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Continuous sign language recognition algorithm based on object detection and variable-length coding sequence.
Di Fan1, Meng Yi1, Wenshuo Kang1,2
1Shandong University of Science and Technology, Qingdao, 266590, China.
Scientific Reports
|November 11, 2024
Summary
This study introduces an improved continuous sign language recognition method using target detection and coding sequences. The new approach significantly reduces word error rates and computational costs, enhancing speed and accuracy.
Area of Science:
- Computer Science
- Artificial Intelligence
- Human-Computer Interaction
Background:
- Continuous sign language recognition faces challenges including skeletal data acquisition, long training times for 3D CNNs, and hand occlusion/blurring.
- Existing methods struggle with efficiency and accuracy in real-world sign language interpretation.
Purpose of the Study:
- To propose a novel continuous sign language recognition method addressing current limitations.
- To enhance the speed, accuracy, and efficiency of sign language recognition systems.
Main Methods:
- Utilized a Dual-branch Shuffle Attention-You Only Look Once version X (DSA-YOLOX) network for head and hand detection.
- Developed a method to encode sign language videos, transforming 3D data to 1D.
- Implemented a Bi-directional Long Short-Term Memory (BiLSTM) model with Fast Dynamic Time Warping (FastDTW) for sequence classification and feature extraction.
Main Results:
- Achieved a 21.26% reduction in word error rate (WER) compared to DTW-HMM and 11.53% compared to LSTM-A.
- Dramatically reduced computational load, with GFLOPs being 1/13 of VAC and 1/57 of STMC models.
- Demonstrated superior performance in balancing speed and accuracy for sign language recognition.
Conclusions:
- The proposed DSA-YOLOX and BiLSTM with FastDTW method effectively overcomes challenges in continuous sign language recognition.
- The approach offers significant improvements in recognition accuracy and computational efficiency.
- This method represents a substantial advancement in developing practical and effective sign language recognition technologies.
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