Dynamic ASL Gesture Recognition with Real-Time Customization for Alphabets and Actions
Bibliographic record
Abstract
Traditional sign language recognition systems primarily focus on static gestures and lack the flexibility to adapt in real time, limiting their effectiveness in natural communication scenarios. This creates barriers in recognizing dynamic movements and hinders personalization for diverse users. This paper introduces a novel system for Dynamic ASL Gesture Recognition with Real-Time Customization for Alphabets and Actions. The framework integrates computer vision techniques using MediaPipe for landmark detection and deep learning models like CNN-LSTM for dynamic sequence classification. Its core innovation is a User-Centric Customization Module that enables individuals to add or modify gestures on the fly without retraining the entire model. This ensures adaptability for both alphabets and user-defined action commands, bridging inclusivity gaps in communication. The system delivers a robust, real-time, and scalable solution that promotes seamless interaction between hearing-impaired individuals and the broader community.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".