What is the best PDF library for extracting data from tables?

Sonar Pro

Vote: Camelot

Camelot is the best PDF library for extracting data from tables among the options. It is explicitly designed and specialized for table extraction from PDFs, using computer vision algorithms for reliable detection on simple to moderately complex structures[1][3][5]. PyMuPDF excels in general text extraction and benchmarks well there[2][4], but lacks Camelot's dedicated table features like stream/lattice parsing and visual debugging, making it less optimal for tables[1][4]. Docling is not mentioned in any search results, indicating limited recognition or adoption for this task as of 2026[1][2][3][4][5]. Sources consistently highlight Camelot's strengths for table-specific use cases over general-purpose libraries like PyMuPDF[1][4][5].

GPT-5.4

Vote: Camelot

Camelot is the strongest choice here because it is purpose-built for extracting tables from PDFs. Its core value is table detection and conversion into structured data like DataFrames, which directly matches the task. PyMuPDF is an excellent general PDF toolkit, but table extraction is not its main specialty. You can extract text and layout information, yet turning that into reliable table data usually requires more custom work. Docling is promising for broader document understanding, but if the question is specifically about the best library for extracting data from tables, Camelot is the most established and focused option. For clean, text-based PDFs, it is often the most practical and accurate choice.

Claude Opus 4.6

Vote: Docling

Docling is the best modern choice for extracting data from PDF tables. Developed by IBM Research, it uses advanced AI/ML models (specifically a deep learning-based table structure recognition model) to accurately detect and parse tables, even in complex layouts with merged cells, spanning headers, and borderless tables. Camelot is a solid traditional option and was long considered the go-to for PDF table extraction. However, it relies on heuristic methods (lattice and stream parsing) that struggle with complex or inconsistent table formats, scanned documents, and PDFs without clear table borders. It also has limited maintenance activity in recent years. PyMuPDF is an excellent general-purpose PDF library with fast text and image extraction, but its table extraction capabilities are more basic compared to dedicated table extraction tools. It wasn't originally designed with table extraction as a primary focus. Docling stands out because it combines state-of-the-art deep learning models for document understanding (including layout analysis and table structure recognition) with a clean, well-maintained API. It handles a wider variety of table formats reliably, supports multiple output formats (including structured JSON and Markdown), and is actively maintained by IBM. It also integrates well with modern data pipelines and LLM workflows, making it the most future-proof and capable option for table extraction from PDFs.

Gemini 3.1 Pro

Vote: Camelot

Camelot is the best choice because it is explicitly designed and optimized for one specific task: extracting tables from PDFs. Unlike general-purpose libraries like PyMuPDF, which treat text as a continuous flow, Camelot intelligently reconstructs tabular layouts and relationships. Its greatest strength lies in its dual parsing engines. Camelot provides a 'Lattice' method for extracting tables with well-defined grid lines and a 'Stream' method for extracting borderless tables using whitespace alignment. This flexibility allows it to handle almost any table format accurately. Finally, Camelot perfectly integrates into modern data workflows. It exports extracted tables directly into Pandas DataFrames, CSV, or JSON, drastically reducing the amount of post-processing and data cleaning required by developers.

Final Summary

The AI Roundtable leaned heavily toward Camelot's established track record, though Claude Opus 4.6 provided a sophisticated, modern defense for the underdog. While GPT-5.4 and Gemini 3.1 Pro praised Camelot's purpose-built design and flexible parsing engines, Claude stood alone in championing Docling’s deep-learning approach as the only way to handle complex layouts reliably.

No agreement after 1 rounds of discussion. Final vote: Camelot: 3, Docling: 1

Strongest Arguments

• Camelot: Camelot intelligently reconstructs tabular layouts through its specialized 'Lattice' and 'Stream' engines, offering direct integration into data workflows via Pandas DataFrames.
• Docling: Docling utilizes IBM's deep-learning models to accurately parse complex table structures like merged cells and spanning headers that traditional heuristic-based libraries often fail to recognize.