Sign Language Digit Detection with MediaPipe and Machine Learning Algorithm

Salim, Safyzan; Jamil, Muhammad Mahadi Abdul; Ambar, Radzi; Roslan, Rozaini; Kamardan, Muhammad Ghazali

doi:10.1109/iccsce54767.2022.9935659

Cited by 5 publications

(1 citation statement)

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“…The code for capturing the image starts by installing both library package files: cvzone and mediapipe. Installing cvzone is for applying computer vision as it reads and captures images [5], while mediapipe generates a real-time perception pipeline, which is very useful for reading finger gestures and hand tracking [6].…”

Section: Capturing Image and Hand Gesture Recognitionmentioning

confidence: 99%

Application of Machine Learning in Malaysia Sign Language Translation

Ramli,

Yee

2023

EmAIT

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Sign language serves as a vital form of communication for individuals with hearing impairments, enabling seamless interaction among those who cannot hear. It is a widely used means of communication worldwide, facilitating communication within the deaf community. In Malaysia, Malaysian Sign Language (MySL) prevails as the primary sign language employed by the deaf community. However, sign languages possess unique grammatical rules and structures, making them unfamiliar to hearing individuals, leading to potential misunderstandings and communication barriers. This project aimed at developing a sign language translator capable of translating 24 alphabets based on hand gestures. The system employs a dataset of sign language alphabet images, gathered and trained using the Teachable Machine. To evaluate the translator's performance, response time is thoroughly analyzed. The results indicate that 18 out of the 24 alphabets can be recognized within 5 seconds, displaying promising accuracy. By bridging the communication gap between deaf and hearing individuals, the findings of this study hold substantial potential to enhance interactions and foster better understanding between these two communities. The sign language translator represents a significant step towards inclusive communication and improved accessibility for individuals with hearing impairments

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Section: Capturing Image and Hand Gesture Recognitionmentioning

confidence: 99%