Are Sparse Representation and Dictionary Learning Good for Handwritten Character Recognition?
Bibliographic record
Abstract
Recently the theories of sparse representation (SR) and dictionary learning (DL) have brought much attention and become powerful tools for pattern recognition and computer vision. Due to the fact that images can be represented in a sparse and compressible way with respect to some dictionaries, these theories have shown successful applications in many different areas including face recognition, image denoising and in painting, medical imaging, image classification and registration, motion estimation, and many more. Over a relatively short time, many improvements and innovative ideas using SR and DL have been developed. However, very little published work is found in the application of these theories on handwritten character recognition. One question comes to mind is whether these theories could produce good results for handwritten character recognition as in the case of other applications. In this paper, we would like to address this question by investigating various applications of the theories to handwritten character recognition. Experiments were conducted in both handwritten digits and alphabetical characters on three benchmark databases: MNIST, USPS, and CEDAR. The results showed that while this approach can achieve good results, it cannot beat the state of the art. The main advantage of this approach is that it does not require the choice of features and hence it may reduce computational cost.
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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 | 0.005 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".