N-grams: a well-structured knowledge representation for recognition of graphical documents
Bibliographic record
Abstract
N-grams are a well-structured knowledge representation that has proven useful in the recognition of textual documents. In this paper, we propose that n-grams can be extended to the domain of graphical documents as well. This has the advantage of providing a regular, easily-maintainable knowledge representation for local constraints, specifically symbol-interaction knowledge, in the graphical domain. To extend the definition of an n-gram from the textual to the graphical domain, we must resolve how to handle directional considerations, how to treat the variety of relations that can occur between image primitives, and how to treat multiple neighbours. We have implemented a prototype system for the application of n-gram knowledge to the recognition of sketch maps. Early results are encouraging. We look forward to further refining this system. N-grams appear to be a promising tool for representing spatial constraints in a form that is useful to the recognition of graphical documents.
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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 | 0.000 |
| 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".