Deskew before you classify: training-free page orientation for any model
Interactive page · Quickstart · Results
Document pipelines pick a page's quarter turn first and remove its tilt second. Orientation models are trained on level pages, so a tilted page fools them before the deskewer ever runs. LevelHead swaps the order: level the page, then let any frozen model vote over the four quarter turns of the level page.
- Training-free: wraps any CNN, ViT, OCR page classifier or VLM; nothing is fine-tuned.
- Principled vote: answers are rolled back and multiplied, so a model's fixed preference for one answer cancels out.
- Reads photographs: a Hann taper lets the spectral deskewer level phone photos, where the released jdeskew returns 0°.
| Classify-first | LevelHead | |
|---|---|---|
| 4 orientation classifiers, pages at any angle | 71.9 | 85.2 |
| 4 Qwen3-VL models, 7 benchmarks | 41.9 | 84.6 |
| GPT-4o-mini, 7 benchmarks | 34.3 | 69.9 |
| Gemma-4-31B, 7 benchmarks | 62.0 | 94.5 |
| DeepSeek-V4.1-Flash, 7 benchmarks | 68.8 | 96.5 |
| 4 classifiers, phone photos at any angle | 69.3 | 89.8 |
| 4 classifiers, DISE scans at any angle | 65.0 | 91.3 |
Accuracy (%) within 15°. Per-backbone numbers: results/main_table.csv; figures, galleries and charts: the interactive page.
Any angle splits as θ = 90°·k + s. Rows of text reveal the tilt s and look the same after a quarter turn; the shapes of letters reveal k. So the deskewer removes s first and the classifier only ever sees the kind of page it was trained on.
pip install "levelhead[hf] @ git+https://github.com/beingamanforever/LevelHead"from PIL import Image
from levelhead import LevelHead
from levelhead.backbones import HFOrientationClassifier
lh = LevelHead(HFOrientationClassifier()) # any images -> (N, 4) probabilities works
pred = lh.predict(Image.open("scan.jpg"))
print(pred.angle, pred.tilt, pred.posterior) # clockwise angle, tilt, P(0/90/180/270)
lh.correct(Image.open("scan.jpg")).save("upright.jpg")Command line: python examples/demo.py scan.jpg --out upright.jpg.
| Path | Contents |
|---|---|
levelhead/ |
the method: deskewer, rolled geometric vote, backbone adapters |
examples/, tests/ |
command-line demo and end-to-end self-checks |
experiments/ |
benchmark builders (ODB-4/any, Real5-Rot, DISE-any), labels, VLM runners |
results/ |
per-backbone accuracies on the public benchmarks |
docs/ |
the interactive page |
Builds on jdeskew and evaluates on OmniDocBench, ORB, Real5-OmniDocBench and DISE 2021. The paper is under review; a citation will follow. Licensed under Apache 2.0.