Interactive and Markerless Visual Recognition of Brazilian Sign Language Alphabet
Bibliographic record
Abstract
The automatic recognition of sign languages will increase the inclusion of non-verbal persons in society by allowing them to communicate with people who are not familiar with sign language. To this end, recently some systems have been proposed to automatically recognize sign language. Among them, those with external aids such as gloves, markers, clothing/background control, or radars usually have high accuracy, but are not practical in daily life situations. On the other hand, those that use only a camera, such as prevalent smartphone cameras, are practical but have lower accuracy. In this work, we present a system that can recognize the alphabet in the Brazilian sign language, Língua Brasileira de Sinais (LIBRAS), using only a camera yet achieving high accuracy. Like most existing works, our system captures and processes the image of hand gesture and uses a classifier to recognize the sign. However, unlike existing works, the classifier is the Inception-v3 neural network trained with transfer learning on our custom-collected LIBRAS alphabet dataset. Performance evaluations show the system recognizes LIBRAS alphabet with 97% accuracy. We also developed an interactive app, demonstrating that it can run in real time.
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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 | 0.000 | 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".