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.