Reconstruction of a Hidden Page from Segmented Contours Using Deep Learning
Bibliographic record
Abstract
Historical documents are always too fragile to be opened, so the contents should be extracted carefully with less physical damage to the books. The significant improvements in X-ray scanning have made it possible to digitize and “virtual” these documents. A new technology involving a bunch of 3D X-ray images could help recognize the inner surfaces and create visualizations of written content [1]. The problem is extracting the book contents but avoiding opening them directly. This project aims to visualize the complete contents without secondary damage by using a CT scan to capture contour information and restructure the 3D surfaces of the books. This research will extract the edge or contour information from the segmentation stage and reconstruct 3D surfaces representing the book's individual pages. Once the pages have been reconstructed, text or image information will be mapped onto the surface of each page. This project aims to develop a computational framework for recovering surfaces from curves. The framework of this research provides a new technology for historians to better study historical books and protect the sealed books simultaneously. This study is very helpful for studying historical documents as it is a better way to extract contents from historical books with as little damage as possible than previous technologies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".