Implementation and Evaluation of a Real Time Sign Language Recognition System
Bibliographic record
Abstract
This research presents the deployment and assessment of a real-time sign language identification technique with 87% accuracy for gesture and sound recognition and 450ms latency. Using a webcam interface, the system utilizes an adaptive ensemble learning architecture of CNN, RNN, and Transformer models for static and dynamic gesture classification. Environmental testing in adverse lighting and background noise environments consistently performs (>85% accuracy). Novel features include cross-platform compatibility with desktop and mobile platforms, bidirectional communication with sign-to-text/speech and speech-to-sign translation support, and scalable architecture supporting different signing styles. Preprocessing techniques for improved occlusion and lighting robustness ensure system performance in real-world operation. User testing with deaf and hearing subjects showed promising feedback in terms of usability and communication effectiveness. This inclusive solution bridges substantial communication barriers while offering a platform for future growth in vocabulary extension and regional sign language adaptation.
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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".