Skip to content

Rust Python

⚡ Tablers

A blazingly fast PDF table extraction library with python API powered by Rust

License: MIT PyPI version Python versions pdm-managed

Features#

  • 🚀 Blazingly Fast - Core algorithms written in Rust for maximum performance
  • 🐍 Pythonic API - Easy-to-use Python interface with full type hints
  • 📄 Edge Detection - Accurate table detection using line and rectangle edge analysis
  • 📝 Text Extraction - Extract text content from table cells with configurable settings
  • 📤 Multiple Export Formats - Export tables to CSV, Markdown, and HTML
  • 🔐 Encrypted PDFs - Support for password-protected PDF documents
  • 💾 Memory Efficient - Lazy page loading for handling large PDF files
  • 🖥️ Cross-Platform - Works on Windows, Linux, and macOS

Why Tablers?#

This project draws significant inspiration from the table extraction modules of pdfplumber and PyMuPDF. Compared to pdfplumber and PyMuPDF, tablers has the following advantages:

  • High Performance: Utilizes Rust for high-performance PDF processing
  • Higher Accuracy: Tablers optimizes some table detection algorithms to address table extraction problems that other libraries have not fully solved, including:
  • More Configurable: Supports customizable table filter settings (min_rows, min_columns, include_single_cell, e.g., see this issue)
  • Clean Python Dependencies: No external python dependencies required

Benchmark#

Benchmarked on the ICDAR 2013 Table Competition dataset, evaluating both extraction speed and accuracy across tablers, PyMuPDF, pdfplumber, and camelot. All libraries use their default configuration for table extraction. PyMuPDF excludes tables that have only one row or only one column (see PyMuPDF#3987), and this behaviour is not configurable; among the compared libraries, only tablers allows configuring minimum row/column counts. For a fair comparison, the benchmark therefore includes both tablers (default) and tablers (min 2×2) — the latter with min_rows=2 and min_columns=2 so that single-row/single-column tables are filtered out in the same way as in PyMuPDF. For more on the libraries and settings, see the Libraries compared section in tablers-benchmark.

Table Extraction Benchmark

For more details, please refer to the tablers-benchmark repository.

Note#

This solution is primarily designed for text-based PDFs and does not support scanned PDFs.

Installation#

pip install tablers

Quick Start#

Basic Table Extraction#

from tablers import Document, find_tables

# Open a PDF document
doc = Document("example.pdf")

# Extract tables from each page
for page in doc.pages():
    tables = find_tables(page, extract_text=True)
    for table in tables:
        print(f"Found table with {len(table.cells)} cells")
        for cell in table.cells:
            print(f"  Cell: {cell.text} at {cell.bbox}")

doc.close()

Using Context Manager#

from tablers import Document, find_tables

with Document("example.pdf") as doc:
    page = doc.get_page(0)  # Get first page
    tables = find_tables(page, extract_text=True)

    for table in tables:
        print(f"Table bbox: {table.bbox}")

For more advanced usage, please refer to the documents.

Requirements#

  • Python >= 3.10
  • Supported platforms: Windows (x64), Linux (x64) with glibc >= 2.28, macOS (ARM64)

License#

This project is licensed under the MIT License - see the LICENSE file for details.

Acknowledgments#