Arab Sign language Recognition with Convolutional Neural Networks
Why this work is in the frame
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Bibliographic record
Abstract
The implementation of an automatic recognition system for Arab sign language (ArSL) has a major social and humanitarian impact. With the growth of the deaf-dump community, such a system will help in integrating those people and enjoy a normal life. Like other languages, Arab sign language has many details and diverse characteristics that need a powerful tool to treat it. In this work, we propose a new system based on the convolutional neural networks, fed with a real dataset, this system will recognize automatically numbers and letters of Arab sign language. To validate our system, we have done a comparative study that shows the effectiveness and robustness of our proposed method compared to traditional approaches based on k-nearest neighbors (KNN) and support vector machines (SVM).
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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 | 0.001 |
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 it