47,564 research outputs found
STEFANN: Scene Text Editor using Font Adaptive Neural Network
Textual information in a captured scene plays an important role in scene
interpretation and decision making. Though there exist methods that can
successfully detect and interpret complex text regions present in a scene, to
the best of our knowledge, there is no significant prior work that aims to
modify the textual information in an image. The ability to edit text directly
on images has several advantages including error correction, text restoration
and image reusability. In this paper, we propose a method to modify text in an
image at character-level. We approach the problem in two stages. At first, the
unobserved character (target) is generated from an observed character (source)
being modified. We propose two different neural network architectures - (a)
FANnet to achieve structural consistency with source font and (b) Colornet to
preserve source color. Next, we replace the source character with the generated
character maintaining both geometric and visual consistency with neighboring
characters. Our method works as a unified platform for modifying text in
images. We present the effectiveness of our method on COCO-Text and ICDAR
datasets both qualitatively and quantitatively.Comment: Accepted in The IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 202
Learning Visual Importance for Graphic Designs and Data Visualizations
Knowing where people look and click on visual designs can provide clues about
how the designs are perceived, and where the most important or relevant content
lies. The most important content of a visual design can be used for effective
summarization or to facilitate retrieval from a database. We present automated
models that predict the relative importance of different elements in data
visualizations and graphic designs. Our models are neural networks trained on
human clicks and importance annotations on hundreds of designs. We collected a
new dataset of crowdsourced importance, and analyzed the predictions of our
models with respect to ground truth importance and human eye movements. We
demonstrate how such predictions of importance can be used for automatic design
retargeting and thumbnailing. User studies with hundreds of MTurk participants
validate that, with limited post-processing, our importance-driven applications
are on par with, or outperform, current state-of-the-art methods, including
natural image saliency. We also provide a demonstration of how our importance
predictions can be built into interactive design tools to offer immediate
feedback during the design process
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