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American Sign Language Recognition Using Leap Motion Controller with Machine Learning Approach.

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This study developed a sign language recognition prototype using the Leap Motion Controller (LMC) for full American Sign Language (ASL) recognition. The deep neural network (DNN) achieved 93.81% accuracy for letters, showing potential to bridge communication gaps.

Keywords:
American Sign LanguageLeap Motion Controllerdeep neural networkhuman-computer interactionmachine learningmulti-class classificationsign language recognitionsupport vector machine

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Area of Science:

  • Computer Science
  • Human-Computer Interaction
  • Artificial Intelligence

Background:

  • Sign language facilitates communication for deaf and dumb communities.
  • Limited societal adoption of sign language learning necessitates technological solutions.
  • Existing sign language recognition systems often focus on incomplete sets of gestures.

Purpose of the Study:

  • To develop a sign language recognition prototype for full American Sign Language (ASL) recognition, including 26 letters and 10 digits.
  • To differentiate between static and dynamic ASL gestures by extracting features from finger and hand motions.
  • To evaluate the performance of Support Vector Machine (SVM) and Deep Neural Network (DNN) models for ASL recognition.

Main Methods:

  • Utilized the Leap Motion Controller (LMC) for capturing hand and finger movements.
  • Implemented feature extraction techniques to analyze both static and dynamic gestures.
  • Trained and evaluated Support Vector Machine (SVM) and Deep Neural Network (DNN) models.

Main Results:

  • Deep Neural Network (DNN) achieved a recognition rate of 93.81% for 26 ASL letters.
  • Support Vector Machine (SVM) achieved a recognition rate of 80.30% for 26 ASL letters.
  • Recognition rates for the combined set of 26 letters and 10 digits were 88.79% (DNN) and 72.79% (SVM).

Conclusions:

  • The developed sign language recognition system demonstrates significant potential for improving communication accessibility.
  • The prototype can serve as a valuable interpreter for deaf and dumb individuals in daily service interactions.
  • The study highlights the effectiveness of DNNs in recognizing complex ASL gestures.