Release DigitizerBench Full and Lite (083026)

README.md

@@ -1,3 +1,87 @@
 ---
-license: apache-2.0
+pretty_name: DigitizerBench
+tags:
+- benchmark
+- plot-digitization
+- scientific-figures
+- synthetic
+- computer-vision
+size_categories:
+- 1K<n<10K
 ---
+
+# DigitizerBench
+
+DigitizerBench measures how accurately a tool can recover numerical curves from raster line plots. Each PNG is paired with the original curve coordinates and the rendered tick positions, so you can evaluate curve recovery and axis calibration separately.
+
+## Quick start
+
+Choose **Lite** for a 60-image human-guided comparison or a quick pipeline check. Choose **Full** for automatic evaluation and analysis across rendering and signal conditions. Both downloads include their ground truth.
+
+| Download | Images | Use |
+| --- | ---: | --- |
+| [Lite](https://huggingface.co/datasets/LinePilot/DigitizerBench/resolve/main/lite.zip) | 60 | The fixed, outcome-independent set used in the paper's human-guided comparison |
+| [Full](https://huggingface.co/datasets/LinePilot/DigitizerBench/resolve/main/full.zip) | 2,980 | 2,500 core plots and 480 matched rerenderings for automatic evaluation |
+
+Download and unpack Lite, then load a plot and its reference data:
+
+```bash
+python -m pip install huggingface_hub pandas pyarrow pillow
+hf download LinePilot/DigitizerBench lite.zip --repo-type dataset --local-dir digitizerbench
+python -m zipfile -e digitizerbench/lite.zip digitizerbench
+```
+
+```python
+from pathlib import Path
+
+import pandas as pd
+from PIL import Image
+
+root = Path("digitizerbench/lite")
+manifest = pd.read_parquet(root / "manifest.parquet")
+case = manifest.iloc[0]
+image = Image.open(root / case["image_path"])
+image_filter = [("image_id", "==", case["image_id"])]
+curves = pd.read_parquet(root / "ground_truth.parquet", filters=image_filter)
+ticks = pd.read_parquet(root / "tick_ground_truth.parquet", filters=image_filter)
+
+image.show()
+for line_id, points in curves.groupby("line_id"):
+    print(line_id, points.sort_values("sample_index")[["x", "y"]].head())
+```
+
+For Full, replace `lite.zip` and `digitizerbench/lite` with `full.zip` and `digitizerbench/full`.
+
+## Files and ground truth
+
+| File | Contents |
+| --- | --- |
+| `images/` | Input PNGs organized by renderer and plot type |
+| `manifest.parquet` | One row per image. Join on `image_id` and resolve `image_path` relative to the extracted Full or Lite folder. Includes `base_id`, renderer, plot type, line count, factor settings and seeds. |
+| `ground_truth.parquet` | One row per curve sample. Group by `image_id` and `line_id`, then order by `sample_index`. Use `x` and `y` as the reference in data coordinates. The remaining fields describe the signal, baseline, noise and stacked offsets. |
+| `tick_ground_truth.parquet` | Visible ticks keyed by `image_id`, `axis` and `tick_index`. Includes the rendered label, numeric value and tick position in top-left-origin image pixels. |
+| `factor_levels.json` | Factor definitions and levels |
+| `design/` | Full only. Base configurations, orthogonal-panel assignments, bridge manifest and design summary. |
+
+## Benchmark design and evaluation
+
+The plots are synthetic. They span single, overlapping and stacked layouts rendered in Matplotlib, Plotly and Altair. Controlled factors include curve shape, baseline, noise, stroke width, color, DPI, blur, figure dimensions, tick styling and axis ranges. Full uses four strength-3 orthogonal-array panels. Each of the 60 Lite configurations has nine renderer × plot-type variants in Full, including its Lite image.
+
+Run your digitizer on the PNGs, then join the recovered data to ground truth by image and match the recovered curves to the reference lines. For the paper's protocol, report failure-penalized capped normalized RMSE (FPC-NRMSE) alongside output success, trusted usability and points per line. FPC-NRMSE assigns unit loss to missing, unusable or catastrophically inaccurate output. See the paper and appendix for the matching and scoring rules.
+
+Lite is nested within Full. For custom train/test partitions, keep all variants sharing a `base_id` together. Results characterize recovery on these controlled renders. Scanned pages, compression and real-world annotation styles require separate evaluation.
+
+## Project and citation
+
+[LinePilot website](https://rayana07.github.io/LinePilot/) · [GitHub and usage docs](https://github.com/RayanA07/LinePilot) · [Paper](https://arxiv.org/abs/2609.19377) · [Online appendix](https://github.com/RayanA07/LinePilot/blob/main/docs/assets/LinePilot_Online_Appendix.pdf) · [Issues](https://github.com/RayanA07/LinePilot/issues)
+
+```bibtex
+@article{ma2026linepilot,
+  title   = {LinePilot Digitizer: Line-Plot Recovery with Manual and Automatic Calibration},
+  author  = {Ma, Fengbo and Akhtar, Rayan and Joshi, Aakash H. and Li, Xiaoting and
+             Sun, Haijian and Xiang, Zhen and Chen, Xianyan and Zhao, Yiping},
+  journal = {arXiv preprint arXiv:2609.19377},
+  year    = {2026},
+  doi     = {10.48550/arXiv.2609.19377}
+}
+```

full.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:55f84ad3d88c61d946d93e850368919b9f0afd5016a723e4ce37562606c0fc88
+size 749958209

lite.zip

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:2127fde88c7a1f9475f782c8e610a25c32df05c82fcdc39c5f2d199222e698ab
+size 17094028