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Real-Time Recognition of NZ Sign Language Alphabets by Optimal Use of Machine Learning.

Mubashir Ali1, Seyed Ebrahim Hosseini2, Shahbaz Pervez2

  • 1Idexx Laboratories Inc., Auckland 4440, New Zealand.

Bioengineering (Basel, Switzerland)
|October 29, 2025
PubMed
Summary

This study developed a machine learning application to assess New Zealand Sign Language (NZSL) proficiency by recognizing hand gestures. The system aims to bridge communication gaps for deaf individuals in New Zealand.

Keywords:
AdaBoost (AB)New Zealand Sign Language (NZSL)PythonRandom Forest (RF)Support Vector Machine (SVM)applicationcomputer visionk-Nearest Neighbours (KNN)landmarkmachine learning

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Area of Science:

  • Computer Science
  • Artificial Intelligence
  • Linguistics

Background:

  • Effective communication is vital for deaf individuals, yet challenges persist due to a lack of shared language.
  • New Zealand's educational system's focus on oralism disadvantages deaf students relying on sign language.
  • Bridging the communication gap between the public and the deaf community is crucial for inclusion.

Purpose of the Study:

  • To develop an application for assessing New Zealand Sign Language (NZSL) proficiency.
  • To investigate machine learning methods for accurate hand gesture recognition using landmark detection.
  • To enhance communication accessibility for deaf individuals in New Zealand.

Main Methods:

  • Hand gesture recognition using machine learning, focusing on landmark detection.
  • Utilizing a dataset of approximately 100,000 hand gesture expressions in CSV format.
  • Evaluating multiple classifiers: Random Forest, k-Nearest Neighbours, AdaBoost, Naïve Bayes, Support Vector Machine, Decision Trees, and Logistic Regression.

Main Results:

  • The study systematically evaluated various machine learning models for NZSL gesture recognition.
  • Landmark detection proved effective for feature extraction in computer vision tasks.
  • The developed system aims to provide a quantifiable measure of NZSL proficiency.

Conclusions:

  • Machine learning, particularly landmark detection, offers a viable approach to developing NZSL proficiency assessment tools.
  • The application has the potential to significantly improve communication accessibility for the deaf community in New Zealand.
  • Further development can enhance the system's accuracy and expand its application in educational and professional settings.