Hand Gesture Recognition Using CNN & Publication of World's Largest ASL Database
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Sign language is used throughout the world by the hearing impaired to communicate. Recent advancements in Computer Vision and Deep Learning has given rise to many machine learning based translators. In this research paper, a solution to recognize the English alphabet presented as static signs in the American Sign Language (ASL) is proposed. The classifications are achieved by a four layer CNN. The model is trained and tested on a dataset created for this paper. This dataset will be published as a contribution to the community and is currently the world's largest ASL database consisting of 624,000 images. Split into two sections, the database contains images in both the IR and RGB spectrum. Classifications on both sets of data achieve state-of-the-art results when compared to similar research. An accuracy of 99.89% and 99.91 % are achieved when classifying the IR and RGB datasets respectively.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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 it