Baseemah Mat Jalaluddin
Thumbnail Image
Preferred name
Baseemah Mat Jalaluddin
Official Name
Baseemah, Mat Jalaluddin
Alternative Name
Jalaluddin, B. Mat
Main Affiliation
Scopus Author ID
57221201459

Research Output

Filters

1
1
1
Reset filters

Settings
Now showing 1 - 1 of 1
  • Publication
    Real-time vision-based hand gesture to text interpreter by using artificial intelligence with augmented reality element
    ( 2024-03-07)
    Rosnazri M.H.
    ;
    Mohd Rizal Manan
    ;
    Rosnazri Ali
    ;
    Baseemah Mat Jalaluddin
    ;
    Aimi Salihah Abdul Nasir
    ;
    Muhammad Aizat Abu Bakar
    ;
    Zamri N.F.
    ;
    Rahmat M.A.
    ;
    Zamzuri M.A.
    ;
    Azmi M.A.A.
    Real-time Vision-based Hand Gesture to Text Interpreter by Using Artificial Intelligence with Augmented Reality Element is a device that can interpret sign language to text in real-time. This communicator used a machine learning approach with a slight touch of deep learning elements, which are OpenCV, MediaPipe, and TensorFlow algorithms. Those algorithms have been used to differentiate the hand from other objects, detect the movement and coordinate of hands and perform imagery data analysis to produce output instantly in real-time. The camera will detect the user's hand movement, and the output will be produced on an LCD monitor. This project has been developed by using Python programming language. 13,000 of ASL's alphabets and 5,000 of ASL's number imagery datasets have been collected and trained by using cloud platforms which are Google Teachable Machine and Google Colab. The training process produced 99.85% of accuracy for the alphabets and 100% accuracy for the number. Finally, the constructed machine learning algorithm able to display alphabets and numbers on an LCD monitor by performing ASL's alphabet and number hand gesture in real-time. The performance of the prototype has been analyzed and experimented by two users at plain and noise background with different determined distances.
      1