Surya Layout2
A lightweight document layout detection model used by Surya. It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU.
Model source
- Kind
- Other
- File size
- 6.74–129 MB
- Version
- vRevision 0aee81d
- Updated
- Oct 21, 2026
- Downloads
- 15K
- Source
- Hugging Face
Source description
Surya Layout (fast)
A lightweight document layout detection model used by Surya. It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU.
This is the "fast" layout detector — a compact object detector that serves as a drop-in alternative to Surya's VLM-based layout model.
➡️ Documentation, installation, and everything else lives in the Surya repository.
Usage
Install Surya:
pip install surya-ocr
Point the fast layout predictor at this checkpoint:
from PIL import Image
from surya.fast_layout import FastLayoutPredictor
predictor = FastLayoutPredictor(checkpoint="hf://datalab-to/surya_layout2")
layout = predictor([Image.open("page.png")])
for box in layout[0].bboxes:
print(box.label, box.bbox, box.position) # region label, [x0,y0,x1,y1], reading-order index
Or make it the default so the CLI and library use it without an explicit path:
export FAST_LAYOUT_MODEL_CHECKPOINT="hf://datalab-to/surya_layout2"
License
Released under the AI Pubs OpenRAIL-M license (see LICENSE) — the same
license as the surya-ocr-2
model weights.
Sources
1 source
Sources
1 sourceVerified Oct 21
Model artifacts
2 artifacts
Model artifacts
2 artifactsSource excerpts
3 excerpts
Source excerpts
3 excerpts--- license: openrail license_link: LICENSE tags: - object-detection - document-layout-analysis - ocr library_name: surya --- # Surya Layout (fast) A lightweight document **layout detection** model used by [Surya](https://github.com/datalab-to/surya). It detects layout regions (text, tables, figures, headers, captions, equations, etc.) on a page image and runs on CPU or GPU. This is the "fast" layout detector — a compact object detector that serves as a drop-in alternative to Surya's VLM-based layout model. ➡️ **Documentation, installation, and everything else lives in the [Surya repository](https://github.com/datalab-to/surya).** ## Usage Install Surya: ```shell pip install surya-ocr ``` Point the fast layout predictor at this checkpoint: ```python from PIL import Image from surya.fast_layout import FastLayoutPredictor predictor = FastLayoutPredictor(checkpoint="hf://datalab-to/surya_layout2") layout = predictor([Image.open("page.png")]) for box in layout[0].bboxes: print(box.label, box.bbox, box.position) # region label, [x0,y0,x1,y1], reading-order index ``` Or make it the default so the CLI and library use it without an explicit path: ```shell export FAST_LAYOUT_MODEL_CHECKPOINT="hf://datalab-to/surya_layout2" ``` ## License Released under the AI Pubs OpenRAIL-M license (see `LICENSE`) — the same license as the [surya-ocr-2](https://huggingface.co/datalab-to/surya-ocr-2) model weights.
Source context: 14963 downloads · 8 likes · Pipeline object-detection · Library surya · Repo datalab-to/surya_layout2
Source context: 9529 downloads · 4 likes · Pipeline object-detection · Library surya · Repo datalab-to/surya_layout2
