latex-llm-cleaner 0.9.0

Flatten and clean LaTeX, PDF, and PPTX files for LLM consumption

LaTeX LLM Cleaner

A Python CLI tool that takes a LaTeX .tex file, compiled .pdf, or PowerPoint .pptx and produces a cleaned text version optimized for LLM consumption. Combines functionality from tools like flachtex, arxiv_latex_cleaner, and pandoc into a single utility.

Installation

pip install latex-llm-cleaner

This includes support for .tex, .pdf, and .pptx files out of the box.

For OCR-based equation recovery from PDFs (requires Python ≤ 3.13):

pip install 'latex-llm-cleaner[ocr]'

Global install

# Recommended — handles extras natively:
uv tool install latex-llm-cleaner
uv tool install 'latex-llm-cleaner[ocr]'

# Alternative (pipx):
pipx install latex-llm-cleaner
# For OCR with pipx, inject the heavy dependencies:
pipx inject latex-llm-cleaner surya-ocr 'transformers<5'

Note: OCR requires libjpeg headers. On macOS: brew install jpeg

From source

pip install -e ".[dev]"

Usage

latex-llm-cleaner paper.tex                    # output to stdout
latex-llm-cleaner paper.tex -o cleaned.tex     # output to file
latex-llm-cleaner paper.tex --no-bibliography  # skip bib inlining
latex-llm-cleaner thesis.pdf -o thesis.md      # extract text from PDF
latex-llm-cleaner thesis.pdf --ocr -o thesis.md  # OCR with LaTeX equation recovery
latex-llm-cleaner slides.pptx -o slides.md       # extract slides from PPTX
latex-llm-cleaner slides.pptx --notes -o slides.md  # include speaker notes

All features are on by default. Disable individual steps with --no-* flags:

latex-llm-cleaner INPUT_FILE [options]

Options:
  -o, --output FILE          Write to FILE (default: stdout)
  --no-flatten               Disable \input/\include flattening
  --no-comments              Disable comment removal
  --no-bibliography          Disable bibliography inlining
  --no-figures               Disable figure summary substitution
  --figure-summary-suffix S  Suffix for summary files (default: _summary.txt)
  --notes                    Include speaker notes (PPTX only)
  --ocr                      Use Surya vision OCR (recovers LaTeX equations, slower)
  --encoding ENC             File encoding (default: utf-8)
  -v, --verbose              Print processing info to stderr

PDF Input

For compiled PDFs (e.g., theses, published papers), latex-llm-cleaner extracts the text as markdown, preserving table structure and dropping images. Tables are output as markdown tables with | delimiters. Images are noted as [picture omitted] placeholders. The .tex pipeline flags (--no-flatten, etc.) are ignored for PDF input since they don't apply.

OCR mode (equation recovery)

The default PDF extraction is fast but loses display equations. For compiled LaTeX PDFs, the --ocr flag uses Surya vision-based OCR to recover equations as LaTeX source:

pip install 'latex-llm-cleaner[ocr]'
latex-llm-cleaner thesis.pdf --ocr -o thesis.md

This reconstructs inline math as $...$ and display equations as $$...$$ with full LaTeX notation. It's slower (~30s/page on Apple Silicon) but dramatically more accurate for math-heavy documents. Requires Python ≤ 3.13.

PPTX Input

Each slide becomes a markdown section with a heading (# Slide N: Title), separated by ---. Tables are output as markdown pipe-tables. Images are shown as [Image] placeholders unless a summary file is provided (see below).

Speaker notes are excluded by default. Use --notes to include them:

latex-llm-cleaner slides.pptx --notes -o slides.md

Equations stored as Office MathML (OMML) in the presentation are passed through as XML, which LLMs can read directly.

Image summaries for PPTX

Since images are embedded in PPTX files (no file paths), summaries use a slide/image numbering convention. Place summary files in the same directory as the .pptx:

slides.pptx
slide1_image1_summary.txt    ← first image on slide 1
slide3_image1_summary.txt    ← first image on slide 3
slide3_image2_summary.txt    ← second image on slide 3

The --figure-summary-suffix flag works here too (default: _summary.txt).

Processing Pipeline (.tex files)

The four steps run in this order (each operates on the output of the previous step):

1. Flatten includes — inline \input{}, \include{}, and \subfile{} recursively, with cycle detection
2. Remove comments — strip % comments while respecting \% escapes and verbatim environments
3. Inline bibliography — use a pre-compiled .bbl file if available (common in arXiv downloads), otherwise parse .bib files; replaces \bibliography{} with a \begin{thebibliography} block
4. Figure summary substitution — replace \includegraphics with text descriptions when summary files are available

Figure Summaries

LLMs (as of early 2026) are still poor at extracting precise information from complex figures in papers — dense plots, multi-panel layouts, small labels, etc. To work around this, latex-llm-cleaner can replace figures with equivalent text descriptions.

For each image (e.g., figs/plot.png), place a summary file alongside it with the configured suffix:

figs/plot.png              ← the image
figs/plot_summary.txt      ← the text summary

What to put in a summary

A summary should be data-equivalent to the figure: it should convey the same information a reader would get from looking at the figure, and nothing more. Avoid editorial commentary, interpretation, or conclusions that aren't visually present in the figure itself.

Good example:

Bar chart with four groups (A, B, C, D). Method X scores 0.92, 0.87, 0.76, 0.81. Method Y scores 0.85, 0.91, 0.80, 0.74. Error bars show standard deviation across 5 runs.

Bad example:

This figure clearly demonstrates the superiority of Method X, which aligns with our hypothesis.

Accessibility benefits

LaTeX has limited built-in support for producing accessible output — generated PDFs typically lack alt-text for figures, making them difficult to navigate with screen readers. These same summary files can serve as alt-text source material when compiling to tagged PDF or HTML, improving accessibility beyond the LLM use case.

Development

pip install -e ".[dev]"
pytest