74 research outputs found
A survey of comics research in computer science
Graphical novels such as comics and mangas are well known all over the world.
The digital transition started to change the way people are reading comics,
more and more on smartphones and tablets and less and less on paper. In the
recent years, a wide variety of research about comics has been proposed and
might change the way comics are created, distributed and read in future years.
Early work focuses on low level document image analysis: indeed comic books are
complex, they contains text, drawings, balloon, panels, onomatopoeia, etc.
Different fields of computer science covered research about user interaction
and content generation such as multimedia, artificial intelligence,
human-computer interaction, etc. with different sets of values. We propose in
this paper to review the previous research about comics in computer science, to
state what have been done and to give some insights about the main outlooks
SlideImages: A Dataset for Educational Image Classification
In the past few years, convolutional neural networks (CNNs) have achieved
impressive results in computer vision tasks, which however mainly focus on
photos with natural scene content. Besides, non-sensor derived images such as
illustrations, data visualizations, figures, etc. are typically used to convey
complex information or to explore large datasets. However, this kind of images
has received little attention in computer vision. CNNs and similar techniques
use large volumes of training data. Currently, many document analysis systems
are trained in part on scene images due to the lack of large datasets of
educational image data. In this paper, we address this issue and present
SlideImages, a dataset for the task of classifying educational illustrations.
SlideImages contains training data collected from various sources, e.g.,
Wikimedia Commons and the AI2D dataset, and test data collected from
educational slides. We have reserved all the actual educational images as a
test dataset in order to ensure that the approaches using this dataset
generalize well to new educational images, and potentially other domains.
Furthermore, we present a baseline system using a standard deep neural
architecture and discuss dealing with the challenge of limited training data.Comment: 8 pages, 2 figures, to be presented at ECIR 202
Feature Mixing for Writer Retrieval and Identification on Papyri Fragments
This paper proposes a deep-learning-based approach to writer retrieval and
identification for papyri, with a focus on identifying fragments associated
with a specific writer and those corresponding to the same image. We present a
novel neural network architecture that combines a residual backbone with a
feature mixing stage to improve retrieval performance, and the final descriptor
is derived from a projection layer. The methodology is evaluated on two
benchmarks: PapyRow, where we achieve a mAP of 26.6 % and 24.9 % on writer and
page retrieval, and HisFragIR20, showing state-of-the-art performance (44.0 %
and 29.3 % mAP). Furthermore, our network has an accuracy of 28.7 % for writer
identification. Additionally, we conduct experiments on the influence of two
binarization techniques on fragments and show that binarizing does not enhance
performance. Our code and models are available to the community.Comment: accepted for HIP@ICDAR202
CleanPage: Fast and Clean Document and Whiteboard Capture
The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing, and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work, and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use
CleanPage: Fast and Clean Document and Whiteboard Capture
The move from paper to online is not only necessary for remote working, it is also significantly more sustainable. This trend has seen a rising need for the high-quality digitization of content from pages and whiteboards to sharable online material. However, capturing this information is not always easy nor are the results always satisfactory. Available scanning apps vary in their usability and do not always produce clean results, retaining surface imperfections from the page or whiteboard in their output images. CleanPage, a novel smartphone-based document and whiteboard scanning system, is presented. CleanPage requires one button-tap to capture, identify, crop, and clean an image of a page or whiteboard. Unlike equivalent systems, no user intervention is required during processing, and the result is a high-contrast, low-noise image with a clean homogenous background. Results are presented for a selection of scenarios showing the versatility of the design. CleanPage is compared with two market leader scanning apps using two testing approaches: real paper scans and ground-truth comparisons. These comparisons are achieved by a new testing methodology that allows scans to be compared to unscanned counterparts by using synthesized images. Real paper scans are tested using image quality measures. An evaluation of standard image quality assessments is included in this work, and a novel quality measure for scanned images is proposed and validated. The user experience for each scanning app is assessed, showing CleanPage to be fast and easier to use
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