Multi-class SVM based gradient feature for banknote recognition
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
Banknote recognition system is the focus of different image processing and pattern recognition research. With the improvement in modern-day banking operations, automated systems for banknote recognition have become pertinent. Recognition of banknotes is a challenging task as banknotes can suffer from defects and images get distorted during acquisition, which raises the need for a robust recognition system to mitigate these flaws. This research proposes a new banknote recognition approach that classifies the principal components of the extracted Histogram of Gradient feature vectors using an efficient error correcting output code technique based on a Multi-Class Support Vector Machine. The method works on both sides of the bank note and efficiently recognize the denomination based on any side of the bill. The system was implemented using the Nigerian Naira, and for experimental evaluation, additional analysis was conducted using the US Dollar, Canadian Dollar, and Euro banknotes. Finally, the system performance was evaluated based on the recognition rate and processing time.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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