Welcome from the Executive Chairs
Bibliographic record
Abstract
Welcome to the 2013 International Conference on Document Analysis and Recognition in Washington, DC.It is truly an honor to host our premier conference in the capital of the United States of America.While the city is still very young compared to previous hosts, it represents the founding of our nation from early settlers to the modern day center of government.We hope that you will take time to enjoy the cultural and historic sites the city has to offer, especially the many monuments and museums, only minutes from the conference venue.ICDAR 2013 is the twelfth biennial meeting of our international research community which began in St. Malo, France in 1991.Since that time, we have met in Tsukuba, Japan ('93), Montreal, Canada ('95), Ulm, Germany ('97), Bangalore, India ('99), Seattle, Washington ('01), Edinburgh, UK ('03), Seoul, Korea ('05), Curitiba, Brazil ('07), Barcelona, Spain ('09) and most recently in Beijing, China ('11).ICDAR 2013 continues a long tradition of providing state-ofthe-art snapshots of the research advances in our field and we hope that you will benefit from all that the conference has to offer, both technically and socially.This year's conference will be highlighted by three keynote talks.The first keynote will be by IAPR/ICDAR Outstanding Achievements Award winner Réjean Plamondon, who will focus his talk on the neuromotor aspects of handwriting.In the second keynote, Roger Easton, a professor from the Rochester Institute of Technology, will draw on his work in using imaging technologies to enable manuscript scholars to uncover lost or unexpected material.Finally Michael Lesk, a professor at Rutgers University, will provide his insights on the generality of graphic tools and text tools and what can be learned from relationships between them.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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; both teacher heads agree on what is shown here.
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".