration, etc. The results show that compared with the traditional control method, the visual gesture control method ensures the accuracy while has a faster response speed and takes up less of the user’s attention.
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Meng, Yuting, Haibo Jiang, Nengquan Duan, and Haijun Wen. "Real-Time Hand Gesture Monitoring Model Based on MediaPipe’s Registerable System." Sensors 24, no. 19 (2024): 6262. http://dx.doi.org/10.3390/s24196262.
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Hand gesture recognition plays a significant role in human-to-human and human-to-machine interactions. Currently, most hand gesture detection methods rely on fixed hand gesture recognition. However, with the diversity and variability of hand gestures in daily life, this paper proposes a registerable hand gesture recognition approach based on Triple Loss. By learning the differences between different hand gestures, it can cluster them and identify newly added gestures. This paper constructs a registerable gesture dataset (RGDS) for training registerable hand gesture recognition models. Additionally, it proposes a normalization method for transforming hand gesture data and a FingerComb block for combining and extracting hand gesture data to enhance features and accelerate model convergence. It also improves ResNet and introduces FingerNet for registerable single-hand gesture recognition. The proposed model performs well on the RGDS dataset. The system is registerable, allowing users to flexibly register their own hand gestures for personalized gesture recognition.
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Prananta, Gidion Bagas, Hagi Azzam Azzikri, and Chaerur Rozikin. "REAL-TIME HAND GESTURE DETECTION AND RECOGNITION USING CONVOLUTIONAL ARTIFICIAL NEURAL NETWORKS." METHODIKA: Jurnal Teknik Informatika dan Sistem Informasi 9, no. 2 (2023): 30–34. http://dx.doi.org/10.46880/mtk.v9i2.1911.
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Real-time hand gesture detection is an interesting topic in pattern recognition and computer vision. In this study, we propose the use of a Convolutional Neural Network (CNN) to detect and recognize hands in real-time. Our goal is to develop a system that can accurately identify and interpret user gestures in real-time. The proposed approach involves two main stages, namely hand gesture recognition and gesture recognition. For stage detection, we use the CNN architecture to recognize hands in the video. We train the CNN model using a dataset containing various hand gestures. Once a hand is detected, we extract the relevant hand region and proceed to the gesture recognition stage. The gesture recognition stage involves training and testing CNN models for different hand signal recognition. We use a hand gesture dataset that contains a variety of common hand signals. The experimental results show that the proposed system can detect and recognize hand movements in real-time with satisfactory accuracy. Although there are still some challenges that need to be overcome, this research provides a solid foundation for further development in real-time hand gesture recognition.
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Bhargavi, Mrs Jangam, Chitikala Sairam, and Donga Hemanth. "Real time interface for deaf-hearing communication." International Scientific Journal of Engineering and Management 04, no. 03 (2025): 1–7. https://doi.org/10.55041/isjem02356.
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Bridging the communication gap between the deaf and hearing communities using AI is achieved by integrating two key modules: Speech-to-Sign Language Translation and Sign Gesture Detection in Real Time. The first module translates English spoken language into American Sign Language (ASL) animations. It consists of three sub-modules: speech-to-text conversion using the speech recognition module in Python, English text to ASL gloss translation using an NLP model, and ASL gloss to animated video generation, where DWpose Pose Estimation, and an avatar is used for visual representation. The second module focuses on real-time sign gesture detection, where a dataset is created from the WLASL and MS-ASL datasets. Hand gestures are labeled using Labeling, and a YOLO-based model is trained for hand pose detection to enable real-time recognition. The system aims to enhance accessibility and interaction between deaf and hearing users through an efficient, automated translation and recognition pipeline. Keywords: Speech-to-sign translation, real-time sign language recognition, ASL gloss, YOLO hand pose detection, AI for accessibility, deep learning for sign language, gesture recognition, DWpose Pose Estimation, NLP, dataset labeling, real-time gesture recognition.
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Liu, Chang, and Tamás Szirányi. "Real-Time Human Detection and Gesture Recognition for On-Board UAV Rescue." Sensors 21, no. 6 (2021): 2180. http://dx.doi.org/10.3390/s21062180.
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Unmanned aerial vehicles (UAVs) play an important role in numerous technical and scientific fields, especially in wilderness rescue. This paper carries out work on real-time UAV human detection and recognition of body and hand rescue gestures. We use body-featuring solutions to establish biometric communications, like yolo3-tiny for human detection. When the presence of a person is detected, the system will enter the gesture recognition phase, where the user and the drone can communicate briefly and effectively, avoiding the drawbacks of speech communication. A data-set of ten body rescue gestures (i.e., Kick, Punch, Squat, Stand, Attention, Cancel, Walk, Sit, Direction, and PhoneCall) has been created by a UAV on-board camera. The two most important gestures are the novel dynamic Attention and Cancel which represent the set and reset functions respectively. When the rescue gesture of the human body is recognized as Attention, the drone will gradually approach the user with a larger resolution for hand gesture recognition. The system achieves 99.80% accuracy on testing data in body gesture data-set and 94.71% accuracy on testing data in hand gesture data-set by using the deep learning method. Experiments conducted on real-time UAV cameras confirm our solution can achieve our expected UAV rescue purpose.
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Anthoniraj, Dr S. "GestureSpeak & Real-Time Virtual Mouse Using Hand Gestures." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem46121.
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Abstract - GestureSpeak presents a new method of virtual mouse control that enables real-time hand gestures to be used to interface with computers. GestureSpeak, which was created to increase accessibility and inclusion, uses machine learning and computer vision algorithms to identify and decipher hand gestures and convert them into virtual mouse operations. To improve communication for those who use sign language, the system also has a sign language interpreter that translates American Sign Language (ASL) movements into spoken words. GestureSpeak overcomes the drawbacks of physical input devices and conventional mouse systems by building upon standard gesture recognition techniques, offering users with physical disabilities a flexible and hands-free option. GestureSpeak hopes to provide a smooth user experience in a variety of settings by optimizing gesture detection and performance. Keywords — Computer Vision, Accessibility, Sign Language Translation, Virtual Mice, Real-time Gesture Detection and Human-Computer Interaction
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Shinde, Siddhesh, Vaibhav Sonawane, Om Suryawanshi, R. U. Shekokar, and Prathamesh Mohalkar. "Real-Time American Sign Language Detection System Using Raspberry Pi and Sequential CNN." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem43820.
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This paper presents the development of a real time American Sign Language (ASL) detection system using Raspberry Pi and a Sequential Convolutional Neural Network (CNN) model. The system aims to bridge the communication gap between the Deaf and Hard of Hearing (DHH) community and the hearing population by translating ASL gestures into text and audio outputs. The proposed system leverages the Raspberry Pi 5, a cost effective and scalable hardware platform, combined with deep learning techniques to achieve high accuracy in gesture recognition. The system utilizes the custom dataset for training and employs hand landmark detection using MediaPipe for real-time gesture analysis. The results demonstrate an 85% accuracy in recognizing ASL gestures, with real-time text and audio outputs. The system is designed for personal, educational, and public applications, offering a practical solution for enhancing communication accessibility for the DHH community. Key Words: American Sign Language(ASL), Raspberry Pi, Machine Learning, Gesture recognition, Sign Language Translation, Text-to-Speech conversion
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Prof., C. D. Sawarkar Vivek Vaidya Vansh Sharma Samir Sheikh Aniket Neware Prathmesh Chaudhari. "AI Based Real Time Hand Gesture Recognition System." International Journal of Advanced Innovative Technology in Engineering 9, no. 3 (2024): 320–23. https://doi.org/10.5281/zenodo.12747525.
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This research presents a comprehensive approach for real-time hand gesture recognition using a synergistic combination of TensorFlow, OpenCV, and Media Pipe. Hand gesture recognition holds immense potential for natural and intuitive human-computer interaction in various applications, such as augmented reality, virtual reality, and human computer interfaces. The proposed system leverages the strengths of TensorFlow for deep learning-based model development, OpenCV for computer vision tasks, and Media Pipe for efficient hand landmark detection. The workflow begins with hand detection using OpenCV, followed by the extraction of hand landmarks through Media Pipe's hand tracking module. These landmarks serve as crucial input features for a custom trained TensorFlow model, designed to recognize a diverse set of hand gestures. The model is trained on a well- curated dataset, ensuring robust performance across different hand shapes, sizes, and orientations.
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Tran, Dinh-Son, Ngoc-Huynh Ho, Hyung-Jeong Yang, Eu-Tteum Baek, Soo-Hyung Kim, and Gueesang Lee. "Real-Time Hand Gesture Spotting and Recognition Using RGB-D Camera and 3D Convolutional Neural Network." Applied Sciences 10, no. 2 (2020): 722. http://dx.doi.org/10.3390/app10020722.
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Using hand gestures is a natural method of interaction between humans and computers. We use gestures to express meaning and thoughts in our everyday conversations. Gesture-based interfaces are used in many applications in a variety of fields, such as smartphones, televisions (TVs), video gaming, and so on. With advancements in technology, hand gesture recognition is becoming an increasingly promising and attractive technique in human–computer interaction. In this paper, we propose a novel method for fingertip detection and hand gesture recognition in real-time using an RGB-D camera and a 3D convolution neural network (3DCNN). This system can accurately and robustly extract fingertip locations and recognize gestures in real-time. We demonstrate the accurateness and robustness of the interface by evaluating hand gesture recognition across a variety of gestures. In addition, we develop a tool to manipulate computer programs to show the possibility of using hand gesture recognition. The experimental results showed that our system has a high level of accuracy of hand gesture recognition. This is thus considered to be a good approach to a gesture-based interface for human–computer interaction by hand in the future.
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Ms., H. Vaishnavi, Seetha Rama Raju SV Mr., Adaveni Nithin Mr., and Sandhya N. Dr. "Real-Time Sign Language Detection and Translation Using CNN." Journal of Scholastic Engineering Science and Management 2, no. 8 (2023): 1–11. https://doi.org/10.5281/zenodo.8170563.
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<strong>Our project focuses on the development of a computer application and the training of a model specifically designed to interpret real-time video footage of hand gestures in American Sign Language (ASL). The primary objective is to create a system that can accurately recognize and translate these gestures into corresponding text formats on the screen. To achieve this, we have successfully implemented a set of 27 symbols representing the alphabet (A-Z) in ASL, along with a symbol for blank or no gesture. Our project aims to provide a reliable and efficient tool for facilitating communication between ASL users and non-ASL speakers. </strong>
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Nyirarugira, Clementine, Hyo-rim Choi, and TaeYong Kim. "Hand Gesture Recognition Using Particle Swarm Movement." Mathematical Problems in Engineering 2016 (2016): 1–8. http://dx.doi.org/10.1155/2016/1919824.
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We present a gesture recognition method derived from particle swarm movement for free-air hand gesture recognition. Online gesture recognition remains a difficult problem due to uncertainty in vision-based gesture boundary detection methods. We suggest an automated process of segmenting meaningful gesture trajectories based on particle swarm movement. A subgesture detection and reasoning method is incorporated in the proposed recognizer to avoid premature gesture spotting. Evaluation of the proposed method shows promising recognition results: 97.6% on preisolated gestures, 94.9% on stream gestures with assistive boundary indicators, and 94.2% for blind gesture spotting on digit gesture vocabulary. The proposed recognizer requires fewer computation resources; thus it is a good candidate for real-time applications.
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Abhishek, Chauhan, and Shyni Emilin. "Affordable Real-Time Hand Gesture Detection Using Random Forest." International Journal of Innovative Science and Research Technology (IJISRT) 10, no. 2 (2025): 183–90. https://doi.org/10.5281/zenodo.14898697.
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This paper represents a hand gesture recognition system that classifies each gesture from a given gesture alphabet, makes use of the Random Forest Algorithm alongside with Pickle library to efficiently save and train the model. This system is capable of identifying all the alphabets along the continuous stream of video. The approach focuses on training the Random Forest model to identify between the gesture and non-gesture instances based on the features extracted from the training dataset. The saved models seamlessly allow for integration for the real-time use. The results produced truly shows the efficiency of the Random Forest approach achieving the desired accuracy without having or requiring any additional complex preprocessing or additional spatial information. We conclude the advantage of the approach as to how low the computational cost can be attained and ease of implementation, while keeping in mind the area for enhancement for future perspective.
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Moldovan, Constantin Catalin, and Ionel Staretu. "Real-Time Gesture Recognition for Controlling a Virtual Hand." Advanced Materials Research 463-464 (February 2012): 1147–50. http://dx.doi.org/10.4028/www.scientific.net/amr.463-464.1147.
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Object tracking in three dimensional environments is an area of research that has attracted a lot of attention lately, for its potential regarding the interaction between man and machine. Hand gesture detection and recognition, in real time, from video stream, plays a significant role in the human-computer interaction and, on the current digital image processing applications, this represent a difficult task. This paper aims to present a new method for human hand control in virtual environments, by eliminating the need of an external device currently used for hand motion capture and digitization. A first step in this direction would be the detection of human hand, followed by the detection of gestures and their use to control a virtual hand in a virtual environment.
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Shaha, Arya Ashish, Sanika Ashok Salunkhe, Milind Mahadeo Gargade, and Rutuja Vijay Kumbhar. "Hand Gesture Detection with Indication." International Journal of Engineering Research for Sustainable Development 1, no. 1 (2025): 1–4. https://doi.org/10.5281/zenodo.15332400.
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AbstractThis paper presents a method for hand gesture detection and indication using a flex sensor and Arduino. Hand gesture detection using flexible sensors and Arduino platforms offers an innovative approach to human-computer interaction, enabling intuitive control systems in various applications such as robotics, gaming, and assistive technology. This paper presents a system that utilizes flex sensors integrated with Arduino microcontrollers to detect hand gestures and provide real-time indications based on the recognized gestures. Flex sensors, which measure the degree of bending in fingers or hands, are strategically placed on a glove or wearable device to capture the user's hand movements. The Arduino system processes the sensor data to interpret specific gestures, such as open, closed, or finger-specific movements. Upon detecting a gesture, the system triggers corresponding feedback mechanisms, such as visual indicators on a screen or haptic responses, providing clear indications to the user. The approach's simplicity, low cost, and high accuracy make it suitable for applications in gesture-based control systems, virtual reality interfaces, and sign language translation. Experimental results show that the system is capable of reliably detecting a range of hand gestures and delivering appropriate responses, demonstrating its effectiveness in creating intuitive interaction environments.
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Shaha, Arya Ashish, Sanika Ashok Salunkhe, Milind Mahadeo Gargade, and Rutuja Vijay Kumbhar. "Hand Gesture Detection with Indication." International Journal of Engineering Research for Sustainable Development 1, no. 1 (2025): 1–4. https://doi.org/10.5281/zenodo.15332725.
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<em>Abstract</em> <em>This paper presents a method for hand gesture detection and indication using a flex sensor and Arduino. </em><em>Hand gesture detection using flexible sensors and Arduino platforms offers an innovative approach to human-computer interaction, enabling intuitive control systems in various applications such as robotics, gaming, and assistive technology. This paper presents a system that utilizes flex sensors integrated with Arduino microcontrollers to detect hand gestures and provide real-time indications based on the recognized gestures. Flex sensors, which measure the degree of bending in fingers or hands, are strategically placed on a glove or wearable device to capture the user's hand movements. The Arduino system processes the sensor data to interpret specific gestures, such as open, closed, or finger-specific movements. Upon detecting a gesture, the system triggers corresponding feedback mechanisms, such as visual indicators on a screen or haptic responses, providing clear indications to the user. The approach's simplicity, low cost, and high accuracy make it suitable for applications in gesture-based control systems, virtual reality interfaces, and sign language translation. Experimental results show that the system is capable of reliably detecting a range of hand gestures and delivering appropriate responses, demonstrating its effectiveness in creating intuitive interaction environments.</em>
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Fahad, Azher A., Hassan J. Hassan, and Salma H. Abdullah. "Real-time Hand Gesture Extraction Using Python Programming Language Facilities." Engineering and Technology Journal 39, no. 6 (2021): 1031–40. http://dx.doi.org/10.30684/etj.v39i6.1619.
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Hand gesture recognition is one of communication in which used bodily behavior to transmit several messages. This paper aims to detect hand gestures with the mobile device camera and create a customize dataset that used in deep learning model training to recognize hand gestures. The real-time approach was used for all these objectives: the first step is hand area detection; the second step is hand area storing in a dataset form to use in the future for model training. A framework for human contact was put in place by studying pictures recorded by the camera. It was converted the RGB color space image to the greyscale, the blurring method is used for object noise removing efficaciously. To highlight the edges and curves of the hand, the thresholding method is used. And subtraction of complex background is applied to detect moving objects from a static camera. The objectives of the paper were reliable and favorable which helps deaf and dumb people interact with the environment through the sign language fully approved to extract hand movements. Python language as a programming manner to discover hand gestures. This work has an efficient hand gesture detection process to address the problem of framing from real-time video.
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Naveen, Y., and Ch Navya Sree. "GestureFlow: Advanced Hand Gesture Control System." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44540.
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Our project "GestureFlow: Advanced Hand Gesture Control System" leverages real-time computer vision and deep learning techniques to create a robust, touchless control interface using hand gestures. The system utilizes MediaPipe Hands for efficient hand landmark detection and processes dynamic hand movements and finger configurations to identify a wide range of intuitive gestures such as swipes, pinches, and specific finger patterns. These gestures are mapped to actions like mouse control, clicks, volume adjustment, media playback, screenshot capture, window management, and many more. The system includes smoothed cursor tracking, velocity-based gesture recognition, and responsive command execution to ensure real-time performance. It also offers dynamic visual feedback and adaptive handling of gesture timing to improve precision and usability. Overall, the project presents an accessible, multi-functional human-computer interaction framework aimed at enhancing hands-free control and reducing reliance on traditional input devices in everyday computing environments. Keywords: Hand Gesture Recognition, Human-Computer Interaction, Computer Vision, Deep Learning, MediaPipe, Real-Time Control, Touchless Interface, Gesture Classification, Cursor Navigation, Accessibility, Adaptive Gestures, Visual Feedback.
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Shinde, Aditya. "Indian Sign Language Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 01 (2025): 1–9. https://doi.org/10.55041/ijsrem41093.
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- The communication gap remains one of the most significant barriers between individuals with hearing and speech impairments and the broader society. This project addresses this challenge by developing a real-time Indian Sign Language (ISL) detection system that leverages computer vision and machine learning techniques. By capturing hand gestures from video input, the system translates these movements into text or speech, enabling effective communication between ISL users and those unfamiliar with the language. Additionally, the system incorporates text-to-speech functionality, ensuring a seamless and humanized interaction experience. The proposed model utilizes Convolutional Neural Networks (CNNs) for image processing and gesture recognition, trained on a comprehensive dataset of ISL gestures. The framework employs preprocessing, feature extraction, and classification algorithms to accurately identify static and dynamic gestures. The system is designed to focus on the nuances of ISL, providing accurate recognition of gestures in real time while offering multilingual support. This initiative aspires to create an inclusive environment by empowering the hearing-impaired community and promoting better integration within society. By using cost-effective techniques, the project ensures scalability and practicality for everyday applications, making communication more efficient and inclusive. Keywords: Indian Sign Language (ISL), Gesture Recognition, Convolutional Neural Networks (CNNs), Real-time Communication
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Herbert, Oswaldo Mendoza, David Pérez-Granados, Mauricio Alberto Ortega Ruiz, Rodrigo Cadena Martínez, Carlos Alberto González Gutiérrez, and Marco Antonio Zamora Antuñano. "Static and Dynamic Hand Gestures: A Review of Techniques of Virtual Reality Manipulation." Sensors 24, no. 12 (2024): 3760. http://dx.doi.org/10.3390/s24123760.
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This review explores the historical and current significance of gestures as a universal form of communication with a focus on hand gestures in virtual reality applications. It highlights the evolution of gesture detection systems from the 1990s, which used computer algorithms to find patterns in static images, to the present day where advances in sensor technology, artificial intelligence, and computing power have enabled real-time gesture recognition. The paper emphasizes the role of hand gestures in virtual reality (VR), a field that creates immersive digital experiences through the Ma blending of 3D modeling, sound effects, and sensing technology. This review presents state-of-the-art hardware and software techniques used in hand gesture detection, primarily for VR applications. It discusses the challenges in hand gesture detection, classifies gestures as static and dynamic, and grades their detection difficulty. This paper also reviews the haptic devices used in VR and their advantages and challenges. It provides an overview of the process used in hand gesture acquisition, from inputs and pre-processing to pose detection, for both static and dynamic gestures.
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Raman, Swati, Sanchita Patel, Surbhi Yadav, and Dr Vanchna Singh. "Emotion and Gesture detection." International Journal for Research in Applied Science and Engineering Technology 10, no. 5 (2022): 3731–34. http://dx.doi.org/10.22214/ijraset.2022.43205.
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Abstract: Machine learning algorithms have removed the constraints of computer vision. Researchers and developers have developed new approaches for detecting emotions making it possible to predict the behaviour and consecutive actions of human beings. As machine learning methods make use of GPUs' massive computation capability, these models' image processing skills are well suited to real-world issues. Computer vision has moved from a niche field to a variety of other fields, including behavioural sciences. These algorithms or models are utilised in a wide range of real-world applications, including security, driver safety, autonomous cars, human-computer interaction, and healthcare. Due to the emergence of graphics processing units, which are hardware devices capable of doing millions of computations in seconds or minutes, these models are constantly changing. Technologies like augmented reality and virtual reality are also on the rise. Robotic vision and interactive robotic communication are two of their most intriguing uses. Both verbal and visual modalities can be used to identify human emotions. Facial expressions are an excellent way to determine a person's emotional state. This work describes a real-time strategy for emotion and gesture detection. The fundamental idea is to use the MediaPipe framework which is based on real-time deep learning, to generate critical points. Furthermore, A series of precisely constructed mesh generators and angular encoding modules are used to encode the generated key points. Finally, by assessing failure instances of existing models, we are evaluating the applicability of emotion and gesture detection from our model.We are using models such as Random Forest(RF),logistic regression(LR),Gradient Classifier(GR) and Ridge classifier(RC). Real-time inference and good prediction quality are demonstrated by the suggested system and architecture. Keywords: Body Landmarks, MediaPipe, Prediction, Accuracy, Real-time on-device Tracking, Recognition.
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Alvin, Arsheldy, Nabila Husna Shabrina, Aurelius Ryo, and Edgar Christian. "Hand Gesture Detection for Sign Language using Neural Network with Mediapipe." Ultima Computing : Jurnal Sistem Komputer 13, no. 2 (2021): 57–62. http://dx.doi.org/10.31937/sk.v13i2.2109.
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The most popular way of interfacing with most computer systems is a mouse and keyboard. Hand gestures are an intuitive and effective touchless way to interact with computer systems. However, hand gesture-based systems have seen low adoption among end-users primarily due to numerous technical hurdles in detecting in-air gestures accurately. This paper presents Hand Gesture Detection for American Sign Language using K-Nearest Neighbor with Mediapipe, a framework developed to bridge this gap. The framework learns to detect gestures from demonstrations, it is customizable by end-users, and enables users to interact in real-time with computers having only RGB cameras, using gestures.
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Yoo, Minjeong, Yuseung Na, Hamin Song, et al. "Motion Estimation and Hand Gesture Recognition-Based Human–UAV Interaction Approach in Real Time." Sensors 22, no. 7 (2022): 2513. http://dx.doi.org/10.3390/s22072513.
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As an alternative to traditional remote controller, research on vision-based hand gesture recognition is being actively conducted in the field of interaction between human and unmanned aerial vehicle (UAV). However, vision-based gesture system has a challenging problem in recognizing the motion of dynamic gesture because it is difficult to estimate the pose of multi-dimensional hand gestures in 2D images. This leads to complex algorithms, including tracking in addition to detection, to recognize dynamic gestures, but they are not suitable for human–UAV interaction (HUI) systems that require safe design with high real-time performance. Therefore, in this paper, we propose a hybrid hand gesture system that combines an inertial measurement unit (IMU)-based motion capture system and a vision-based gesture system to increase real-time performance. First, IMU-based commands and vision-based commands are divided according to whether drone operation commands are continuously input. Second, IMU-based control commands are intuitively mapped to allow the UAV to move in the same direction by utilizing estimated orientation sensed by a thumb-mounted micro-IMU, and vision-based control commands are mapped with hand’s appearance through real-time object detection. The proposed system is verified in a simulation environment through efficiency evaluation with dynamic gestures of the existing vision-based system in addition to usability comparison with traditional joystick controller conducted for applicants with no experience in manipulation. As a result, it proves that it is a safer and more intuitive HUI design with a 0.089 ms processing speed and average lap time that takes about 19 s less than the joystick controller. In other words, it shows that it is viable as an alternative to existing HUI.
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R N, Pushpa. "Sign Language Detection Using Deep Learning." International Journal for Research in Applied Science and Engineering Technology 12, no. 12 (2024): 565–71. https://doi.org/10.22214/ijraset.2024.65817.
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Sign language recognition is an essential tool for bridging communication gaps between individuals with hearing or speech impairments and the broader community. This study introduces an advanced sign language recognition system leveraging computer vision and machine learning techniques. The system utilizes real-time hand tracking and gesture recognition to identify and classify hand gestures associated with common phrases such as "Hello," "I love you," and "Thank you." A two-step approach is implemented: first, a data collection module captures hand images using a robust preprocessing pipeline, ensuring uniformity in image size and quality; second, a classification module uses a trained deep learning model to accurately predict gestures in real-time. The framework integrates OpenCV for image processing, CVZone modules for hand detection, and TensorFlow for gesture classification. Extensive testing demonstrates the system's capability to process live video input, classify gestures accurately, and display corresponding labels seamlessly. This solution addresses challenges in gesture recognition, such as variable hand shapes and dynamic backgrounds, through efficient preprocessing and model training. By offering a scalable and efficient design, this work has the potential to contribute significantly to assistive technologies and accessible communication systems, paving the way for further advancements in human-computer interaction and inclusive technology.
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Gudi, Swaroop. "Sign Language Detection Using Gloves." International Journal for Research in Applied Science and Engineering Technology 12, no. 11 (2024): 1387–91. http://dx.doi.org/10.22214/ijraset.2024.65315.
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This paper presents a comprehensive system for real-time translation of Indian Sign Language (ISL) gestures into spoken language using gloves equipped with flex sensors. The system incorporates an Arduino Nano microcontroller for data acquisition, an HC-05 Bluetooth module for wireless data transmission, and an Android application for processing. A deep learning model, trained on an ISL dataset using Keras and TensorFlow, classifies the gestures. The processed data is then converted into spoken language using Google Text-to-Speech (GTTS). The gloves measure finger movements through flex sensors, with data transmitted to the Android app for real-time classification and speech synthesis. This system is designed to bridge communication gaps for the hearing-impaired community by providing an intuitive and responsive translation tool. Our evaluation shows high accuracy in gesture recognition, with average latency ensuring near real-time performance. The system's effectiveness is demonstrated through extensive testing, showcasing its potential as an assistive technology. Future improvements include expanding the dataset and incorporating additional sensors to enhance gesture recognition accuracy and robustness. This research highlights the integration of wearable technology and machine learning as a promising solution for enhancing accessibility and communication for sign language users
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Li, Xin Xiong, Yi Xiong, Zhi Yong Pang, and Di Hu Chen. "Hand Gesture Recognition Algorithm: A Real-Time Human-Body-Based Approach." Applied Mechanics and Materials 303-306 (February 2013): 1338–43. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.1338.
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Despite the appearance of high-tech human computer interface (HCI) devices, pattern recognition and gesture recognition with single camera are still playing vital role in research. A real-time human-body based algorithm for hand gesture recognition is proposed in this paper. The basis of our approach is a combination of moving object segmentation process and skin color detector based on human body structure to obtain the moving hands from input images, which is able to deal with the problem of complex background and random noises, and a rotate correction process for better finger detection. With ten fingers detected, more than 1000 gestures can be recognized before concerning motion paths. This paper includes experimental results of five gestures, which can be extended to other conditions. Experiments show that the algorithm can achieve a 99 percent recognition average rate and is suitable for real-time applications.
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Lu, Cong, Haoyang Zhang, Yu Pei, et al. "Online Hand Gesture Detection and Recognition for UAV Motion Planning." Machines 11, no. 2 (2023): 210. http://dx.doi.org/10.3390/machines11020210.
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Recent advances in hand gesture recognition have produced more natural and intuitive methods of controlling unmanned aerial vehicles (UAVs). However, in unknown and cluttered environments, UAV motion planning requires the assistance of hand gesture interaction in complex flight tasks, which remains a significant challenge. In this paper, a novel framework based on hand gesture interaction is proposed, to support efficient and robust UAV flight. A cascading structure, which includes Gaussian Native Bayes (GNB) and Random Forest (RF), was designed, to classify hand gestures based on the Six Degrees of Freedom (6DoF) inertial measurement units (IMUs) of the data glove. The hand gestures were mapped onto UAV’s flight commands, which corresponded to the direction of the UAV flight.The experimental results, which tested the 10 evaluated hand gestures, revealed the high accuracy of online hand gesture recognition under asynchronous detection (92%), and relatively low latency for interaction (average recognition time of 7.5 ms; average total time of 3 s).The average time of the UAV’s complex flight task was about 8 s shorter than that of the synchronous hand gesture detection and recognition. The proposed framework was validated as efficient and robust, with extensive benchmark comparisons in various complex real-world environments.
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Vasuki, M. "Al Powered Real-Time Sign Language Detection and Translation System for Inclusive Communication Between Deaf and Hearing Communities Worldwide." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–9. https://doi.org/10.55041/ijsrem50025.
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Abstract - Sign language is a vital communication tool for individuals who are deaf or hard of hearing, yet it remains largely inaccessible to the wider population. This project aims to address this barrier by developing a sign language recognition system that converts hand gestures into text, followed by text-to-speech (TTS) conversion. The system utilizes Convolutional Neural Networks (CNNs) to recognize static hand gestures and translate them into corresponding textual representations. The text is then processed by a TTS engine, which generates spoken language, making it comprehensible to individuals who are not familiar with sign language. The approach leverages deep learning techniques to improve gesture recognition accuracy, particularly in diverse real-world scenarios. By training the CNN on a comprehensive dataset of sign language gestures, the model is able to learn important features such as hand shape, orientation, and motion, which are critical for identifying specific signs. Keywords: Sign Language Recognition-Gesture to Text-Text to Speech (TTS)-Convolutional Neural Networks (CNN)-Deep Learning-Hand Gesture Recognition-Assistive Technology-Real-Time Translation-Speech Synthesis-Accessibility-Inclusivity-Communication Aid-Deaf and Hard of Hearing-Human-Computer Interaction-Static Hand Gestures
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Parihar, Er Anushka. "Real Time Sign Language Interpreter Using Live Video Feed using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem47967.
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Abstract - This paper presents a real-time sign language interpreter system designed to bridge the communication gap between Deaf/non-verbal individuals and non-signers. The proposed system captures hand gestures through a standard webcam and utilizes a Convolutional Neural Network (CNN) for accurate gesture recognition. Preprocessing steps such as Histogram of Oriented Gradients (HOG) and Gaussian Mixture Models (GMM) are used for background elimination and feature extraction. The recognized gestures are translated into both text and speech, enabling seamless interaction in real-world environments. The system also features a user-friendly interface and a modular web-based platform for future expansion. Unlike traditional glove-based or high-complexity models, this solution is lightweight, cost-effective, and deployable on standard hardware, making it highly accessible for educational, professional, and assistive applications. Key Words: Sign Language Recognition, Deep Learning, Convolutional Neural Network, Real-Time Gesture Detection, Assistive Technology
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Dubey, Anuj. "Real Time Sign Language Interpreter Using Live Video Feed using Deep Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem45616.
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Abstract - This paper presents a real-time sign language interpreter system designed to bridge the communication gap between Deaf/non-verbal individuals and non-signers. The proposed system captures hand gestures through a standard webcam and utilizes a Convolutional Neural Network (CNN) for accurate gesture recognition. Preprocessing steps such as Histogram of Oriented Gradients (HOG) and Gaussian Mixture Models (GMM) are used for background elimination and feature extraction. The recognized gestures are translated into both text and speech, enabling seamless interaction in real-world environments. The system also features a user-friendly interface and a modular web-based platform for future expansion. Unlike traditional glove-based or high-complexity models, this solution is lightweight, cost-effective, and deployable on standard hardware, making it highly accessible for educational, professional, and assistive applications. Key Words: Sign Language Recognition, Deep Learning, Convolutional Neural Network, Real-Time Gesture Detection, Assistive Technology
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Theresa, W. Gracy, S. Santhana Prabha, D. Thilagavathy, and S. Pournima. "Analysis of the Efficacy of Real-Time Hand Gesture Detection with Hog and Haar-Like Features Using SVM Classification." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 2s (2022): 199–207. http://dx.doi.org/10.17762/ijritcc.v10i2s.5929.
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The field of hand gesture recognition has recently reached new heights thanks to its widespread use in domains like remote sensing, robotic control, and smart home appliances, among others. Despite this, identifying gestures is difficult because of the intransigent features of the human hand, which make the codes used to decode them illegible and impossible to compare. Differentiating regional patterns is the job of pattern recognition. Pattern recognition is at the heart of sign language. People who are deaf or mute may understand the spoken language of the rest of the world by learning sign language. Any part of the body may be used to create signs in sign language. The suggested system employs a gesture recognition system trained on Indian sign language. The methods of preprocessing, hand segmentation, feature extraction, gesture identification, and classification of hand gestures are discussed in this work as they pertain to hand gesture sign language. A hybrid approach is used to extract the features, which combines the usage of Haar-like features with the application of Histogram of Oriented Gradients (HOG).The SVM classifier is then fed the characteristics it has extracted from the pictures in order to make an accurate classification. A false rejection error rate of 8% is achieved while the accuracy of hand gesture detection is improved by 93.5%.
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Suyitno, Yoga Kristian, I. Wayan Sudiarsa, I. Nyoman Buda Hartawan, and I. Dewa Putu Gede Wiyata Putra. "Implementation of Sibi Sign Language Realtime Detection Program (Case Studi At Sekolah Luar Biasa Negeri 1 Tabanan)." Journal of Computer Networks, Architecture and High Performance Computing 6, no. 3 (2024): 1431–41. http://dx.doi.org/10.47709/cnahpc.v6i3.4405.
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Indonesian deaf people utilize SIBI to communicate using spoken words, gestures, facial expressions, and body language. SIBI, certified for Special Schools (SLB), helps deaf pupils communicate. This project implements SIBI (Indonesian Sign Language System) a real-time detection algorithm at Sekolah Luar Biasa Negeri 1 Tabanan using image processing and YoloV8 ultralytics deep learning. The program trains a sign language gesture detection model on Google Colab's GPU. The SIBI sign language images were used to train a YoloV8 object detection model. The camera captures movements, which the YoloV8 algorithm trained on SIBI gesture data processes. It can recognize gestures in real time and generate text to non-sign language users. The dataset has 107 class vocabulary and 7 class affix prefixes for complete gesture recognition. Shirt color, room brightness, and webcam quality affect detection rates. Optimal detection accuracy is 87.74% and subpar 58.02%. Despite these limitations, the strategy helps deaf students communicate more effectively with non-sign language speakers. This program improves inclusivity and communication in schools, making learning easier for hearing-impaired pupils. This work provides a reliable and quick sign language identification system to help deaf educators and caregivers with daily interactions and education.
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Dunai, Larisa, Isabel Seguí Verdú, Dinu Turcanu, and Viorel Bostan. "Prosthetic Hand Based on Human Hand Anatomy Controlled by Surface Electromyography and Artificial Neural Network." Technologies 13, no. 1 (2025): 21. https://doi.org/10.3390/technologies13010021.
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Humans have a complex way of expressing their intuitive intentions in real gestures. That is why many gesture detection and recognition techniques have been studied and developed. There are many methods of human hand signal reading, such as those using electroencephalography, electrocorticography, and electromyography, as well as methods for gesture recognition. In this paper, we present a method based on real-time surface electroencephalography hand-based gesture recognition using a multilayer neural network. For this purpose, the sEMG signals have been amplified, filtered and sampled; then, the data have been segmented, feature extracted and classified for each gesture. To validate the method, 100 signals for three gestures with 64 samples each signal have been recorded from 2 users with OYMotion sensors and 100 signals for three gestures from 4 users with the MyWare sensors. These signals were used for feature extraction and classification using an artificial neuronal network. The model converges after 10 sessions, achieving 98% accuracy. As a result, an algorithm was developed that aimed to recognize two specific gestures (handling a bottle and pointing with the index finger) in real time with 95% accuracy.
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Guo, Jiang, Jun Cheng, Yu Guo, and Jian Xin Pang. "A Real-Time Dynamic Gesture Recognition System." Applied Mechanics and Materials 333-335 (July 2013): 849–55. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.849.
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In this paper, we present a dynamic gesture recognition system. We focus on the visual sensory information to recognize human activity in form of hand movements from a small, predefined vocabulary. A fast and effective method is presented for hand detection and tracking at first for the trajectory extraction. A novel trajectory correction method is applied for simply but effectively trajectory correction. Gesture recognition is achieved by means of a matching technique by determining the distance between the unknown input direction code sequence and a set of previously defined templates. A dynamic time warping (DTW) algorithm is used to perform the time alignment and normalization by computing a temporal transformation allowing the two signals to be matched. Experiment results show our proposed gesture recognition system achieve well result in real time.
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Mujahid, Abdullah, Mazhar Javed Awan, Awais Yasin, et al. "Real-Time Hand Gesture Recognition Based on Deep Learning YOLOv3 Model." Applied Sciences 11, no. 9 (2021): 4164. http://dx.doi.org/10.3390/app11094164.
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Using gestures can help people with certain disabilities in communicating with other people. This paper proposes a lightweight model based on YOLO (You Only Look Once) v3 and DarkNet-53 convolutional neural networks for gesture recognition without additional preprocessing, image filtering, and enhancement of images. The proposed model achieved high accuracy even in a complex environment, and it successfully detected gestures even in low-resolution picture mode. The proposed model was evaluated on a labeled dataset of hand gestures in both Pascal VOC and YOLO format. We achieved better results by extracting features from the hand and recognized hand gestures of our proposed YOLOv3 based model with accuracy, precision, recall, and an F-1 score of 97.68, 94.88, 98.66, and 96.70%, respectively. Further, we compared our model with Single Shot Detector (SSD) and Visual Geometry Group (VGG16), which achieved an accuracy between 82 and 85%. The trained model can be used for real-time detection, both for static hand images and dynamic gestures recorded on a video.
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Wang, Bin, Ruiqi Zhang, Chong Xi, Jing Sun, and Xiaochun Yang. "Virtual and Real-Time Synchronous Interaction for Playing Table Tennis with Holograms in Mixed Reality." Sensors 20, no. 17 (2020): 4857. http://dx.doi.org/10.3390/s20174857.
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Real-time and accurate interaction technology is required to realize new wearable Mixed Reality (MR) solutions. At present, the mainstream interaction method relies on gesture detection technology, which has two shortcomings: 1. the hand feature points may easily be obstructed by obstacles and cannot be detected and 2. the kinds of gesture that can be recognized are limited. Hence, it cannot support complex interactions well. Moreover, the traditional collision detection algorithm has difficulty detecting the collision between real and virtual objects under motion. Because location information of real objects needs updating in real time, it is easy to lose collision detection under high speeds. In the implementation of our system, Mixed Reality Table Tennis System, we propose novel methods which overcome these shortcomings. Instead of using gesture detection technology, we use a locator as the main input device and build a data exchange channel for the devices, so that the system can update the motion state of the racket in real time. Besides, we adjust the thickness of the collider dynamically to solve the collision detection problem and calculate rebound results responding to the motion state of the racket and the ball. Experimental results show that our method avoids losing collision detection and improves the authenticity of simulation. It keeps good interaction in real time.
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Zhuang, Hongchao, Yilu Xia, Ning Wang, and Lei Dong. "High Inclusiveness and Accuracy Motion Blur Real-Time Gesture Recognition Based on YOLOv4 Model Combined Attention Mechanism and DeblurGanv2." Applied Sciences 11, no. 21 (2021): 9982. http://dx.doi.org/10.3390/app11219982.
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The combination of gesture recognition and aerospace exploration robots can realize the efficient non-contact control of the robots. In the harsh aerospace environment, the captured gesture images are usually blurred and damaged inevitably. The motion blurred images not only cause part of the transmitted information to be lost, but also affect the effect of neural network training in the later stage. To improve the speed and accuracy of motion blurred gestures recognition, the algorithm of YOLOv4 (You Only Look Once, vision 4) is studied from the two aspects of motion blurred image processing and model optimization. The DeblurGanv2 is employed to remove the motion blur of the gestures in YOLOv4 network input pictures. In terms of model structure, the K-means++ algorithm is used to cluster the priori boxes for obtaining the more appropriate size parameters of the priori boxes. The CBAM attention mechanism and SPP (spatial pyramid pooling layer) structure are added to YOLOv4 model to improve the efficiency of network learning. The dataset for network training is designed for the human–computer interaction in the aerospace space. To reduce the redundant features of the captured images and enhance the effect of model training, the Wiener filter and bilateral filter are superimposed on the blurred images in the dataset to simply remove the motion blur. The augmentation of the model is executed by imitating different environments. A YOLOv4-gesture model is built, which collaborates with K-means++ algorithm, the CBAM and SPP mechanism. A DeblurGanv2 model is built to process the input images of the YOLOv4 target recognition. The YOLOv4-motion-blur-gesture model is composed of the YOLOv4-gesture and the DeblurGanv2. The augmented and enhanced gesture data set is used to simulate the model training. The experimental results demonstrate that the YOLOv4-motion-blur-gesture model has relatively better performance. The proposed model has the high inclusiveness and accuracy recognition effect in the real-time interaction of motion blur gestures, it improves the network training speed by 30%, the target detection accuracy by 10%, and the value of mAP by about 10%. The constructed YOLOv4-motion-blur-gesture model has a stable performance. It can not only meet the real-time human–computer interaction in aerospace space under real-time complex conditions, but also can be applied to other application environments under complex backgrounds requiring real-time detection.
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Lin, Weikun. "A Systematic Review of Computer Vision-Based Virtual Conference Assistants and Gesture Recognition." Journal of Computer Technology and Applied Mathematics 1, no. 4 (2024): 28–35. https://doi.org/10.5281/zenodo.13889718.
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In the process of introducing gesture recognition, it is essential to explore its technical background and implementation methods. Gesture recognition algorithms based on deep learning perform exceptionally well when processing real-time video streams. These algorithms can extract gesture features and classify them to identify user intentions. For instance, analyzing gesture images using Convolutional Neural Networks (CNN) can effectively enhance recognition accuracy and real-time performance. Additionally, combining optical flow methods with object detection techniques allows for real-time tracking of user hand movements, leading to more precise recognition results. Factors such as changes in ambient lighting, cluttered backgrounds, and the diversity of user gestures can all impact recognition accuracy. Therefore, researchers need to continuously optimize algorithms to improve the robustness and adaptability of the system. At the same time, when designing virtual conference assistants, the user interface's friendliness and usability should also be considered, enabling users of varying technical skill levels to use the system with ease.
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Song, Lin, Rui Min Hu, Hua Zhang, Yu Lian Xiao, and Li Yu Gong. "Real-Time 3D Hand Gesture Detection from Depth Images." Advanced Materials Research 756-759 (September 2013): 4138–42. http://dx.doi.org/10.4028/www.scientific.net/amr.756-759.4138.
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In this paper, we describe an real-time algorithm to detect 3D hand gestures from depth images. Firstly, we detect moving regions by frame difference; then, regions are refined by removing small regions and boundary regions; finally, foremost region is selected and its trajectories are classified using an automatic state machine. Experiments on Microsoft Kinect for Xbox captured sequences show the effectiveness and efficiency of our system.
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N, Prashanth, K. T. Anchan Poovanna, Gaurav Raj, Preetham S, and Qurrath Ul Ayen. "Gesture Recognition for Interactive Presentation Control: A Deep Learning and Edge Computing Approach on Raspberry Pi." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–9. https://doi.org/10.55041/ijsrem39636.
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Gesture recognition is one of the important tech- nologies for intuitive HCI applications, for ex- ample, in interactive presentations. This re- view focuses on recent work on gesture recog- nition systems, deep learning-based methods for presentation control by hand gestures, on edge devices like the Raspberry Pi. This work de- scribes both static and dynamic frameworks for gesture recognition and human-machine interac- tion techniques based on deep learning.Recent studies show the usability of real-time hand gesture recognition using Python-based systems via Raspberry Pi, stressing portability and ef- ficiency in solutions. The review discusses var- ious deep learning frameworks such as Tensor- Flow, which can be used in image preprocess- ing and model training to enhance the accuracy of gesture detection. Techniques cover wear- able devices to computer vision and demon- strate the flexibility of gesture recognition in a variety of hardware platforms. Advances in edge computing enable complex gesture recog- nition on low- power devices, with reduced la- tency, and enables improved accessibility.The re- view addresses challenges such as gesture type distinction, responsiveness, and environmental variability in developing reliable gesture-based systems. In this regard, it emphasizes the Rasp- berry Pi as an edge computing solution for in- teractive presentation control in HCI. Keywords Gesture Recognition Human-Computer Inter- action (HCI) Edge Computing Deep Learning Real-Time Gesture Detection
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Nikita Kashyap, Pragati Patharia, and Arun Kumar Kashyap. "Gesture-Based Control of Multimedia Player Using Python and OpenCV." International Journal of Advanced Technology and Social Sciences 1, no. 4 (2023): 267–78. http://dx.doi.org/10.59890/ijatss.v1i4.983.
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The increasing significance of computers in our daily lives, coupled with the rise of ubiquitous computing, has necessitated effective human-computer interaction. Hand gesture recognition systems have emerged as a real-time video-based solution for detecting and interpreting hand gestures, offering intelligent and natural human-computer interaction (HCI) methods. This project focuses on leveraging human hands as input devices for computer operation. Developed using Python and the OpenCV library, the program utilizes a computer webcam to capture and analyze hand shapes and patterns. The program provides real-time feedback by displaying recognized hand gestures on the live video stream. The ultimate outcome of this project is an application that enhances user experiences in contactless systems. The project recognizes and detects human hand motions using the Python computer language through a process flow that includes background subtraction, hand ROI segmentation, contour detection, and finger recognition. Techniques for processing images are used, including hand gesture detection, pattern recognition, thresholding, and contour detection. The processing of incoming photos and the creation of related keystrokes are made possible by OpenCV, a rich set of image processing tools
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Kashyap, Nikita, Pragati Patharia, and Arun Kumar Kashyap. "Gesture-Based Control of Multimedia Player Using Python and OpenCV." Gesture-Based Control of Multimedia Player Using Python and OpenCV 1, Vol. 1 No. 4 (2023): December 2023 (2024): 12. https://doi.org/10.59890/ijatss.v1i4.983.
Full textAbstract:
The increasing significance of computers in our daily lives, coupled with the rise of ubiquitous computing, has necessitated effective human-computer interaction. Hand gesture recognition systems have emerged as a real-time video-based solution for detecting and interpreting hand gestures, offering intelligent and natural human-computer interaction (HCI) methods. This project focuses on leveraging human hands as input devices for computer operation. Developed using Python and the OpenCV library, the program utilizes a computer webcam to capture and analyze hand shapes and patterns. The program provides real-time feedback by displaying recognized hand gestures on the live video stream. The ultimate outcome of this project is an application that enhances user experiences in contactless systems. The project recognizes and detects human hand motions using the Python computer language through a process flow that includes background subtraction, hand ROI segmentation, contour detection, and finger recognition. Techniques for processing images are used, including hand gesture detection, pattern recognition, thresholding, and contour detection. The processing of incoming photos and the creation of related keystrokes are made possible by OpenCV, a rich set of image processing tools
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Yaseen, Oh-Jin Kwon, Jaeho Kim, Jinhee Lee, and Faiz Ullah. "Vision-Based Gesture-Driven Drone Control in a Metaverse-Inspired 3D Simulation Environment." Drones 9, no. 2 (2025): 92. https://doi.org/10.3390/drones9020092.
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Unlike traditional remote control systems for controlling unmanned aerial vehicles (UAVs) and drones, active research is being carried out in the domain of vision-based hand gesture recognition systems for drone control. However, contrary to static and sensor based hand gesture recognition, recognizing dynamic hand gestures is challenging due to the complex nature of multi-dimensional hand gesture data, present in 2D images. In a real-time application scenario, performance and safety is crucial. Therefore we propose a hybrid lightweight dynamic hand gesture recognition system and a 3D simulator based drone control environment for live simulation. We used transfer learning-based computer vision techniques to detect dynamic hand gestures in real-time. The gestures are recognized, based on which predetermine commands are selected and sent to a drone simulation environment that operates on a different computer via socket connectivity. Without conventional input devices, hand gesture detection integrated with the virtual environment offers a user-friendly and immersive way to control drone motions, improving user interaction. Through a variety of test situations, the efficacy of this technique is illustrated, highlighting its potential uses in remote-control systems, gaming, and training. The system is tested and evaluated in real-time, outperforming state-of-the-art methods. The code utilized in this study are publicly accessible. Further details can be found in the “Data Availability Statement”.
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Elmezain, Mahmoud, Majed M. Alwateer, Rasha El-Agamy, Elsayed Atlam, and Hani M. Ibrahim. "Forward Hand Gesture Spotting and Prediction Using HMM-DNN Model." Informatics 10, no. 1 (2022): 1. http://dx.doi.org/10.3390/informatics10010001.
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Automatic key gesture detection and recognition are difficult tasks in Human–Computer Interaction due to the need to spot the start and the end points of the gesture of interest. By integrating Hidden Markov Models (HMMs) and Deep Neural Networks (DNNs), the present research provides an autonomous technique that carries out hand gesture spotting and prediction simultaneously with no time delay. An HMM can be used to extract features, spot the meaning of gestures using a forward spotting mechanism with varying sliding window sizes, and then employ Deep Neural Networks to perform the recognition process. Therefore, a stochastic strategy for creating a non-gesture model using HMMs with no training data is suggested to accurately spot meaningful number gestures (0–9). The non-gesture model provides a confidence measure, which is utilized as an adaptive threshold to determine where meaningful gestures begin and stop in the input video stream. Furthermore, DNNs are extremely efficient and perform exceptionally well when it comes to real-time object detection. According to experimental results, the proposed method can successfully spot and predict significant motions with a reliability of 94.70%.
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Mayank, Anmol, and Asmika Jain. "SignNet- Hand Sign Detection." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 04 (2025): 1–9. https://doi.org/10.55041/ijsrem44390.
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Sign language is a critical communication tool for individuals with hearing and speech impairments, yet its limited understanding among the general population creates significant social and professional barriers. This research proposes a real- time hand sign detection system that leverages computer vision and deep learning to bridge this gap, enabling seamless interaction between sign language users and non-users. The system employs Convolutional Neural Networks (CNNs) for spatial feature extraction and Recurrent Neural Networks (RNNs) for temporal sequence modeling, achieving accurate recognition of hand gestures and their conversion into text or speech. By processing video input at 20–30 frames per second, the system ensures efficient real-time performance suitable for everyday use. Preliminary evaluations suggest an accuracy of 85–95% on a vocabulary of 100–200 signs, with potential scalability to larger datasets. This solution has wide-ranging applications, including education, healthcare, and customer service, fostering inclusivity and accessibility. By addressing the challenges of gesture variability and processing latency, this work advances the development of automated sign language interpretation, paving the way for more equitable communication in diverse settings. Keywords— Sign Language Recognition, Convolutional Neural Networks (CNNs), Hand Gesture Detection, Recurrent Neural Networks (RNNs), Deep Learning, Real-Time Processing.
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Nadar, Daswini, Saista Anjum, and K. C. Sriharipriya. "Hand Gesture Recognition System based on 60 GHz FMCW Radar and Deep Neural Network." International Journal of Electrical and Electronics Research 11, no. 3 (2023): 760–65. http://dx.doi.org/10.37391/ijeer.110319.
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The proposed study provides a novel technique for recognizing hand gestures that use a combination of Deep Convolutional Neural Networks (DCNN) and 60 GHz Frequency Modulated Continuous Wave (FMCW) radar. The motion of a Human's hand is detected using the FMCW radar, and the various gestures are classified using the DCNN. Motion detection and frequency analysis are two techniques that the suggested system combines. The basis of the capability of motion detection in FMCW radars' is to recognize the Doppler shift in the received signal brought on by the target's motion. To properly identify the hand motions, the presented technique combines these two techniques. The system is analyzed using a collection of hand gesture photos, and the outcomes are analyzed with those of other hand gesture recognition systems which are already in use. A dataset of five different hand gestures is used to examine the proposed system. According to the experimental data, the suggested system can recognize gestures with an accuracy of 96.5%, showing its potential as a productive gesture recognition system. Additionally, the suggested system has a processing time of 100 ms and can run in real time. The outcomes also demonstrate the proposed system's resistance to noise and its ability to recognize gestures in a variety of configurations. For gesture detection applications in virtual reality and augmented reality systems, this research offers a promising approach.
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Sravani, Jiripurapu, Soma Yagna Priya, Gowra Pavan Kumar, Chereddy Mohith Sankar, and KRMC Sekhar. "Hand Gesture Detection using Deep Learning with YOLOv5." International Journal of Multidisciplinary Research and Growth Evaluation. 6, no. 2 (2025): 742–50. https://doi.org/10.54660/.ijmrge.2025.6.2.742-750.
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Hand gesture recognition has become a significant technological advancement in assistive communication, offering a reliable means of interaction for individuals with hearing and speech impairments. This research introduces an intelligent gesture detection system powered by YOLOv5, a leading object detection model, to enable accurate and real-time recognition of Indian Sign Language (ISL) gestures. The system effectively handles diverse environmental conditions and user-specific variations using an extensive and well-annotated dataset. The methodology encompasses essential stages such as image preprocessing, data augmentation, and feature extraction to optimize model performance. Furthermore, a user-friendly web interface allows users to upload images for gesture detection, with corresponding text and audio outputs generated using a text-to-speech module. Designed for seamless scalability, the system can accommodate additional gestures and languages, making it a versatile solution for educational institutions, healthcare facilities, and public service sectors. By fostering greater inclusivity and accessibility, this approach represents a step forward in empowering the hearing-impaired community through innovative deep-learning applications.
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Yokota, Masae, Soichiro Majima, Sarthak Pathak, and Kazunori Umeda. "Unconstrained Home Appliance Operation by Detecting Pointing Gestures from Multiple Camera Views." Journal of Robotics and Mechatronics 37, no. 2 (2025): 500–509. https://doi.org/10.20965/jrm.2025.p0500.
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In this paper, we propose a method for manipulating home appliances using arm-pointing gestures. Conventional gesture-based methods are limited to home appliances with known locations or are device specific. In the proposed method, the locations of home appliances and users can change freely. Our method uses object- and keypoint-detection algorithms to obtain the positions of the appliance and operator in real time. Pointing gestures are used to operate the device. In addition, we propose a start gesture algorithm to make the system robust against accidental gestures. We experimentally demonstrated that using the proposed method, home appliances can be operated with high accuracy and robustness, regardless of their location or the user’s location in real environments.
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Nuthan A C, Dr. "Health Monitoring for Stroke Patients Using Machine Learning." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem49039.
Full textAbstract:
Abstract - Stroke patients often face challenges in communication and independent medication management, necessitating innovative assistive technologies for better healthcare monitoring. This project introduces a real-time hand gesture-based system to monitor tablet intake in stroke patients using computer vision and machine learning techniques. The solution integrates OpenCV for video processing, Media Pipe for accurate hand landmark detection, and Twilio for sending automated SMS alerts to caregivers. The system operates by detecting specific hand gestures-particularly the movement of the index finger toward the mouth-which signifies an attempt to consume medication. When this gesture is confirmed, the system logs the event, updates the tablet count, and triggers both a visual alert using Tkinter and an SMS notification via Twilio. This ensures real-time tracking and caregiver awareness without physical intervention. This low-cost, non-invasive solution can be implemented using a standard webcam and basic hardware, making it especially suitable for home healthcare environments. The methodology enhances patient autonomy while maintaining safety and accountability. Future developments may incorporate deep learning for more robust gesture recognition and extend the system’s functionality to cover broader health monitoring scenarios. Key Words: Hand Gesture Recognition, Stroke Patient Monitoring, Computer Vision, Real-time Alert System. .