A Web-Based Sign Language Translator Using 3D Video Processing
Bibliographic record
Abstract
The American Sign Language (ASL) is used by hearing-impaired people in North America, as well as in other parts of the world to supplement indigenous sign language. A proof-of-concept ASL Translator has been designed and developed using 3D video processing techniques. Foreseeing its potential as a Web-based application, the Translator must have a portable input device to capture gestures and its cost must be kept low. The recently introduced Xbox Kinect is a versatile gesture input device and fits the low-cost requirement as well. 3D data of the joints of a user captured by the Kinect are analyzed and matched to a library of pre-recorded signs. The matched signs are then transcribed to word or phrase, and output to a suitable user interface. The implemented prototype works with excellent accuracy for a limited vocabulary. Using the Web and a server to archive the pre-recorded signs and to process recorded gesture via a motion capture device, there are many potential applications. The Translator can be utilized as an assistive tool for the hearing impaired to communicate or as a teaching tool for those who want to learn the sign language.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.007 |
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 source (direct Gemma or distilled Codex), 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".