Local thresholding of composite documents using multi-layer perceptron nerual network
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
Bi-level thresholding of document images with poor contrast, non-uniform illumination, complex background patterns and non-uniformly distributed backgrounds is a challenging problem that researchers have been trying to solve. The problem is that different algorithms tend to yield different results based on the assumptions made to the images content. A new binarization algorithm is proposed to deal with such images. The algorithm proposed uses statistical and texture feature measures to obtain a feature vector from a pixel window of size (2n+1)/spl times/(2n+1), it then uses a multi-layer perceptron neural network (MLP NN) to classify each pixel value in the image. The proposed method performed better than existing global and local thresholding techniques and works on different variety of images. The algorithm provides a local understanding of pixels from its neighborhood. This new method that uses NN and works on scanned documents with non-uniform backgrounds.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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