Blind Source Separation Based Framework for Multispectral Document Images Binarization
Bibliographic record
Abstract
In this paper, we propose a novel Blind Source Separation (BSS) based framework for multispectral (MS) document images binarization. This framework takes advantage of the multidimensional data representation of MS images and makes use of the Graph regularized Non-negative Matrix Factorization (GNMF) to decompose MS document images into their different constituting components, i.e., foreground (text, ink), background (paper, parchment), degradation information, etc. The proposed framework is validated on two different real-world data sets of manuscript images showing a high capability of dealing with: variable numbers of bands regardless of the acquisition protocol, different types of degradations, and illumination non-uniformity while outperforming the results reported in the state-of-the-art. Although the focus was put on the binary separation (i.e., foreground/background), the proposed framework is also used for the decomposition of document images into different components, i.e., background, text, and degradation, which allows full sources separation, whereby further analysis and characterization of each component can be possible. A comparative study is performed using Independent Component Analysis (ICA) and Principal Component Analysis (PCA) methods. Our framework is also validated on another third dataset of MS images of natural objects to demonstrate its generalizability beyond document samples.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".