FigureSeer: Parsing Result-Figures in Research Papers

@inproceedings{Siegel2016FigureSeerPR,
  title={FigureSeer: Parsing Result-Figures in Research Papers},
  author={Noah Siegel and Zachary Horvitz and Roie Levin and S. Divvala and Ali Farhadi},
  booktitle={ECCV},
  year={2016}
}
‘Which are the pedestrian detectors that yield a precision above 95 % at 25 % recall. [...] Key Method The key challenge in analyzing the figure content is the extraction of the plotted data and its association with the legend entries. We address this challenge by formulating a novel graph-based reasoning approach using a CNN-based similarity metric. We present a thorough evaluation on a real-word annotated dataset to demonstrate the efficacy of our approach.Expand
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References

SHOWING 1-10 OF 58 REFERENCES
Automatic Extraction of Figures from Scholarly Documents
TLDR
The challenges of how to build a heuristic independent trainable model for such an extraction task and how to extract figures at scale are discussed and three new evaluation metrics are defined: figure-precision, figure-recall, and figure-F1-score are defined. Expand
An Architecture for Information Extraction from Figures in Digital Libraries
TLDR
A modular architecture for analyzing multiple figures representing experimental findings, an extractor algorithm to extract vector graphics from scholarly documents and a classification algorithm for figures which is very scalable, yet achieves 85\% accuracy are proposed. Expand
Looking Beyond Text: Extracting Figures, Tables and Captions from Computer Science Papers
TLDR
This work introduces a new dataset of 150 computer science papers along with ground truth labels for the locations of the figures, tables and captions within them and demonstrates a caption-to-figure matching component that is effective even in cases where individual captions are adjacent to multiple figures. Expand
A figure search engine architecture for a chemistry digital library
TLDR
This work gives the frame work for the extraction algorithm, architecture and ranking function, and indexes figure caption and mentions extracted from the PDF in documents using a custom built extractor. Expand
The Pascal Visual Object Classes Challenge: A Retrospective
TLDR
A review of the Pascal Visual Object Classes challenge from 2008-2012 and an appraisal of the aspects of the challenge that worked well, and those that could be improved in future challenges. Expand
Mind's eye: A recurrent visual representation for image caption generation
TLDR
This paper explores the bi-directional mapping between images and their sentence-based descriptions with a recurrent neural network that attempts to dynamically build a visual representation of the scene as a caption is being generated or read. Expand
Improving state-of-the-art OCR through high-precision document-specific modeling
TLDR
This work uses the state-of-the-art OCR system Tesseract to produce an initial translation, and uses this initial translation to bootstrap document-specific character models, which are able to reduce the error over properly segmented characters by 34.1% overall. Expand
ImageNet: A large-scale hierarchical image database
TLDR
A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets. Expand
Towards retrieving relevant information graphics
TLDR
This paper presents the first steps toward a system for retrieving bar charts and line graphs that reasons about the content of the graphic itself in deciding its relevance to the user query, and achieves accuracy higher than 80\% on a corpus of collected user queries. Expand
ReVision: automated classification, analysis and redesign of chart images
TLDR
ReVision is a system that automatically redesigns visualizations to improve graphical perception, and applies perceptually-based design principles to populate an interactive gallery of redesigned charts. Expand
...
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5
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