reap-pdf 0.1.7

Rust-first PDF text extraction with geometry-aware search and optional Python bindings

reap

reap is a low-level PDF parser written in Rust, with Python bindings for fast geometry-aware text extraction and spatial queries. It is designed for fast spatial lookups, but also supports full text extraction and regex queries.

Installing

uv add reap-pdf

Usage

from reap import Rectangle, TextBlockIndex

index = TextBlockIndex.from_path("my_w2.pdf")
blocks = index.search_regex("Wage and Tax Statement")
print(blocks)
#> [TextBlock("Wage and Tax Statement")]

# Search to the right of the label.
search_rect = Rectangle(
    top=blocks[0].rect.top,
    left=blocks[0].rect.right,
    bottom=blocks[0].rect.bottom,
    right=blocks[0].rect.right + 100,
)
year = index.search(search_rect, overlap=0.3)
print(year[0].text)
#> 2026

Details

• TextBlockIndex builds an index of word level TextBlocks, across all pages.
• TextBlockIndex.search_regex works on the entire text corpus, in reading order, but returns merged TextBlocks that match the query into one TextBlock.
• TextBlockIndex.text returns text in reading order.
• All pages are merged, and considered as one big page, with each page's coordinates stacked below each other. Coordinate units are points.

Advanced use cases

• TextBlockIndex.scoped creates a TextBlockIndex scoped to a specific region, with support for merging text blocks within the region into multi-word TextBlocks.
• TextBlock.chars returns the per-block extracted TextChar list.

Scope

• OCR is not in-scope. Reap focuses entirely on extracting visible text present in the PDF.
• No promises for all PDF spec details to be supported, support is added as the need arises.
• Best effort to extract all visible text in PDFs.

Comparisons

Extraction differences

PyMuPDF and pdfminer will both extract hidden text, eg. white text on a white background, which is often not desirable. reap performs visibility checks during extraction, to ensure that invisible text does not get included in the final output.

Speed

Text extraction performance measured for comparability, as neither alternative offers proper spatial query support.

Library	Text Extraction
reap	0.850ms median
PyMuPDF	5ms median
pdfminer	220ms median

Speed comparisons vary greatly depending on the PDF, with smaller PDFs reap is roughly 10x faster than PyMuPDF, and 500x faster than pdfminer, with larger PDFs the advantage narrows to roughly 5x and 250x respectively. Tests were conducted on a private test suite of PDFs with varying complexity