textpraline 0.1.0

TextPraline is a text normalization and refinement engine designed to prepare raw extracted content for reliable downstream processing. It cleans and stabilizes text coming from: PDF extractors , OCR pipelines, HTML scrapers. Praline removes structural noise and invisible corruption without altering meaning.

TextPraline 🍬

TextPraline is the layer used after text extraction to "sweeten" the extracted lines. Perfect before a RAG ingestion 😉

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What is TextPraline?

TextPraline is a deterministic, extraction-agnostic text refinement layer designed to run after:

• PDF text extraction
• OCR pipelines
• Vision encoders
• HTML scrapers
• Any raw text source

Its goal is simple:

Turn unstable, noisy, extraction-level text into clean, ingestion-ready text.

TextPraline does not rewrite content.
It stabilizes and refines it.

Supported Python versions: 3.10+

Why does this matter?

Raw extracted text is often:

• polluted with invisible Unicode artifacts
• broken by layout issues (PDF column noise, vertical text)
• full of repeated headers/footers
• corrupted by glyph artefacts or (cid:123) markers
• unstable in whitespace formatting
• inconsistent in punctuation normalization

These artifacts degrade:

• embedding quality
• chunking accuracy
• retrieval performance
• downstream reasoning

TextPraline ensures that what gets embedded is structurally stable and semantically clean.

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What TextPraline Does

Universal Cleaning

• Unicode normalization (NFKC)
• Removes:
  • control characters
  • zero-width characters
  • BOM
  • soft hyphen
  • variation selectors
  • Private Use Area (PUA)
  • replacement/noncharacters (�)
• Normalizes typographic quotes and dashes
• Stabilizes list markers

Extraction-Aware Cleaning (auto-detected)

• Removes glyph<...> artifacts
• Removes (cid:NNN) markers
• Unescapes HTML entities
• Collapses excessive blank lines
• Drops layout-noise blocks (PDF axis garbage / vertical text runs)
• Removes repeated header/footer lines (frequency-based)
• Removes common boilerplate patterns

Profiles

• safe → preserves structure, canonical bullet •
• markdown_safe → markdown-friendly -
• strict → aggressive whitespace collapsing

Monitoring

debug=True returns a PralineReport with metrics about:

• layout noise removed
• repeated lines removed
• ToC lines removed
• etc.

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Future Perspective

TextPraline currently focuses on post-extraction refinement.

A natural evolution would be:

• optional native PDF extraction adapters
• OCR integration modules
• multimodal-aware extraction helpers
• structured text reconstruction utilities

However, extraction is intentionally kept out of scope for now to preserve:

• modularity
• composability
• separation of concerns

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Contributing

TextPraline is built with real-world ingestion in mind.

If you encounter:

• extraction edge cases
• layout patterns not properly handled
• PDFs that break the heuristics
• corpus-specific artifacts

Please open an issue or propose a pull request.

We actively encourage improvements, new heuristics, and robustness enhancements.

Let’s make ingestion sweeter 🍬