PubTables-1M

This repository contains training and evaluation code for the paper "PubTables-1M: Towards a universal dataset and metrics for training and evaluating table extraction models".

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.

News

10/21/2021: The full PubTables-1M dataset has been officially released on Microsoft Research Open Data.

Getting the Data

PubTables-1M is available for download from Microsoft Research Open Data.

It comes in 5 tar.gz files:

• PubTables-1M-Image_Page_Detection_PASCAL_VOC.tar.gz
• PubTables-1M-Image_Page_Words_JSON.tar.gz
• PubTables-1M-Image_Table_Structure_PASCAL_VOC.tar.gz
• PubTables-1M-Image_Table_Words_JSON.tar.gz
• PubTables-1M-PDF_Annotations_JSON.tar.gz

To download from the command line:

1. Visit the dataset home page with a web browser and click Download in the top left corner. This will create a link to download the dataset from Azure with a unique access token for you that looks like https://msropendataset01.blob.core.windows.net/pubtables1m?[SAS_TOKEN_HERE].
2. You can then use the command line tool azcopy to download all of the files with the following command:

azcopy copy "https://msropendataset01.blob.core.windows.net/pubtables1m?[SAS_TOKEN_HERE]" "/path/to/your/download/folder/" --recursive

Then unzip each of the archives from the command line using:

tar -xzvf yourfile.tar.gz

Code Installation

Create a conda environment from the yml file and activate it as follows

conda env create -f environment.yml
conda activate tables-detr

Model Training

The code trains models for 2 different sets of table extraction tasks:

1. Table Detection
2. Table Structure Recognition + Functional Analysis

For a detailed description of these tasks and the models, please refer to the paper.

Sample training commands:

cd src
python main.py --data_root_dir /path/to/detection --data_type detection
python main.py --data_root_dir /path/to/structure --data_type structure

GriTS metric evaluation

GriTS metrics proposed in the paper can be evaluated once you have trained a model. We consider the model trained in the previous step. This script calculates all 4 variations presented in the paper. Based on the model, one can tune which variation to use. The table words dir path is not required for all variations but we use it in our case as PubTables1M contains this information.

python main.py --data_root_dir /path/to/structure --model_load_path /path/to/model --table_words_dir /path/to/table/words --mode grits

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

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