awesome-table-recognition
github.com/cv-small-snails/awesome-table-recognition ↗A curated list of resources dedicated to table recognition
Use this list with your AI agent
Add the Context Awesome MCP server to Claude, Cursor, or any MCP client, then ask:
"Show me 2.1 introduction resources from awesome-table-recognition"
Installation instructions →What's inside
2. Datasets
- FinTabNet2.1 Introduction
English This dataset contains complex tables from the annual reports of S&P 500 companies with detailed table structure annotations to help train and test structure recognition.
- PubTables-1M2.1 Introduction
English 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.
- PubTabNet2.1 Introduction
English PubTabNet is a large dataset for image-based table recognition, containing 568k+ images of tabular data annotated with the corresponding HTML representation of the tables. It contain cell Topology, cell content and non-blank cell location groudtruth
- SciTSR2.1 Introduction
* English 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. It contain cell Topology, cell content groudtruth
- SynthTabNet2.1 Introduction
English SynthTabNet is a synthetically generated dataset that contains annotated images of data in tabular layouts. It contain 600k train image, All parts are divided into Train, Test and Val splits (80%, 10%, 10%). It contain cell Topology, cell content and all cell location groudtruth
- TableBank2.1 Introduction
English TableBank is a new image-based table detection and recognition dataset built with novel weak supervision from Word and Latex documents on the internet, contains 417K high-quality labeled tables. It only contain cell Topology groudtruth
3. Other technical solutions
- Solution Report PPTPRCV2021 Table Recognition Technology Challenge
- Solution Report PPTPRCV2021 Table Recognition Technology Challenge
- Solution Report PPTPRCV2021 Table Recognition Technology Challenge
- Solution Report PPTICDAR 2021 Competition on Scientfic Literature Parsing TaskB: Table Recognition to HTML
- Solution Report PPTICDAR 2021 Competition on Scientfic Literature Parsing TaskB: Table Recognition to HTML
- Solution Report VideoICDAR 2021 Competition on Scientfic Literature Parsing TaskB: Table Recognition to HTML
Showing a sample of 16 resources. View the full list on GitHub →