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A 3D Camera-Based Approach for Real-Time Hand Configuration Recognition in Italian Sign Language.

Luca Ulrich1, Asia De Luca2, Riccardo Miraglia2

  • 1Management and Production Engineering, Politecnico di Torino, C.so Duca degli Abruzzi, 24, 10129 Torino, Italy.

Sensors (Basel, Switzerland)
|February 13, 2026
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Summary
This summary is machine-generated.

Recognizing hand shapes in Italian Sign Language (LIS) is crucial for communication. This study uses RGB-D sensing and 3D features to improve hand configuration recognition, enhancing LIS analysis systems.

Keywords:
Italian Sign Language (LIS)MediaPipeRGB-D camerasmachine learningsign-language recognition

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Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Linguistics

Background:

  • Deafness presents communication barriers, especially without sign language interpreters.
  • Hand configuration recognition is vital for understanding sign languages like Italian Sign Language (LIS).
  • Existing methods often integrate configuration recognition with temporal gesture modeling.

Purpose of the Study:

  • To address hand configuration recognition as an independent classification task for LIS.
  • To propose a machine vision framework utilizing RGB-D sensing for this task.
  • To evaluate the impact of real depth data on classification performance.

Main Methods:

  • Developed a framework combining MediaPipe hand landmark extraction with normalized 3D geometric features.
  • Employed a Support Vector Machine (SVM) classifier for configuration recognition.
  • Integrated sensor-acquired RGB-D depth measurements with landmark-based features.

Main Results:

  • The proposed method significantly improves classification performance and class separability for LIS hand configurations.
  • The use of real depth data enhances recognition, especially for geometrically similar configurations.
  • Decoupling configuration recognition from temporal modeling simplifies the task and improves results.

Conclusions:

  • Real depth data is critical for robust hand configuration recognition in LIS.
  • The proposed framework offers a promising approach for developing advanced LIS analysis systems.
  • This work advances the field of sign language recognition by focusing on the fundamental configuration-level problem.