Automated Real-Time Recognition and Translation of Indigenous Sign Language Using Deep Learning
Bibliographic record
Abstract
This research focuses on creating a translation system of Plains Indian Sign Language (PISL) using deep learning. The key aim is to help close the communication gap between PISL users and non-users, especially among Indigenous populations. In the process of preparing the system, a set of 370 unique sign movements was created. The movements were recorded from different viewpoints to aid the model in learning with greater precision. The system utilizes MobileNetV2 to identify key features from the gestures and Mediapipe for real-time hand movement tracking. Certain data preprocessing steps, including resizing the frames and removing noise, were carried out to enable data preparation for training. The implemented model has robust accuracy and precision, which means that it will work well in real-world applications. Our work brings a way to improve the lives and learning of people with disabilities using PISL, as well as helping to preserve culturally essential and under-represented sign language.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".