AAblation Study1 We conduct ablation studies to evaluate three key design choices in PosterAgent: (1) the binary-tree2 layout strategy for layout planning; (2) the inclusion of a commenter module as a visual critic; and3 (3) the use of in-context examples to enhance the visual perception capabilities of the commenter.4 We define the following variants:5 Direct: replacing the binary-tree layout with direct layout generation by an LLM;6 Tree: using the binary-tree layout strategy but removing the commenter module;7 Tree + Commenter: including the commenter module but without in-context examples;8 Tree + Commenter + IC: the full system, with both the commenter and in-context examples.9 All ablation variants are implemented using PosterAgent-4o, keeping all other components un-10 changed to isolate the effect of each factor. We visualize and compare results across five randomly11 selected papers from Paper2Poster, as shown in Figures 1 to 5.12 When prompting the LLM to directly generate poster layouts (Direct), the results are often structurally13 compromised (e.g., Figures 1a-3a), or resemble blog-style layouts that lack visual hierarchy and14 appeal (Figures 4a,5a). Fine-grained layout components, such as text boxes and figures, are especially15 challenging to synthesize in this setting: for instance, Figures1a-4a exhibit missing text boxes that16 leave noticeable blank areas, and Figure 4a fails to preserve the correct aspect ratio of figures.17

This study focuses on improving the optical character recognition (OCR) data for panels in the COMICS dataset, the largest dataset containing text and images from comic books. To do this, we developed a pipeline for OCR processing and labeling of comic books and created the first text detection and recognition datasets for western comics, called "COMICS Text+: Detection" and "COMICS Text+: Recognition". We evaluated the performance of state-of-the-art text detection and recognition models on these datasets and found significant improvement in word accuracy and normalized edit distance compared to the text in COMICS. We also created a new dataset called "COMICS Text+", which contains the extracted text from the textboxes in the COMICS dataset. Using the improved text data of COMICS Text+ in the comics processing model from resulted in state-of-the-art performance on cloze-style tasks without changing the model architecture. The COMICS Text+ dataset can be a valuable resource for researchers working on tasks including text detection, recognition, and high-level processing of comics, such as narrative understanding, character relations, and story generation. All the data and inference instructions can be accessed in https://github.com/gsoykan/comics_text_plus.

This paper presents an end-to-end trainable fast scene text detector, named TextBoxes, which detects scene text with both high accuracy and efficiency in a single network forward pass, involving no post-process except for a standard non-maximum suppression. TextBoxes outperforms competing methods in terms of text localization accuracy and is much faster, taking only 0.09s per image in a fast implementation. Furthermore, combined with a text recognizer, TextBoxes significantly outperforms state-of-the-art approaches on word spotting and end-to-end text recognition tasks.