Accurate Multi-Column Extraction For Fast Non-ML PDF Parsing
EdgeParse extracts structured Markdown, JSON, and HTML from born-digital PDFs at 83 times the speed of ML parsers with no model, reaching 118 GitHub stars from developers building RAG and document pipelines, but its issues show the accuracy edge cases that decide adoption: multi-column PDFs are not parsed in correct reading order, and a text-page-separator flag is accepted but not applied. Teams want fast, cheap, deterministic parsing that gets layout right. The wedge is a non-ML PDF parser that nails multi-column reading order and honors its options, the correctness bar that justifies skipping heavy ML extractors.
Problem Statement
A team building a RAG pipeline needs to parse thousands of born-digital PDFs cheaply and fast, so they adopt a non-ML parser, but multi-column documents come out with text interleaved in the wrong reading order, corrupting downstream chunks, and a configuration flag they set is silently ignored. The speed is great, but incorrect layout extraction poisons the pipeline, pushing them back to slow, expensive ML parsers.
The Idea
A fast, deterministic non-ML PDF parser that extracts born-digital documents into Markdown and JSON with correct multi-column reading order for RAG and document pipelines.
Why Now
RAG and document pipelines exploded in 2026 and teams want to avoid slow, costly ML PDF extractors for born-digital files, and EdgeParse's speed advantage is compelling, but its multi-column reading-order and flag-handling issues show that layout accuracy is the bar a non-ML parser must clear to be trusted over the ML tools.
Target User
Developers building RAG, search, and document-processing pipelines over born-digital PDFs
Target Market
Document parsing and extraction developer tooling
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