3 research outputs found
A Survey and Approach to Chart Classification
Charts represent an essential source of visual information in documents and
facilitate a deep understanding and interpretation of information typically
conveyed numerically. In the scientific literature, there are many charts, each
with its stylistic differences. Recently the document understanding community
has begun to address the problem of automatic chart understanding, which begins
with chart classification. In this paper, we present a survey of the current
state-of-the-art techniques for chart classification and discuss the available
datasets and their supported chart types. We broadly classify these
contributions as traditional approaches based on ML, CNN, and Transformers.
Furthermore, we carry out an extensive comparative performance analysis of
CNN-based and transformer-based approaches on the recently published CHARTINFO
UB-UNITECH PMC dataset for the CHART-Infographics competition at ICPR 2022. The
data set includes 15 different chart categories, including 22,923 training
images and 13,260 test images. We have implemented a vision-based transformer
model that produces state-of-the-art results in chart classification.Comment: Accepted in 15th IAPR Workshop on Graphics Recognition (GREC) 2023 in
conjunction with 17th International Conference on Document Analysis and
Recognition (ICDAR) 2023, August 21-26, 2023 San Jose, US
Context-Aware Chart Element Detection
As a prerequisite of chart data extraction, the accurate detection of chart
basic elements is essential and mandatory. In contrast to object detection in
the general image domain, chart element detection relies heavily on context
information as charts are highly structured data visualization formats. To
address this, we propose a novel method CACHED, which stands for Context-Aware
Chart Element Detection, by integrating a local-global context fusion module
consisting of visual context enhancement and positional context encoding with
the Cascade R-CNN framework. To improve the generalization of our method for
broader applicability, we refine the existing chart element categorization and
standardized 18 classes for chart basic elements, excluding plot elements. Our
CACHED method, with the updated category of chart elements, achieves
state-of-the-art performance in our experiments, underscoring the importance of
context in chart element detection. Extending our method to the bar plot
detection task, we obtain the best result on the PMC test dataset.Comment: Published in ICDAR 2023. Code and model are available at
https://github.com/pengyu965/ChartDet
Transforming the Reading Experience of Scientific Documents with Polymorphism
Despite the opportunities created by digital reading, documents remain mostly static and mimic paper. Any improvement in the shape or form of documents has to come from authors who contend with current digital formats, workflows, and software and who impose a presentation to readers. Instead, I propose the concept of polymorphic documents which are documents that can change in form to offer better representations of the information they contain. I believe that multiple representations of the same information can help readers, and that any document can be made polymorphic, with no intervention from the original author. This thesis presents four projects investigating what information can be obtained from existing documents, how this information can be better represented, and how these representations can be generated using only the source document. To do so, I draw upon theories showing the benefit of presenting information using multiple representations; the design of interactive systems to support morphing representations; and user studies to evaluate system usability and the benefits of the new representations on reader comprehension