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Sign language spotting with a threshold model based on conditional random fields.

Hee-Deok Yang1, Stan Sclaroff, Seong-Whan Lee

  • 1Department of Computer Science and Engineering, Korea University, Seoul 136-713, Korea. hdyang@image.korea.ac.kr

IEEE Transactions on Pattern Analysis and Machine Intelligence
|May 16, 2009
PubMed
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This study introduces a new method for sign language spotting, improving the detection and recognition of signs in continuous streams. The novel approach significantly enhances accuracy and reduces errors in sign language processing.

Area of Science:

  • Computer Vision
  • Human-Computer Interaction
  • Natural Language Processing

Background:

  • Sign language spotting involves detecting and recognizing signs within continuous streams, facing challenges due to variations in sign appearance and motion.
  • Distinguishing between in-vocabulary signs and nonsign patterns (e.g., transitional movements, out-of-vocabulary signs) is a key difficulty.

Purpose of the Study:

  • To propose a novel method for designing threshold models in Conditional Random Field (CRF) models for improved sign language spotting.
  • To enhance the accuracy of sign language spotting by incorporating a short-sign detector, hand appearance-based verification, and subsign reasoning.

Main Methods:

  • Developed an adaptive threshold model for Conditional Random Field (CRF) to differentiate between signs and nonsign patterns.

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  • Integrated a short-sign detector, a hand appearance-based sign verification method, and a subsign reasoning module.

    • Main Results:

    • Achieved an 87.0% spotting rate on continuous data and a 93.5% recognition rate on isolated data.
    • Reduced Sign Error Rate (SER) to 15.0% for continuous data and 6.4% for isolated data, significantly outperforming conventional CRFs.

    Conclusions:

    • The proposed novel threshold model significantly improves sign language spotting accuracy compared to traditional methods.
    • The integrated system, including short-sign detection and subsign reasoning, demonstrates superior performance in recognizing and spotting signs in continuous and isolated data.