A Deep Learning-Based Approach for Hand Sign Recognition Using CNN Architecture
Bibliographic record
Abstract
The domain of hand sign recognition, an integral facet of computer vision, encompasses a wide array of practical applications, ranging from interpreting sign language and recognizing gestures to facilitating human-computer interaction. This research elucidates the introduction of a Convolutional Neural Network (CNN) model tailored to the identification of hand signs representing the English alphabet. For model training and validation, a dataset comprising 26,000 grayscale images of hand signs was employed. The model architecture embraced a profound CNN design, featuring numerous layers for convolution and pooling, followed by fully connected layers. Employing the Adam optimizer, the training procedure yielded an impressive accuracy of 96.7% when evaluated on the Kaggle dataset. These outcomes underscore the effectiveness of the proposed CNN model in precisely discerning hand signs corresponding to the English alphabet. The model's potential utility extends to the recognition of intricate manual gestures and realtime applications, including aiding individuals with motor impairments and enriching virtual reality experiences. Hence, this study accentuates the capacity of deep learning to propel the domain of hand sign recognition forward.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".