Proc. ICDAR’95, Third International Conference on Document Analysis and Recognition, Montreal, Canada,
Bibliographic record
Abstract
We describe a new approach for evaluating page segmentation algorithms. Unlike techniques that rely on OCR output, our method is region-based: the segmentation output, described as a set of regions together with their types, output order etc., is matched against the pre-stored set of ground-truth regions. Misclassifications, splitting, and merging of regions are among the errors that are detected by the system. Each error is weighted individually for a particular application and a global estimate of segmentation quality is derived. The system can be customized to benchmark specific aspects of segmentation (e.g., headline detection) and according to the type of error correction that might follow (e.g., re-typing). Segmentation ground-truth files are quickly and easily generated and edited using GroundsKeeper, an X-Window based tool that allows one to view a document, manually draw regions (arbitrary polygons) on it, and specify information about each region (e.g., type, parent). 1
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 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 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".