# HybridTabNet: Towards Better Table Detection in Scanned Document Images
###### 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. table detection; table localization; deep learning; hybrid task cascade; object detection; deformable convolution; deep neural networks; computer vision; scanned document images; document image analysis +
Footnote †: journal: Medical Image Communication  
It has already been established in the community of table understanding [6; 7; 8; 9; 10; 11; 12] that table detection in document images hold two major challenges: (1) low inter-class variance (between different classes, such as tables, figures and charts) and (2) high intra-class variance (within the single class, such as tables with and without ruling lines). Due to these challenges, it is highly complex to come up with custom heuristics that can assist in developing robust and generic table detection system [13]. Thus far, we have seen a similar trend in the advancement of object detection algorithms in computer vision [14; 15; 16; 17] with the progress in table detection systems [6; 7; 8; 11; 12]. Although recent object detection frameworks have noticeably improved the performance of table detection approaches [7; 18], there is room in further reducing the close false positives. These case of close false positives can be resolved by leveraging the instance segmentation networks where an additional segmentation loss is added along with the bounding box and classification loss [12; 17; 19]. In this paper, we advanced the research for the problem of table detection in scanned document images by introducing the idea of implementing novel state-of-the-art hybrid task cascade networks [20] equipped with deformable convolutions [21]. Unlike prior methods, the proposed technique neither relies on prepossessing methods to transform the raw document images nor requires any rule-based post-processing method to refine the predictions. Moreover, the introduced method is not only applicable for scanned document images but also for PDF documents. Furthermore, the added deformable convolutions in our employed ResNeXt-101 backbone network solve the problem of detecting tables with arbitrary layouts. In particular, the contributions of this paper are summarized as follows:  
* 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. * During our exhaustive evaluation, we accomplish state-of-the-art performance on five well-recognized publicly available datasets for table detection in scanned document images. * We present the superiority of the proposed method by reporting results with a leave-one-out scheme on several table detection datasets.