Topics / #table-extraction

Table-extraction projects on GitHub

Extracting tabular data from PDFs and documents.

As of , the most-starred Table-extraction projects on GitHub are docling (69K stars), surya (21.5K stars) and chandra (12.5K stars). GitHub.Locker tracks 8 Table-extraction repositories with 135.9K stars combined. The fastest-growing on October 17 was docling, with +43 stars in a day.

8Projects
135.9KStars in total
+68Stars in a day
Browse in the catalog →github.com/topics/table-extraction ↗

Hot right now

Most stars gained in a day · Oct 17

+43

docling

docling-project/docling · ★ 69K

Docling parses diverse document formats such as PDF, DOCX, PPTX, XLSX, HTML and more, turning them into a unified representation ready for generative AI pipelines.

+8

PyMuPDF

pymupdf/PyMuPDF · ★ 10.9K

PyMuPDF is a high-performance Python library for extracting, analyzing, converting, rendering, and manipulating PDF and other document formats, built on the MuPDF C engine.

+6

surya

datalab-to/surya · ★ 21.5K

Surya is Datalab's open-source document OCR system built around a 650M parameter model. It reads text from images and PDFs, and also does layout analysis, reading order, and table recognition.

+6

chandra

datalab-to/chandra · ★ 12.5K

Chandra OCR 2 is a state-of-the-art OCR model that converts images and PDFs into structured HTML, Markdown, or JSON while preserving the original layout. It handles complex tables, forms, handwriting, and math, and supports over 90 languages.

The all-time top

8 projects · by stars

  1. #1 · docling-project/docling

    docling

    Docling parses diverse document formats such as PDF, DOCX, PPTX, XLSX, HTML and more, turning them into a unified representation ready for generative AI pipelines.

    69K
  2. #2 · datalab-to/surya

    surya

    Surya is Datalab's open-source document OCR system built around a 650M parameter model. It reads text from images and PDFs, and also does layout analysis, reading order, and table recognition.

    21.5K
  3. #3 · datalab-to/chandra

    chandra

    Chandra OCR 2 is a state-of-the-art OCR model that converts images and PDFs into structured HTML, Markdown, or JSON while preserving the original layout. It handles complex tables, forms, handwriting, and math, and supports over 90 languages.

    12.5K
  1. 4

    PyMuPDF

    10.9Kpymupdf/PyMuPDF · Python

    PyMuPDF is a high-performance Python library for extracting, analyzing, converting, rendering, and manipulating PDF and other document formats, built on the MuPDF C engine.

  2. 5

    pdfplumber

    10.8Kjsvine/pdfplumber · Python

    pdfplumber is a Python library that inspects PDFs in detail, exposing each character, line, rectangle, and curve, and makes it easy to extract text and tables from them.

  3. 6

    PDF-Extract-Kit

    10Kopendatalab/PDF-Extract-Kit · Python

    PDF-Extract-Kit is an open-source Python toolkit that bundles state-of-the-art models for layout detection, formula detection, formula recognition, OCR, and table recognition to pull high-quality content out of complex PDF documents.

  4. 7

    ParseBench

    603run-llama/ParseBench · Python

    ParseBench is a benchmark that measures how well document parsing tools (OCR engines, VLMs, commercial parsing APIs) convert PDFs into structured output that AI agents can reliably act on, rather than just text that looks similar to the original.

  5. 8

    nativePDF-structurer

    409crunz-ai/nativePDF-structurer · Python

    NativePDF Structurer converts large digital-native PDFs — equipment manuals, repair manuals, technical catalogs — into structured Markdown, images and table CSVs without using OCR or any machine-learning model, so long vendor documents can reliably feed into RAG pipelines.

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