Rewrite card: PDF to Markdown positioning, quick start, licence in plain words

README.md

@@ -18,17 +18,16 @@ tags:
   - onnx
   - rag
   - pdf-to-markdown
+  - document-parsing
 ---
 
-# PDFluent formula recognition (ONNX): math formula image to LaTeX
+# PDFluent Formula Recognition (ONNX): Math Formula to LaTeX for PDF to Markdown
 
-This model converts cropped images of display math formulas from PDFs to LaTeX, for developers building document-processing and PDF-to-Markdown pipelines.
+Convert cropped images of PDF display math formulas into LaTeX with this ONNX model. For developers building PDF-to-Markdown and RAG pipelines, it plugs into the PDFluent SDK, which scores the highest overall on opendataloader-bench of the engines on its public leaderboard*. Formulas output as $$...$$ blocks in your Markdown.
 
-Model name: `formula-own-v1`. Encoder, decoder and vocabulary are three files: `encoder.onnx`, `decoder.onnx`, `vocab.json`.
+\* Measured with the benchmark's own harness and evaluator on its 200 documents, PDFluent SDK 1.3.0, `quality="auto"` with `keep_furniture=True`, on Linux (CPU only), 29 October 2026: 0.9168 overall against 0.907 for opendataloader [hybrid], 0.885 for nutrient and 0.882 for docling. Competitor figures are taken from the benchmark's public README (read 2 November 2026), not re-run by us (our own re-run of opendataloader [hybrid] on 28 October 2026 gave 0.9068). PDFluent is not on that leaderboard; the results are submitted in a pull request: https://github.com/opendataloader-project/opendataloader-bench/pull/37. Development was optimised for this benchmark among its targets. The scores are measured on the benchmark's own 200 documents. The 19-measure result on our own held-out half and the separate checks are on pdfluent.com/benchmarks. The scores come from a release candidate wheel of 1.3.0 that reported version 1.2.0. Speed is not published. The run keeps page headers and footers, because the benchmark's ground truth counts them as content. The difference is one run without a confidence interval.
 
-## Quick start
-
-With the PDFluent SDK (Python, SDK 1.3.0 or newer):
+## Use it in two lines
 
 ```bash
 pip install pdfluent
@@ -36,13 +35,13 @@ pdfluent models install formula
 ```
 
 ```python
-import pdfluent
-
-doc = pdfluent.open("paper.pdf")
-markdown = doc.extract_markdown(formula_model=True)  # display formulas appear as $$...$$
-print(markdown)
+import pdfluent; print(pdfluent.open("paper.pdf").extract_markdown(formula_model=True))  # display formulas appear as $$...$$
 ```
 
+The formula model is off by default. The command above installs it and the flag switches it on.
+
+## Inspect the ONNX files without the SDK
+
 With plain onnxruntime, to load the three files and inspect the interface:
 
 ```python
@@ -60,6 +59,14 @@ for name, sess in (("encoder", encoder), ("decoder", decoder)):
 
 Image preprocessing and the decoding loop are implemented in the SDK. If you drive the model yourself, decode greedily from `<bos>` (id 1) until `<eos>` (id 2) and map ids back to LaTeX tokens with `vocab.json`.
 
+## What you get
+
+- ONNX files (encoder.onnx, decoder.onnx, vocab.json) for local inference
+- LaTeX output in $$...$$ for Markdown compatibility
+- Direct integration into PDFluent SDK parsing workflows
+- Training provenance file listing all arXiv source papers
+- Licence choice: AGPL-3.0-only or PDFluent Commercial Licence
+
 ## Inputs and outputs
 
 The model takes a greyscale image of a formula and its width as input. It outputs token ids mapped to LaTeX via the vocabulary. In the PDFluent SDK 1.3.0, enable it with `pdfluent models install formula` and `formula_model=True` in `extract_markdown`. The SDK wraps the LaTeX in `$$ ... $$` in the Markdown output.
@@ -88,11 +95,13 @@ Provenance: [PROVENANCE.md](PROVENANCE.md). Digests of the three weight files ar
 - Display formulas only, no inline formulas.
 - Input must be a tight crop of one formula; layout detection is handled by the SDK.
 
-## Licence
+## Licence in plain words
+
+Model weights are AGPL-3.0-only or available under the PDFluent Commercial Licence. AGPL works for open-source projects. Closed-source products or those avoiding AGPL obligations need the commercial licence. Details at https://pdfluent.com/license.
 
-Weights are offered under AGPL-3.0-only or the PDFluent Commercial Licence. Commercial use without AGPL obligations requires the commercial licence: https://pdfluent.com/license. Copyright 2026 Innovation Trigger B.V. Training papers retain their original CC BY and CC0 licences.
+## Try PDFluent
 
-Licence texts: [LICENSE](LICENSE) (AGPL-3.0) and [LICENSE-COMMERCIAL](LICENSE-COMMERCIAL).
+Run `pip install pdfluent`, install the formula model and watch display formulas in your own papers come out as LaTeX. See the SDK at github.com/pdfluent/pdfluent-sdk and docs at pdfluent.com/docs.
 
 ## Citation
 
@@ -111,9 +120,11 @@ Licence texts: [LICENSE](LICENSE) (AGPL-3.0) and [LICENSE-COMMERCIAL](LICENSE-CO
 
 - Website: https://pdfluent.com
 - Documentation: https://pdfluent.com/docs
-- Licence: https://pdfluent.com/license
+- SDK on PyPI: https://pypi.org/project/pdfluent/
 - SDK source and releases: https://github.com/pdfluent/pdfluent-sdk
+- Licence: https://pdfluent.com/license
 - Organisation: https://huggingface.co/PDFluent
 - Formula recognition model: https://huggingface.co/PDFluent/formula-recognition-onnx
 - Heading level classifier: https://huggingface.co/PDFluent/pdf-heading-level-classifier
-- Layout ground-truth sample: https://huggingface.co/datasets/PDFluent/pdf-document-layout-ground-truth
+- Layout ground-truth dataset: https://huggingface.co/datasets/PDFluent/pdf-document-layout-ground-truth
+- Collection: https://huggingface.co/collections/PDFluent/pdfluent-document-ai-6ae99736adab8c6d90781ab5