Sign Language Translation and Hand Gesture Identification using Deep Learning
Bibliographic record
Abstract
Sign language is an important form of communication among the hearing and speech impaired community. However, the lack of interpreters and language barriers meant that it is often difficult for signers and non-signers to communicate effectively. This paper present a Sign Language Translation and Hand Gesture Identification using deep learning and computer vision techniques, in real-time. The operation of the proposed system has two modes: Letter Mode and Word Mode. In the Letter Mode, MediaPipe extracts the landmarks of the hand which are processed using the Random forest classifier for recognition of individual Individual Alphabets and Numerals. In Word Mode, sequences of 30 frames of video are fed into an InceptionV5-LSTM hybrid model for the recognition of both spatial and temporal features of the gesture in order to determine the word they represent. The system has been able to achieve accurate translation of hand gestures to text and show realtime prediction using integrated OpenCV interface. Experimental evaluation shows good performance under different lighting and background, which can stably keep high detection accuracy, as well as maintain not too low frame rate, ideal for real-time application.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".