Machine Learning and Vision Based Techniques for Detecting and Recognizing Indian Sign Language
Bibliographic record
Abstract
Despite rapid global advancement in technology, the persistent challenge of hearing impairments affects a significant proportion of the global population. Individuals with these impairments face complex communication barriers daily. The Indian Sign Language (ISL) has emerged as a universal communication tool for individuals with hearing impairments in India, playing a vital role in educational institutions and bridging societal gaps in a country marked by rich cultural and linguistic diversity. This work presents an innovative Supervised Learning approach for ISL recognition that extends beyond traditional classification techniques. The method employs advanced algorithms designed to classify new observations. Utilizing an expansive dataset, intricate patterns and nuances are identified, fostering accurate and adaptive decision-making. A Convolutional Neural Network (CNN) algorithm is applied, not only for data classification but also for iterative learning and refinement of classification boundaries. In the vast expanse of n-dimensional space, the CNN strives to identify optimal hyperplanes, establishing dynamic decision boundaries to adeptly categorize diverse data points. This approach transcends traditional classification boundaries, offering a more nuanced and effective data-driven decisionmaking process. This research heralds a new direction in addressing communication barriers, with potential applications extending beyond the realm of ISL. The assimilation of numerous regional languages into the sign language matrix, whilst challenging, is key to fostering a life of normalcy and integration.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".