NameetP/pdfmux

v1.5.0 Feature

This release adds 3 notable features for engineering teams evaluating rollout.

Published 1mo Developer Productivity

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✓ No known CVEs patched

Read the diff → Tool health → What is this tool? →

✓ No known CVEs patched in this version

Topics

ai-agent docling document-parsing llm mcp ocr

+8 more

opendataloader pdf pdf-extraction pdf-to-json pdf-to-markdown python self-healing structured-extraction

Summary

AI summary

Overall benchmark score improved from 0.867 to 0.905.

Full changelog

What's New in v1.5.0

Benchmark Results

0.905 overall benchmark score on opendataloader-bench (200 docs)
Up from 0.867 (v1.3.0) — a +4.4% improvement
100% confidence score across all documents
98 docs improved, only 3 regressed

Key Improvements

Image Table OCR (TEDS: 0.887 → 0.911, +2.7%)

Integrated RapidOCR for tables embedded as images
Smart filtering: 50% fill rate + 30% numeric cell thresholds to avoid false positives on charts

ML Heading Classifier (MHS: 0.844 → 0.852, +0.9%)

ML-based fallback for heading detection when heuristics fail
Improved heading cleanup for cleaner document structure

Column-Aware Reading Order (NID: 0.910 → 0.920)

A/B column reordering: detects multi-column pages, compares both orderings, picks the better one
Safe by design — worst case is no-op (original text preserved)
Conservative detection (200pt gap threshold) to avoid false positives

Install

pip install pdfmux==1.5.0

Full Changelog: https://github.com/NameetP/pdfmux/compare/v1.3.0...v1.5.0

View diff on GitHub

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About NameetP/pdfmux

PDF extraction router with built-in MCP server. Classifies each page (digital, scanned, tables) and routes to the best backend (PyMuPDF, Docling, OCR, or optional LLM fallback)

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