license: other
license_name: nutrient-commercial
pipeline_tag: image-text-to-text
language:
- multilingual
tags:
- page-stream-segmentation
- document-boundary-detection
- document-ai
- document-splitting
datasets:
- nutrientdocs/doc-split-benchmark
doc-split-v2 · commercial
One model that splits any document stream — short or long, English or not, forms or prose. The high-accuracy flagship for page-stream segmentation: hand it a merged/scanned PDF and it marks where each new document begins. Weights are commercial (not downloadable here); this page is a spec + scorecard.
It is the document specialist in a field of generalists — cloud VLMs and OpenPSS's own single-slice specialists each fall down somewhere; this one does short and long streams with one model. For the open-weight companion, see doc-split-v1.
- 🎯 Try it: doc-split-demo
- 🏆 Leaderboard: doc-split-leaderboard
- 📊 Benchmark: doc-split-benchmark
- 🟢 Open weights: doc-split-v1
Results — boundary F1 (κ)
This model vs the open doc-split-v1, the strongest cloud VLM, and prior work (bold = best releasable single model).
| Cut | doc-split-v2 | doc-split-v1 (open) | best cloud VLM | OpenPSS specialist |
|---|---|---|---|---|
| OpenPSS-short (sparse) | 0.652 (.60) | 0.585 | 0.598 | 0.76 (short-spec) |
| OpenPSS-long | 0.891 (.86) | 0.859 | 0.244 | 0.83 (long-spec) |
| our-200 (synthetic) | 0.944 (.79) | 0.936 | 0.942 | — |
| TABME++ test | 0.943 (.91) | 0.704 | — | — |
| Tobacco800 test | 0.969 (.93) | 0.820 | — | — |
| val (real-doc) | 0.917 (.86) | 0.918 | — | — |
One balanced model, not two specialists. OpenPSS needs a separate short- and long-specialist (each craters on the other slice); the flagship does both with one model, and its OpenPSS-long (0.891) tops even OpenPSS's own long-specialist (0.83) and every cloud VLM (best 0.244). It also dominates the modern TABME++/Tobacco800 benchmarks. See the leaderboard for the full field.
Intended use & limits
- Use it for: segmenting a stream of page images (a merged/scanned PDF) into its constituent documents — short or long, English or not, forms or prose. One model handles both sparse and dense boundary regimes; the embedded text layer sharpens boundaries when present, and scanned pages fall back to vision.
- Limits: optimized for document page streams; confidence is calibrated on our-domain data, so very out-of-distribution scans are approximate. Boundaries only (not document type).
License & data
The model weights are offered under a commercial Nutrient license — deployed on-prem, so your documents never leave your infrastructure. The training set is not redistributed; evaluation runs on the held-out doc-split-benchmark.
📩 Get access
doc-split-v2is commercial and its weights are not downloadable here. To run it on-prem — one model for any stream, calibrated, private — contact Nutrient: nutrient.io/contact-sales.
About the author
This project is maintained and funded by Nutrient - The deterministic document infrastructure enterprises run their highest-stakes workflows on: replayable output, clear exceptions, and full audit trails on the messy, regulated documents where AI alone breaks.