Abstract
Tables in document images are an important entity since they contain crucial information. Therefore, accurate table detection can significantly improve the information extraction from documents. In this work, we present a novel end-to-end trainable pipeline, HybridTabNet, for table detection in scanned document images. Our two-stage table detector uses the ResNeXt-101 backbone for feature extraction and Hybrid Task Cascade (HTC) to localize the tables in scanned document images. Moreover, we replace conventional convolutions with deformable convolutions in the backbone network. This enables our network to detect tables of arbitrary layouts precisely. We evaluate our approach comprehensively on ICDAR-13, ICDAR-17 POD, ICDAR-19, TableBank, Marmot, and UNLV. Apart from the ICDAR-17 POD dataset, our proposed HybridTabNet outperformed earlier state-of-the-art results without depending on pre- and post-processing steps. Furthermore, to investigate how the proposed method generalizes unseen data, we conduct an exhaustive leave-one-out-evaluation. In comparison to prior state-of-the-art results, our method reduced the relative error by 27.57% on ICDAR-2019-TrackA-Modern, 42.64% on TableBank (Latex), 41.33% on TableBank (Word), 55.73% on TableBank (Latex + Word), 10% on Marmot, and 9.67% on the UNLV dataset. The achieved results reflect the superior performance of the proposed method.
Highlights
Rapid growth in the digitization of documents has alleviated the demand for methods that can process information accurately and efficiently
While state-of-the-art OCR (Optical Character Recognition) [2,3,4] systems can process the raw text in document images, they are vulnerable to extracting information from graphical page objects [5]
We propose HybridTabNet, a novel table detection system by incorporating deformable convolutions in the backbone network of an instance segmentation-based Hybrid Task Cascade (HTC) network
Summary
Rapid growth in the digitization of documents has alleviated the demand for methods that can process information accurately and efficiently. The digital documents contain various graphical page objects, such as tables, figures, and formulas [1]. While state-of-the-art OCR (Optical Character Recognition) [2,3,4] systems can process the raw text in document images, they are vulnerable to extracting information from graphical page objects [5]. It is important to first localize these page objects in document images such that the information can be retrieved accurately. Tables are one of the most important page objects in documents because they summarize a major piece of information compactly and precisely. We have taken a step forward towards improving the table detection methods in document images
Sign-in/Register to view highlights & summary Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
