Gesture Language Recognition Through Computer Vision and a Spatial-Temporal Mathematical Model
Bibliographic record
Abstract
For people with speech and hearing impairments, sign language is a vital form of communication that allows them to communicate and en-gage with others. Nonetheless, a major obstacle is the general public's limited comprehension of sign language. For the deaf and mute com-communities, this communication gap frequently results in challenges with social inclusion, education, and career prospects. To solve this problem, researchers are increasingly using deep learning and artificial intelligence (AI) techniques to create automatic sign language recognition (SLR) systems that can instantly translate sign motions into speech or text. This paper presents a hybrid method that combines continuous sign language recognition (CSLR) and isolated sign language recognition (SLR) into a single deep learning framework. The system uses a Spatial-Temporal Network (STNet) to identify dynamic sign sequences in CSLR and a Convolutional Neural Network (CNN) for isolated sign identification. An ensemble learning technique is included to increase model robustness, and an optimized Inception-based architecture is utilized for isolated sign classification to boost performance. Additionally, a novel Spatial Resonance Module (SRM) refines frame-to-frame feature extraction, and a Multi-Temporal Perception Module (MTPM) strengthens long-range dependency recognition in sign sequences. These advancements contribute to higher accuracy and efficiency in sign language interpretation. Experimental validation of the proposed system was conducted using benchmark datasets, demonstrating superior performance compared to existing state-of-the-art techniques. The model achieved an accuracy of 98.46% in isolated sign recognition and exhibited a 2.9% improvement in CSLR tasks. The ability to accurately recognize and translate sign language in both isolated and continuous contexts makes this system highly suitable for real-time applications, including assistive communication devices, virtual interpreters, and educational tools. The pro-posed research has the potential to significantly impact accessibility and inclusivity for individuals with speech and hearing impairments. By integrating deep learning with real-time processing, this system enhances human-computer interaction and fosters seamless communication between sign language users and the broader community. Future research can explore the integration of additional modalities, such as facial expressions and hand movement trajectories, to further refine sign language recognition models and ensure even greater accuracy and adapt-ability.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".