This document presents a study on sign language identification based on hand gestures. The researchers aim to develop a system that can recognize American Sign Language gestures from video sequences. They use two different models - a Convolutional Neural Network (CNN) to analyze the spatial features of video frames, and a Recurrent Neural Network (RNN) to analyze the temporal features across frames. The document discusses the methodology used, including data collection from videos, pre-processing of frames, feature extraction using CNN models, and gesture classification. It also provides a literature review on previous studies related to sign language recognition and communication systems for deaf people.
Mems Sensor Based Approach for Gesture Recognition to Control Media in ComputerIJARIIT
Gesture Recognition is the method of identifying and understanding meaningful movements of the arms, hands,
face, or sometimes head. It is one of the most important aspects in the field of Human-Computer interface. There has been a
continuous research in this field because of its ability for application in user interfaces. Gesture Recognition is one of the
important areas of research for engineers and scientists. Nowadays the industry is working on the different implementation for
the trouble free, natural and easy product which can be easy to handle. This paper proposed a method to work with motion
sensors and interpret the motion of hand into various applications in a virtual interface. The Micro-Electro-Mechanical
Systems (MEMS) accelerometers are used to capture the dynamic hand gesture. These sensors information is transferred to
the microcontroller from where these data are transferred wirelessly to the computer system for actual processing of the data
with the use of various algorithms.
Gesture Recognition Technology has evolved greatly over the years. The past has seen the contemporary Human Computer Interface techniques and their drawbacks, which limit the speed and naturalness of the human brain and body. As a result gesture recognition technology has developed
Real Time Vision Hand Gesture Recognition Based Media Control via LAN & Wirel...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Development of Sign Signal Translation System Based on Altera’s FPGA DE2 BoardWaqas Tariq
The main aim of this paper is to build a system that is capable of detecting and recognizing the hand gesture in an image captured by using a camera. The system is built based on Altera’s FPGA DE2 board, which contains a Nios II soft core processor. Image processing techniques and a simple but effective algorithm are implemented to achieve this purpose. Image processing techniques are used to smooth the image in order to ease the subsequent processes in translating the hand sign signal. The algorithm is built for translating the numerical hand sign signal and the result are displayed on the seven segment display. Altera’s Quartus II, SOPC Builder and Nios II EDS software are used to construct the system. By using SOPC Builder, the related components on the DE2 board can be interconnected easily and orderly compared to traditional method that requires lengthy source code and time consuming. Quartus II is used to compile and download the design to the DE2 board. Then, under Nios II EDS, C programming language is used to code the hand sign translation algorithm. Being able to recognize the hand sign signal from images can helps human in controlling a robot and other applications which require only a simple set of instructions provided a CMOS sensor is included in the system.
Sign language SL is commonly considered as the primary gesture based language for deaf and dumb people. It is a medium of communication for such people. Basically image based and sensor based are the two important sign language recognition methods. Because of the difficulties in wearing complex devices like Hand Gloves, armbands, helmets etc. in sensor based approaches, lots of researches are done by companies and researchers on image based approaches. Sign language is used by these people to communicate with the normal people. Understanding this sign language is a difficult task according to the normal people. To address these difficulties, a real time translator for sign language using deep learning DL is introduced. It enables to reduce the limitations and cons of other methods to a greater extent. With the help of this real time translator, communication will be better and fast without causing any delay. Jeni Moni | Anju J Prakash "Real Time Translator for Sign Language" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32915.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32915/real-time-translator-for-sign-language/jeni-moni
Mems Sensor Based Approach for Gesture Recognition to Control Media in ComputerIJARIIT
Gesture Recognition is the method of identifying and understanding meaningful movements of the arms, hands,
face, or sometimes head. It is one of the most important aspects in the field of Human-Computer interface. There has been a
continuous research in this field because of its ability for application in user interfaces. Gesture Recognition is one of the
important areas of research for engineers and scientists. Nowadays the industry is working on the different implementation for
the trouble free, natural and easy product which can be easy to handle. This paper proposed a method to work with motion
sensors and interpret the motion of hand into various applications in a virtual interface. The Micro-Electro-Mechanical
Systems (MEMS) accelerometers are used to capture the dynamic hand gesture. These sensors information is transferred to
the microcontroller from where these data are transferred wirelessly to the computer system for actual processing of the data
with the use of various algorithms.
Gesture Recognition Technology has evolved greatly over the years. The past has seen the contemporary Human Computer Interface techniques and their drawbacks, which limit the speed and naturalness of the human brain and body. As a result gesture recognition technology has developed
Real Time Vision Hand Gesture Recognition Based Media Control via LAN & Wirel...IJMER
International Journal of Modern Engineering Research (IJMER) is Peer reviewed, online Journal. It serves as an international archival forum of scholarly research related to engineering and science education.
Development of Sign Signal Translation System Based on Altera’s FPGA DE2 BoardWaqas Tariq
The main aim of this paper is to build a system that is capable of detecting and recognizing the hand gesture in an image captured by using a camera. The system is built based on Altera’s FPGA DE2 board, which contains a Nios II soft core processor. Image processing techniques and a simple but effective algorithm are implemented to achieve this purpose. Image processing techniques are used to smooth the image in order to ease the subsequent processes in translating the hand sign signal. The algorithm is built for translating the numerical hand sign signal and the result are displayed on the seven segment display. Altera’s Quartus II, SOPC Builder and Nios II EDS software are used to construct the system. By using SOPC Builder, the related components on the DE2 board can be interconnected easily and orderly compared to traditional method that requires lengthy source code and time consuming. Quartus II is used to compile and download the design to the DE2 board. Then, under Nios II EDS, C programming language is used to code the hand sign translation algorithm. Being able to recognize the hand sign signal from images can helps human in controlling a robot and other applications which require only a simple set of instructions provided a CMOS sensor is included in the system.
Sign language SL is commonly considered as the primary gesture based language for deaf and dumb people. It is a medium of communication for such people. Basically image based and sensor based are the two important sign language recognition methods. Because of the difficulties in wearing complex devices like Hand Gloves, armbands, helmets etc. in sensor based approaches, lots of researches are done by companies and researchers on image based approaches. Sign language is used by these people to communicate with the normal people. Understanding this sign language is a difficult task according to the normal people. To address these difficulties, a real time translator for sign language using deep learning DL is introduced. It enables to reduce the limitations and cons of other methods to a greater extent. With the help of this real time translator, communication will be better and fast without causing any delay. Jeni Moni | Anju J Prakash "Real Time Translator for Sign Language" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-4 | Issue-5 , August 2020, URL: https://www.ijtsrd.com/papers/ijtsrd32915.pdf Paper Url :https://www.ijtsrd.com/computer-science/other/32915/real-time-translator-for-sign-language/jeni-moni
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