Table Detection

The ICDAR 2013 dataset consists of 229 training images and 233 testing images, with word-level annotations provided. It is the standard benchmark dataset for evaluating near-horizontal text detection. Source: Single Shot Text Detector with Regional Attention Image Source: https://plos.figshare.com/articles/Detection_examples_of_the_proposed_method_on_the_ICDAR_2013_dataset_17_/5325856

Form Understanding in Noisy Scanned Documents (FUNSD) comprises 199 real, fully annotated, scanned forms. The documents are noisy and vary widely in appearance, making form understanding (FoUn) a challenging task. The proposed dataset can be used for various tasks, including text detection, optical character recognition, spatial layout analysis, and entity labeling/linking. Source: FUNSD: A Dataset for Form Understanding in Noisy Scanned Documents Image source: https://guillaumejaume.github.io/FUNSD/

SciTSR is a large-scale table structure recognition dataset, which contains 15,000 tables in PDF format and their corresponding structure labels obtained from LaTeX source files. Source: https://github.com/Academic-Hammer/SciTSR

To address the need for a standard open domain table benchmark dataset, the author propose a novel weak supervision approach to automatically create the TableBank, which is orders of magnitude larger than existing human labeled datasets for table analysis. Distinct from traditional weakly supervised training set, our approach can obtain not only large scale but also high quality training data. Nowadays, there are a great number of electronic documents on the web such as Microsoft Word (.docx) and Latex (.tex) files. These online documents contain mark-up tags for tables in their source code by nature. Intuitively, one can manipulate these source code by adding bounding box using the mark-up language within each document. For Word documents, the internal Office XML code can be modified where the borderline of each table is identified. For Latex documents, the tex code can be also modified where bounding boxes of tables are recognized. In this way, high-quality labeled data is created for a variety of domains such as business documents, official fillings, research papers etc, which is tremendously beneficial for large-scale table analysis tasks. The TableBank dataset totally consists of 417,234 high quality labeled tables as well as their original documents in a variety of domains. Source: TableBank

The goal of PubTables-1M is to create a large, detailed, high-quality dataset for training and evaluating a wide variety of models for the tasks of table detection, table structure recognition, and functional analysis. It contains: - 460,589 annotated document pages containing tables for table detection. - 947,642 fully annotated tables including text content and complete location (bounding box) information for table structure recognition and functional analysis. - Full bounding boxes in both image and PDF coordinates for all table rows, columns, and cells (including blank cells), as well as other annotated structures such as column headers and projected row headers. - Rendered images of all tables and pages. - Bounding boxes and text for all words appearing in each table and page image. - Additional cell properties not used in the current model training. Additionally, cells in the headers are canonicalized and we implement multiple quality control steps to ensure the annotations are as free of noise as possible. For more details, please see our paper.

IIIT-AR-13K is created by manually annotating the bounding boxes of graphical or page objects in publicly available annual reports. This dataset contains a total of 13k annotated page images with objects in five different popular categories - table, figure, natural image, logo, and signature. It is the largest manually annotated dataset for graphical object detection. Source: IIIT-AR-13K: A New Dataset for Graphical Object Detection in Documents Image Source: http://cvit.iiit.ac.in/usodi/iiitar13k.php

Table is a compact and efficient form for summarizing and presenting correlative information in handwritten and printed archival documents, scientific journals, reports, financial statements and so on. Table recognition is fundamental for the extraction of information from structured documents. The ICDAR 2019 cTDaR evaluates two aspects of table analysis: table detection and recognition. The participating methods will be evaluated on a modern dataset and archival documents with printed and handwritten tables present.

We present TNCR, a new table dataset with varying image quality collected from free open source websites. TNCR dataset can be used for table detection in scanned document images and their classification into 5 different classes. TNCR contains 9428 high-quality labeled images. In this paper, we have implemented state-of-the-art deep learning-based methods for table detection to create several strong baselines. Cascade Mask R-CNN with ResNeXt-101-64x4d Backbone Network achieves the highest performance compared to other methods with a precision of 79.7%, recall of 89.8%, and f1 score of 84.4% on the TNCR dataset. We have made TNCR open source in the hope of encouraging more deep learning approaches to table detection, classification and structure recognition. Image source: https://github.com/abdoelsayed2016/TNCR_Dataset

The Construction Industry Steel Ordering Lists (CISOL) dataset comprises table-centric, real-world documents from the construction industry, annotated to facilitate the testing and training of deep learning models for table detection (TD) and table structure recognition (TSR). Access to the CISOL Leaderboard is provided via https://eval.ai/web/challenges/challenge-page/2257

STDW is a diverse large-scale dataset for table detection with more than seven thousand samples containing a wide variety of table structures collected from many diverse sources.