dongler 0.3.15

Fast Rust-native PDF and document extraction for Python, with Markdown, LaTeX, and JSON output.

Dongler

Turn PDFs and documents into clean Markdown, LaTeX, or structured JSON.
Rust-native, runs locally, no hosted service · API key · LLM · or OCR for digitally born PDFs.

Documentation  ·  Quick start  ·  API reference  ·  LLM context

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Dongler is built around a path-first workflow: load a file, inspect the document object when you need to, then render the output format your pipeline wants. One Rust core powers the CLI, Python, TypeScript, and Rust APIs, so the extraction model is identical everywhere.

Install

cargo install dongler                  # CLI + Rust
pip install dongler                    # Python
npm install @cristianexer/dongler      # Node / TypeScript

For the Rust library, depend on dongler-core. The public dongler crate is the CLI package.

To run extraction in the browser or another WebAssembly host, build the dongler-wasm crate (make build-wasm). It exposes the same engine over an in-memory byte API, so files can be parsed client-side with no server. See crates/dongler-wasm/README.md.

Parse a PDF

Python import dongler doc = dongler.load("report.pdf") markdown = doc.to_markdown() latex = doc.to_latex() data = doc.to_dict()

TypeScript import { load } from "@cristianexer/dongler"; const doc = load("report.pdf"); const markdown = doc.toMarkdown(); const latex = doc.toLatex(); const data = doc.toObject();

Rust use dongler_core::load_path; let doc = load_path("report.pdf")?; println!("{}", doc.to_markdown()?);

What you get

📄 Markdown · LaTeX · JSON Three renderers from one document object — headings, tables, lists, figures, and emphasis. ⚡ Native speed, local runtime A custom Rust PDF parser with rayon page-parallelism. No hosted service, API key, LLM, or OCR for born-digital PDFs. 🧱 Structured document model Page, block, table, image, span, warning, and metadata fields — with source anchors back to PDF objects.

🧩 One API across stacks The same extraction model in Python, Node.js, Rust, and the CLI. 📦 Pipeline-friendly batches Batch APIs return one result per file — a single bad document never stops the job. 🔌 Beyond PDF Native extraction for DOCX/XLSX/PPTX, ODT/ODS/ODP, HTML/XML, EML, JSON/JSONL, CSV/TSV, images, and archives.

Why Dongler

Use Dongler when the job starts with a document path and the next step needs useful text quickly:

• Convert PDFs to Markdown for indexing, review, or RAG ingestion.
• Keep page/block/table/image metadata available through JSON.
• Run locally in scripts, services, queues, notebooks, and shell workflows.
• Use the same extraction model across Python, Node.js, Rust, and the CLI.

Supported inputs

Dongler focuses on digitally born PDFs and also supports native extraction for DOCX, XLSX, PPTX, ODT/ODS/ODP, HTML/XML, EML, JSON/JSONL, CSV/TSV, image metadata including TIFF, and plain text/Markdown/TeX. It also reads gzip-compressed text/JSON/XML/CSV corpus files, bare gzip source files, and zip/tar/tar.gz source packages. Legacy binary Office and Outlook containers are detected and return explicit planned-format errors until their engines land.

Batch processing

One result per file — a bad or unsupported document does not stop the batch.

import dongler

for result in dongler.load_many(["notes.txt", "invoice.pdf"]):
    if result["ok"]:
        print(result["document"].to_markdown())
    else:
        print(f"{result['path']}: {result['error']}")

CLI

dongler --version
dongler inspect invoice.pdf
dongler extract report.docx --format markdown
dongler extract book.xlsx   --format json
dongler extract notes.txt   --format latex

PDF extraction through the CLI uses the same Rust-native engine as the Rust, Python, and TypeScript packages.

Documentation

• Documentation
• Quick start
• Developer guide
• API reference
• LLM context

Benchmarks

Dongler sustains ~90 born-digital pages/second on a single host with no GPU, and on olmOCR-Bench (1,403 real PDFs, 7,019 unit checks) its table-structure pass rate improved +9.7% relative over the previous release on the identical harness. Full results, methodology, and before/after examples are on the benchmarks page.

Generated by scripts/run-benchmarks.py on 2026-05-28 19:56:50 BST. Local cache: 1894.9 MB. All discovered files per dataset. olmOCR-Bench re-measured 2026-06-11 after the table-extraction work; the full table regenerates on the next release run.

Coverage is parse / bbox / anchors. Ground-truth accuracy is token-F1, olmOCR unit-check pass rate, or full-image IoU; n/a means no local target signal. Detailed task names, discovery counts, native scores, and notes are recorded in eval/out/benchmarks/latest.json.

Dataset	Status	Local data	Docs eval	Coverage	Pages/sec	GT accuracy
DocLayNet	missing	0.0 MB	0	n/a / n/a / n/a	n/a	n/a
PubLayNet	missing	0.0 MB	0	n/a / n/a / n/a	n/a	n/a
DocBank	ok	735.6 MB	200	100.0% / 100.0% / 100.0%	81.94	89.5%
PubTabNet	missing	0.0 MB	0	n/a / n/a / n/a	n/a	n/a
PubTables-1M	missing	0.0 MB	0	n/a / n/a / n/a	n/a	n/a
TableBank	ok	1.6 MB	10	100.0% / 100.0% / 100.0%	193.45	100.0%
FUNSD	ok	42.6 MB	200	100.0% / 48.9% / 100.0%	96.09	100.0%
SROIE	ok	627.3 MB	1264	100.0% / 92.7% / 100.0%	231.85	100.0%
RVL-CDIP	missing	0.0 MB	0	n/a / n/a / n/a	n/a	n/a
READoc	ok	39.9 MB	959	100.0% / n/a / n/a	96.86	100.0%
OmniDocBench	ok	40.3 MB	1	100.0% / 100.0% / 100.0%	1030.96	88.5%
olmOCR-Bench	ok	340.5 MB	1403	100.0% / 100.0% / 100.0%	21.15	22.7%
ckorzen benchmark	ok	67.1 MB	192	100.0% / 15.4% / 100.0%	100.37	88.4%
S2ORC	missing	0.0 MB	0	n/a / n/a / n/a	n/a	n/a
PMC OA	missing	0.0 MB	0	n/a / n/a / n/a	n/a	n/a
arXiv source/PDF	missing	0.0 MB	0	n/a / n/a / n/a	n/a	n/a

Extraction-quality improvements

A controlled A/B of the current parser against the previous release baseline — run on the full olmOCR-Bench corpus (1403 real PDFs, 7019 unit checks, identical harness and release build) — isolates the gains from the recent text-spacing and table-structure work:

Signal	Before	After
olmOCR table-structure checks passed	59.7%	65.5% (+9.7% relative)
olmOCR reading-order checks passed	30.7%	32.0%
Overall olmOCR checks passed	1562 / 7019	1595 / 7019
Throughput (born-digital)	~90 pages/sec	~90 pages/sec

Born-digital word segmentation is fixed end to end (UNITEDSTATES → UNITED STATES, Netincome → Net income, fi scal → fiscal), and multi-section financial statements now extract as a single aligned table — in Markdown, LaTeX, and JSON — instead of a label column followed by a detached block of numbers. See the benchmarks page for the full breakdown.

License

Dongler is MIT licensed. Copyright (c) 2026 Daniel Fat. See LICENSE and NOTICE for the full notice text.