HandKey: An Efficient Hand Typing Recognition using CNN for Virtual Keyboard

Enkhbat, Avirmed; Shih, Timothy K.; Thaipisutikul, Tipajin; Hakim, Noorkholis Luthfil; Aditya, Wisnu

doi:10.1109/incit50588.2020.9310783

Cited by 9 publications

(2 citation statements)

References 9 publications

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“…For the two gestures case, the proposed CNN model achieves a classification accuracy of 99.2%, and for the eleven gestures case, it achieves a classification accuracy of 91%. [4] The brain's bioelectrical signals are collected by a computer-based system called a "Brian Computer Interface" (BCI), which then analyses and transforms those signals into commands that execute the user's intent. This study develops a BCI-based virtual keyboard with 36 keys, including 26 English alphabet keys (A-Z), 7 special characters, and 3 action keys, which can be operated by bioelectrical brain waves.…”

Section: Literature Surveymentioning

confidence: 99%

Exploring The Techniques and Applications of Hand Gesture Recognition for Human-Computer Interaction

Khada

2023

IJSREM

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Hand gesture controlled applications are software applications that are controlled by the user's hand movements. Hand gesture controlled applications have the potential to improve user experience by offering more intuitive and immersive ways to interact with system. Hand gesture controlled applications have a huge scope, including gaming, virtual reality, robotics, healthcare, and various other sectors. In this paper, we have combined several disparate end results of subsystems such as virtual mouse, contactless keyboard, finger counting, taking selfies, sign language, game, handling installed applications and volume control using computer vision. For implementation, we used the open-source software library OpenCV, mediapipe and pyautogui. Various hand gestures are assigned to launch and control different applications. These hand gestures are recognized using machine learning trained models, Euclidean distance formula and pixel positions. Overall, it allows humans to interact with computers through hand gestures rather than traditional input/output mechanisms such as the keyboard and mouse, improving accessibility. Key Words: Computer vision, OpenCV, hand gesture, machine learning, TensorFlow, pyautogui, mediapipe, virtual mouse, virtual keyboard, sign language, gaming.

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Section: Literature Surveymentioning

confidence: 99%

Exploring The Techniques and Applications of Hand Gesture Recognition for Human-Computer Interaction

Khada

2023

IJSREM

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“…Enkhbat et al [23] created an efficient framework that recognizes hand typing motions and gestures for making a virtual keyboard using a single RGB (Red, Green, Blue) camera. The paper uses CNN to recognize the typing motion and scored highly accurate results.…”

Section: Neural Network and Deep Learningmentioning

confidence: 99%

Note Frequency Recognition and Finger Motion Capture of a Guitarist: A Survey and Challenges Ahead

Emad

Sobeah

Hassan

et al. 2022

Journal of Computing and Communication

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One of the main issues that face new guitar aspirants is that there is a lot of further information, which proves a lot to take in for just a beginner as it causes much-disliked confusion. Students also face problems with their left-hand motion and correct pitch frequency. Researchers have tackled this problem in many ways and have mainly landed on using two approaches. The first was Finger Motion Capture, where previous research specialized in analyzing images and videos of the guitarist. The second was Note Frequency Recognition, where the research's primary purpose was to examine the sound and audio recorded by the guitarist. This paper surveys all the approaches taken to solve this problem, discussing them in detail and exploring the challenges faced with each approach. Furthermore, this paper proposes a hybrid solution that includes both Note Frequency Recognition and Finger Motion Capture to make a full assessment and give feedback on a guitarist's performance.

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A Survey on 3D Hand Detection and Tracking Algorithms for Human Computer Interfacing

Bajaj,

Rajpal,

Abraham

2023

Lecture Notes in Networks and Systems

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HandKey: An Efficient Hand Typing Recognition using CNN for Virtual Keyboard

Cited by 9 publications

References 9 publications

Exploring The Techniques and Applications of Hand Gesture Recognition for Human-Computer Interaction

Exploring The Techniques and Applications of Hand Gesture Recognition for Human-Computer Interaction

Note Frequency Recognition and Finger Motion Capture of a Guitarist: A Survey and Challenges Ahead

A Survey on 3D Hand Detection and Tracking Algorithms for Human Computer Interfacing

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