TableBank: Table Benchmark for Image-based Table Detection and Recognition

We present TableBank, a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet. Existing research for image-based table detection and recognition usually fine-tunes pre-trained models on out-of-domain data with a few thousand human-labeled examples, which is difficult to generalize on real-world applications. With TableBank that contains 417K high quality labeled tables, we build several strong baselines using state-of-the-art models with deep neural networks. We make TableBank publicly available and hope it will empower more deep learning approaches in the table detection and recognition task. The dataset and models can be downloaded from https://github.com/doc-analysis/TableBank.

[1]  Alexander M. Rush,et al.  OpenNMT: Open-Source Toolkit for Neural Machine Translation , 2017, ACL.

[2]  Rangachar Kasturi,et al.  Structural recognition of tabulated data , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[3]  Jean-Yves Ramel,et al.  Detection, extraction and representation of tables , 2003, Seventh International Conference on Document Analysis and Recognition, 2003. Proceedings..

[4]  Alexey O. Shigarov,et al.  Configurable Table Structure Recognition in Untagged PDF documents , 2016, DocEng.

[5]  Kaiming He,et al.  Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[6]  Yasushi Makihara,et al.  Object recognition supported by user interaction for service robots , 2002, Object recognition supported by user interaction for service robots.

[7]  Pietro Perona,et al.  Microsoft COCO: Common Objects in Context , 2014, ECCV.

[8]  Thomas Kieninger,et al.  The T-Recs Table Recognition and Analysis System , 1998, Document Analysis Systems.

[9]  Tamir Hassan,et al.  Table Recognition and Understanding from PDF Files , 2007, Ninth International Conference on Document Analysis and Recognition (ICDAR 2007).

[10]  Muhammad Imran Malik,et al.  Table Detection Using Deep Learning , 2017, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR).

[11]  Yasuaki Nakano,et al.  Document Analysis Systems: Theory and Practice , 2003, Lecture Notes in Computer Science.

[12]  Miao Fan,et al.  Table Region Detection on Large-scale PDF Files without Labeled Data , 2015, ArXiv.

[13]  Y. Hirayama,et al.  A method for table structure analysis using DP matching , 1995, Proceedings of 3rd International Conference on Document Analysis and Recognition.

[14]  D. H. Chang,et al.  Extracting Tabular Information From Text Files , 1996 .

[15]  Trevor Darrell,et al.  Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation , 2013, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[16]  Clément Chatelain,et al.  Learning to Detect Tables in Scanned Document Images Using Line Information , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[17]  Waleed Ammar,et al.  Extracting Scientific Figures with Distantly Supervised Neural Networks , 2018, JCDL.

[18]  Alexander M. Rush,et al.  What You Get Is What You See: A Visual Markup Decompiler , 2016, ArXiv.

[19]  Aravaipa Canyon Basin,et al.  Volume 3 , 2012, Journal of Diabetes Investigation.

[20]  Zhi Tang,et al.  A Table Detection Method for PDF Documents Based on Convolutional Neural Networks , 2016, 2016 12th IAPR Workshop on Document Analysis Systems (DAS).

[21]  Katharina Kaiser,et al.  pdf2table: A Method to Extract Table Information from PDF Files , 2005, IICAI.

[22]  Tamir Hassan,et al.  ICDAR 2013 Table Competition , 2013, 2013 12th International Conference on Document Analysis and Recognition.

[23]  Andreas Dengel,et al.  DeepDeSRT: Deep Learning for Detection and Structure Recognition of Tables in Document Images , 2017, 2017 14th IAPR International Conference on Document Analysis and Recognition (ICDAR).

[24]  Faisal Shafait,et al.  Table detection in heterogeneous documents , 2010, DAS '10.

[25]  Daniel P. Lopresti,et al.  Medium-independent table detection , 1999, Electronic Imaging.

[26]  Daniel P. Lopresti,et al.  Table structure recognition and its evaluation , 2000, IS&T/SPIE Electronic Imaging.

[27]  Thomas Kieninger,et al.  An open approach towards the benchmarking of table structure recognition systems , 2010, DAS '10.

[28]  Zhuowen Tu,et al.  Aggregated Residual Transformations for Deep Neural Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[29]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[30]  Francesca Cesarini,et al.  Trainable Table Location in Document Images , 2002, ICPR.

[31]  Ross B. Girshick,et al.  Fast R-CNN , 2015, 1504.08083.

[32]  Ana Costa e Silva,et al.  2009 10th International Conference on Document Analysis and Recognition Learning Rich Hidden Markov Models in Document Analysis: Table Location , 2022 .

[33]  Katsuhiko Itonori,et al.  Table structure recognition based on textblock arrangement and ruled line position , 1993, Proceedings of 2nd International Conference on Document Analysis and Recognition (ICDAR '93).

[34]  Ioannis Pratikakis,et al.  Automatic Table Detection in Document Images , 2005, ICAPR.