Camelot review

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Camelot is an open-source Python library for developers who need to extract tabular data from PDF files. It returns tables as pandas DataFrames and includes a command-line interface for running extraction tasks. With self-hosted deployment and no-cost availability, it suits solo developers and teams that want table extraction in a Python workflow rather than a hosted subscription service.

Its parsing options cover ruled tables, whitespace-separated layouts, and text-aligned tables, with machine-learning-assisted parsing available for table structure. Standard parsers work with text-based PDFs; optional OCR components can handle text in scanned or image-only PDFs, but require the relevant extras. Camelot also provides parsing metrics, including accuracy and whitespace, and can stitch contiguous tables across multiple pages. These features make it a focused option when table layout and extraction quality need inspection, but it is not a general-purpose document extraction platform.

Extracted tables can be exported to CSV, JSON, Excel, HTML, Markdown, and SQLite, or kept as DataFrames for further processing. The command-line interface supports running extraction tasks outside an interactive Python session. Developers should choose Camelot when they can work with a self-hosted library and primarily need PDF table extraction; teams seeking a hosted subscription product or broader document extraction should consider alternatives.

Camelot pros and cons

• Where it wins
  • Offers multiple parsers for ruled, whitespace-separated, and text-aligned tables
  • Exports tables as pandas DataFrames, or to CSV, JSON, Excel, and more
  • Reports parsing metrics and can stitch contiguous tables across pages
• Where it doesn't
  • Standard parsers require text-based PDFs
  • OCR is optional and requires the relevant extras
  • Focused on table extraction rather than broader document data

Camelot fact sheet, pricing and score →

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Last updated 29 Oct 2026 · How we research and update