APEX-NET: Automatic Plot Extractor Network

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dc.contributor.author Gangopadhyay, Aalok
dc.contributor.author Singh, Prajwal
dc.contributor.author Raman, Shanmuganathan
dc.date.accessioned 2021-01-27T15:27:13Z
dc.date.available 2021-01-27T15:27:13Z
dc.date.issued 2021-01
dc.identifier.citation Gangopadhyay, Aalok; Singh, Prajwal and Raman, Shanmuganathan, "APEX-NET: Automatic Plot Extractor Network", arXiv, Cornell University Library, DOI: arXiv:2101.06217, Jan. 2021. en_US
dc.identifier.uri http://arxiv.org/abs/2101.06217
dc.identifier.uri https://repository.iitgn.ac.in/handle/123456789/6239
dc.description.abstract Automatic extraction of raw data from 2D line plot images is a problem of great importance having many real-world applications. Several algorithms have been proposed for solving this problem. However, these algorithms involve a significant amount of human intervention. To minimize this intervention, we propose APEX-Net, a deep learning based framework with novel loss functions for solving the plot extraction problem. We introduce APEX-1M, a new large scale dataset which contains both the plot images and the raw data. We demonstrate the performance of APEX-Net on the APEX-1M test set and show that it obtains impressive accuracy. We also show visual results of our network on unseen plot images and demonstrate that it extracts the shape of the plots to a great extent. Finally, we develop a GUI based software for plot extraction that can benefit the community at large.
dc.description.statementofresponsibility by Aalok Gangopadhyay, Prajwal Singh and Shanmuganathan Raman
dc.language.iso en_US en_US
dc.publisher Cornell University Library en_US
dc.subject Computer Science en_US
dc.subject Computer Vision en_US
dc.subject Pattern Recognition en_US
dc.title APEX-NET: Automatic Plot Extractor Network en_US
dc.type Pre-Print en_US
dc.relation.journal arxiv


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