A hybrid method for table detection from document image

Anh, Trần Tuấn; Na, In Seop; Kim, Soo-Hyung

doi:10.1109/acpr.2015.7486480

Cited by 12 publications

(4 citation statements)

References 20 publications

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“…Anh et al [4] proposed a hybrid approach for the detection of table structures, irrespective of the style, a ruling line table or a non-ruling line table. Experimental results are shown by them for the ICDAR-2013 table competition dataset.…”

Section: Related Work and Motivationmentioning

confidence: 99%

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Robust Detection of Tables in Documents Using Scores from Table Cell Cores

et al. 2022

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Table detection is an essential step in many document analysis systems. Tabular data are a pivotal form of information representation that can organize data in a conventional structure for comfortable and quick information retrieval and comparison. Detection of table structures in PDF files or images is a challenging task because of the variability of table layouts, and sometimes the tabular structures’ similarities with non-tabular elements like charts, plots, etc. In this work, we have presented a table detection method using a geometric analysis of the table cell cores that represents the table cell texts. The proposed method works by analyzing the text gap information, and hence it can detect the table cell cores, irrespective of the presence of the table boundary lines and cell-separating rule-lines. Experimentations have been done on various document images of complex structures from well-known datasets. The detection accuracies obtained by us corroborate the usefulness of the proposed method.

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Section: Related Work and Motivationmentioning

confidence: 99%

“…In our proposed method, we are not relying on any horizontal or vertical lines for detecting tables. • A hybrid method to detect both ruled and non-ruled tables has been proposed in [4]. However, this method is very complicated and time consuming.…”

Section: Our Contributionsmentioning

confidence: 99%

Robust Detection of Tables in Documents Using Scores from Table Cell Cores

et al. 2022

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“…Other methods were explored by Jahan et al [22] with word spacing and line height thresholds for table localization. Hybrid approaches attempt to discover table candidate regions, as in Anh et al [1]. Finally, deep learning methods were presented in Hao et al [16] regional proposal network using CNNs, and Gilani et al [15] adapted the Faster R-CNN architecture to segment table regions in a given image.…”

Section: Table Detection Is Not a New Problemmentioning

confidence: 99%

Financial table extraction in image documents

Watson¹,

Liu

2020

Proceedings of the First ACM International Conference on AI in Finance

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Table extraction has long been a pervasive problem in financial services. This is more challenging in the image domain, where content is locked behind cumbersome pixel format. Luckily, advances in deep learning for image segmentation, OCR, and sequence modeling provides the necessary heavy lifting to achieve impressive results. This paper presents an end-to-end pipeline for identifying, extracting and transcribing tabular content in image documents, while retaining the original spatial relations with high fidelity.

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“…The main contributions of this paper are as follows: [33], or horizontal and vertical lines [34][35][36]), keywords [37,38], or formal templates [39] to detect tables in particular scenarios. We refer readers to [40,41] for a more detailed summarization of these conventional approaches.…”

Section: Introductionmentioning

confidence: 99%

Robust Table Detection and Structure Recognition from Heterogeneous Document Images

Ma,

Lin,

Sun

et al. 2022

Preprint

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We introduce a new table detection and structure recognition approach named RobusTabNet to detect the boundaries of tables and reconstruct the cellular structure of the table from heterogeneous document images. For table detection, we propose to use CornerNet as a new region proposal network to generate higher quality table proposals for Faster R-CNN, which has significantly improved the localization accuracy of Faster R-CNN for table detection. Consequently, our table detection approach achieves state-of-the-art performance on three public table detection benchmarks, namely cTDaR TrackA, PubLayNet and IIIT-AR-13K, by only using a lightweight ResNet-18 backbone network. Furthermore, we propose a new split-and-merge based table structure recognition approach, in which a novel spatial CNN based separation line prediction module is proposed to split each detected table into a grid of cells, and a Grid CNN based cell merging module is applied to recover the spanning cells. As the spatial CNN module can effectively propagate contextual information across the whole table image, our table structure recognizer can robustly recognize tables with large blank spaces and geometrically distorted (even curved) tables. Thanks to these two techniques, our table structure recognition approach achieves state-of-the-art performance on three public benchmarks, including SciTSR, PubTabNet and cTDaR TrackB. Moreover, we have further demonstrated the advantages of our approach in recognizing tables with complex structures, large blank spaces, empty or spanning cells as well as geometrically distorted or even curved tables on a more challenging in-house dataset.

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A hybrid method for table detection from document image

Cited by 12 publications

References 20 publications

Robust Detection of Tables in Documents Using Scores from Table Cell Cores

Robust Detection of Tables in Documents Using Scores from Table Cell Cores

Financial table extraction in image documents

Robust Table Detection and Structure Recognition from Heterogeneous Document Images

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