Update README.md

README.md

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 ---
+pretty_name: Slide2SVG
 license: cc-by-4.0
 task_categories:
-- image-text-to-text
+  - document-understanding
+tags:
+  - raster-to-vector
+  - svg
+  - slides
+  - document-layout
+  - derendering
 language:
-- en
----
+  - en
+size_categories:
+  - 10K<n<100K
+---
+
+# 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 **38,000 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)
+    # DatasetDict({
+    #   train: Dataset(...),
+    #   test: Dataset(...)
+    # })
+
+### Inspect an example
+
+    ex = ds["train"][0]
+    print(ex["id"])
+    print(type(ex["image"]))
+    print(ex["svg"][:200])
+    print(ex["num_assets"])
+    print(ex["assets"][:10])
+
+### 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 (optional)
+    from PIL import Image
+    asset0 = Image.open(io.BytesIO(asset0_bytes)).convert("RGBA")
+    asset0.size
+
+### 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"]))
+    assets_dir = os.path.join(out_dir, f"{ex['id']}_assets")
+    os.makedirs(assets_dir, exist_ok=True)
+    zf.extractall(assets_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
+
+---
+
+## Citation
+
+If you use Slide2SVG, please cite:
+
+@misc{hazimeh2025slider,
+      title={Semantic Document Derendering: SVG Reconstruction via Vision-Language Modeling}, 
+      author={Adam Hazimeh and Ke Wang and Mark Collier and Gilles Baechler and Efi Kokiopoulou and Pascal Frossard},
+      year={2025},
+      eprint={2511.13478},
+      url={https://arxiv.org/abs/2511.13478}, 
+}
+
+---
+
+## Maintainer
+
+- Dataset: `ahazimeh/slide2svg`
+- Contact: adam(dot)hazimeh(at)epfl(dot)ch