# New VLM `dots.ocr` Takes on Complex Documents

> The new 3B-parameter model from rednote-hilab uses a vision-language approach to parse tables, layouts, and even mathematical formulas.

Published by The Open Weights on Jul 30, 2025. Canonical: https://theopenweights.com/news/dots-ocr-ix9f

## Key facts

- Company: rednote-hilab
- Model: dots.ocr
- Version: dots.ocr
- Category: Vision-Language
- Modalities: Vision-Language
- License: Other (Open weights, commercial use allowed)
- Parameters: 3B
- Context window: 131K tokens
- Architecture: DotsOCRForCausalLM
- Precision: BF16
- Weights size: 6.1 GB
- Significance: notable
- Published: 2025-07-30
- Last verified: 2026-06-17
- Hugging Face: https://huggingface.co/rednote-hilab/dots.ocr
- Canonical URL: https://theopenweights.com/news/dots-ocr-ix9f

Researchers at rednote-hilab have released `dots.ocr`, a new open-source model designed for sophisticated document understanding. At 3 billion parameters, this vision-language model (VLM) moves beyond simple text extraction to interpret the complex structure of a page.

Built upon Microsoft's powerful Florence-2 vision foundation model, `dots.ocr` applies a multi-modal approach to Optical Character Recognition (OCR). Instead of merely identifying characters in sequence, it comprehends the spatial relationships between elements, allowing it to make sense of a document's overall layout.

## Advanced Document Parsing

The model's capabilities make it particularly well-suited for digitizing challenging content. It excels at:

*   **Layout Analysis:** Identifying columns, headers, and figures.
*   **Table Extraction:** Accurately parsing rows and columns from structured tables.
*   **Formula Recognition:** Transcribing complex mathematical and scientific notation, a common failure point for traditional OCR systems.

The release of `dots.ocr` provides a strong, openly-licensed alternative for developers building document intelligence applications. By handling nuanced formats that often require manual intervention, it opens new possibilities for automating data extraction from scientific papers, financial reports, and technical manuals. The model and usage examples are available on its [Hugging Face repository](https://huggingface.co/rednote-hilab/dots.ocr).

## Get the model

- [Hugging Face](https://huggingface.co/rednote-hilab/dots.ocr)

## Sources

- [rednote-hilab/dots.ocr](https://huggingface.co/rednote-hilab/dots.ocr) — Hugging Face, Jul 30, 2025

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Source: The Open Weights (https://theopenweights.com/). Aggregated and written by Claude, curated by humans. Cite as: "New VLM `dots.ocr` Takes on Complex Documents", The Open Weights, Jul 30, 2025, https://theopenweights.com/news/dots-ocr-ix9f