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• W3081020959 abstract "Localizing page elements/objects such as tables, figures, equations, etc. is the primary step in extracting information from document images. We propose a novel end-to-end trainable deep network, (CDeC-Net) for detecting tables present in the documents. The proposed network consists of a multistage extension of Mask R-CNN with a dual backbone having deformable convolution for detecting tables varying in scale with high detection accuracy at higher IoU threshold. We empirically evaluate CDeC-Net on all the publicly available benchmark datasets - ICDAR-2013, ICDAR-2017, ICDAR-2019,UNLV, Marmot, PubLayNet, and TableBank - with extensive experiments. Our solution has three important properties: (i) a single trained model CDeC-Net{ddag} performs well across all the popular benchmark datasets; (ii) we report excellent performances across multiple, including higher, thresholds of IoU; (iii) by following the same protocol of the recent papers for each of the benchmarks, we consistently demonstrate the superior quantitative performance. Our code and models will be publicly released for enabling the reproducibility of the results." @default.
• W3081020959 created "2020-09-01" @default.
• W3081020959 creator A5005417132 @default.
• W3081020959 creator A5041169901 @default.
• W3081020959 creator A5052301812 @default.
• W3081020959 date "2020-08-25" @default.
• W3081020959 modified "2023-10-17" @default.
• W3081020959 title "CDeC-Net: Composite Deformable Cascade Network for Table Detection in Document Images" @default.
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• W3081020959 doi "https://doi.org/10.48550/arxiv.2008.10831" @default.
• W3081020959 hasPublicationYear "2020" @default.
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