Comic2CEBX: a system for automatic comic content adaptation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Comics are popular almost throughout the world. With the help of comic document digitization, it is much easier for people to archive and browse comic works. However, there are still some big challenges along with comic document digitization progress. Among these challenges, comic content adaptation is an important one to be tackled. The existing works only focus on parts of this problem and do not provide a tangible solution to display comic contents on different devices. In this paper, we solve these problems by proposing Comic2CEBX, a system which can automatically convert a set of scanned comic page images into a CEBX file that allows reflowing of the original comic pages with fixed layouts. Taking raw comic images as inputs, our system first extracts three kinds of low-level visual patterns and then uses multilayer Conditional Random Fields to detect all the panels. Meanwhile, our system automatically identifies the reading orders of the panels within each page. Finally, we encapsulate the comic page images and the obtained page structure information (i.e., the panels detection results and the corresponding reading orders) to generate a CEBX file. Experimental results show that our comic page layout analysis method achieves better performance than the existing ones, and use case presentation of the CEBX files produced by our system demonstrates that it brings better comic reading experience especially on mobile devices.
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.
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 | 0.001 |
| 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 | 0.002 | 0.004 |
| Open science | 0.001 | 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 it