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pdftext

https://github.com/datalab-to/pdftext
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Category
AI Tools
Rank
No. 634Tools index
Pricing
Open Source
Type
TOOL
Builder
datalab-to
GitHub
721 stars
Latest release
v0.7.1
Date

About

Fast structured text extraction from PDFs. The fast-path companion to Marker for when you just need clean text out of a document.

What it does

PDFText reads a PDF’s embedded character data through pypdfium2, captures coordinates and font details, then applies heuristics to assemble characters into spans, lines, and paragraph-like blocks. It can return plain text, page-structured JSON, or text fragments from supplied table regions.

Why it's ranked here

The tool has a focused design, an Apache license, useful structured output, and both command-line and Python interfaces. Its tests cover practical failure points such as encryption, invalid page ranges, rotated documents, malformed Unicode mappings, and non-ASCII console output. The main reservation is dependence on PDFium’s text ordering and Unicode data.

What's good

Structured output preserves page geometry, rotation, font metadata, character indexes, links, and optional character details. Plain-text cleanup normalizes whitespace and ligatures, removes control characters, and handles line-breaking hyphens. It also supports encrypted files, selected pages, form flattening, reading-order sorting, and process-based extraction for sufficiently large documents.

Tradeoffs

PDFium is not thread-safe, so callers must use processes instead of concurrent threads. File-like inputs cannot use worker processes, and smaller workloads remain serial. Arabic and Hebrew usually arrive in visual rather than logical order. Complex scripts and emoji depend on the PDF’s Unicode map. Reading-order sorting and block grouping are heuristic.

How to use it well

Use it for Python pipelines or shell jobs that need embedded PDF text plus layout and font context. Prefer plain output for search or indexing, and structured output when coordinates, links, or typography matter. Supply detected table regions when extracting cell fragments. It does not detect tables itself, and the described architecture does not provide OCR for image-only pages.

Technical notes+

pyproject.toml packages pdftext with Poetry, requires Python 3.10+, pins pypdfium2, and registers the pdftext CLI. pdftext/extraction.py exposes plain_text_output, paginated_plain_text_output, dictionary_output, and table_output; _get_pages switches to ProcessPoolExecutor only when page counts meet the threshold in pdftext/settings.py, while file-like inputs stay serial. pdftext/pdf/chars.py extracts character codes, angles, boxes, and interned font records into NumPy-backed PageChars, replaces lone surrogates, and deduplicates repeated rendered words. pdftext/pdf/pages.py heuristically forms spans, lines, blocks, superscripts, and subscripts. pdftext/pdf/links.py maps PDF annotations onto reconstructed spans and internal references. pdftext/postprocessing.py normalizes whitespace, ligatures, controls, and hyphenation. tests/test_cli.py exercises text and JSON output, page-range validation, UTF-8 console behavior, and encrypted PDFs.

Observed

License
Apache-2.0
Primary language
Python
Installation
Published for pip installation and packaged with Poetry
Runtime
Requires Python 3.10 or newer
Interfaces
Command-line interface and importable Python library
Outputs
Plain text, page-structured JSON, and text fragments from supplied table regions
Concurrency
Process-based workers are supported; PDFium calls are not thread-safe
Tests
Repository includes pytest fixtures and command-line tests

Read from README.md, pyproject.toml, extract_text.py, pdftext/schema.py, pdftext/tables.py, pdftext/settings.py, pdftext/extraction.py, pdftext/postprocessing.py, pdftext/pdf/chars.py, pdftext/pdf/links.py, pdftext/pdf/pages.py, pdftext/pdf/utils.py, pdftext/scripts/extract_text.py, tests/conftest.py, tests/test_cli.py.

What it can do

  • Extract structured text from PDF documents

    PDF file → Structured text

  • Convert PDF content to clean readable text

    PDF file → Clean text

  • Parse PDF documents for text content

    PDF file → Parsed text data

  • Extract text while preserving document structure

    PDF file → Formatted text with structure

Tags

pdftext-extractionpythondocument-aidatalab

Tech Stack

Python

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