Datasets:
File size: 4,652 Bytes
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---
pretty_name: Slide2SVG
license: cc-by-4.0
tags:
- raster-to-vector
- svg
- slides
- document-layout
- derendering
language:
- en
size_categories:
- 10K<n<100K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: id
dtype: string
- name: image
dtype: image
- name: svg
dtype: string
- name: assets_zip
dtype: binary
- name: assets
dtype: string
- name: num_assets
dtype: int32
splits:
- name: train
num_bytes: 8298450173.191999
num_examples: 39472
- name: test
num_bytes: 54768897
num_examples: 225
download_size: 9049767829
dataset_size: 8353219070.191999
task_categories:
- image-to-text
---
# Slide2SVG
Slide2SVG was released as part of the paper “Semantic Document Derendering: SVG Reconstruction via Vision-Language Modeling” (AAAI 2026).
Slide2SVG is a real-world dataset for **semantic document derendering**: transforming a rasterized presentation slide into a structured, editable SVG representation. Curated from publicly available academic conference presentations, it captures diverse design styles, font choices, image placements, and layout configurations found in real slides.
Each sample includes:
- the raster slide in PNG format
- an SVG representation that preserves editability (composed of only image and text SVG elements)
- individual PNG image assets referenced by the SVG
The dataset is split into roughly **40,000 training** samples and **225 test** samples.
---
## Dataset format
This dataset is formatted with Parquet shards. Each example contains:
- `id` (string): unique sample id
- `image` (Image): raster slide (PNG)
- `svg` (string): SVG markup as text
- `assets_zip` (binary): a ZIP archive containing the PNG image assets referenced by the SVG
- `assets` (list[string]): filenames contained in `assets_zip`
- `num_assets` (int): number of assets in the ZIP
---
## Quickstart
### Install
pip install datasets pillow
### Load the dataset
from datasets import load_dataset
ds = load_dataset("ahazimeh/slide2svg")
print(ds)
### Inspect an example
ex = ds["train"][0]
print(ex["id"])
print(type(ex["image"]))
print(ex["num_assets"])
print(ex["assets"])
print(ex["svg"])
### Extract asset PNGs from `assets_zip`
import io
import zipfile
ex = ds["train"][0]
zf = zipfile.ZipFile(io.BytesIO(ex["assets_zip"]))
names = zf.namelist()
print("assets:", names)
# Read one asset as bytes
asset0_bytes = zf.read(names[0])
# Convert bytes to a PIL image
from PIL import Image
asset0 = Image.open(io.BytesIO(asset0_bytes)).convert("RGBA")
### Save one complete example to disk (PNG + SVG + assets)
import os
import io
import zipfile
out_dir = "sample_export"
os.makedirs(out_dir, exist_ok=True)
ex = ds["train"][0]
# Save raster slide
ex["image"].save(os.path.join(out_dir, f"{ex['id']}.png"))
# Save SVG
with open(os.path.join(out_dir, f"{ex['id']}.svg"), "w", encoding="utf-8") as f:
f.write(ex["svg"])
# Save assets
zf = zipfile.ZipFile(io.BytesIO(ex["assets_zip"]))
os.makedirs(out_dir, exist_ok=True)
zf.extractall(out_dir)
---
## Data collection and processing
Slide2SVG was assembled using the following pipeline:
1. **PDF Collection**
Presentation slides in PDF format were collected from archives of several major machine learning conferences.
2. **SVG Conversion**
PDFs were converted to Figma designs and exported as raw SVG files. This conversion is used only for dataset creation. At inference time, we assume the slide is available only as a raster image.
3. **Asset Grouping**
Figma-exported SVGs often group text elements using non-semantic heuristics. To improve structure, text elements are reorganized using a zero-shot DocLayout-YOLO-based procedure so that text assets belonging to one entity are merged into a unified text object with updated spatial attributes.
4. **Outlier Filtering**
Slides with more than 8 image assets or 31 text assets (95th percentile thresholds) are filtered out as unusually complex.
5. **Rasterization**
The final SVGs are rendered into PNG format to obtain paired raster slides.
---
## Intended use
This dataset supports research and development in:
- raster-to-SVG conversion / derendering
- slide/document understanding
- layout analysis and generation
- editability-preserving reconstruction
---
## Maintainer
- Adam Hazimeh
- Contact: adam(dot)hazimeh(at)epfl(dot)ch |