Reading a Ruler in the Wild

TL;DR

RulerNet is a deep learning framework that accurately estimates real-world scale from images of rulers in diverse conditions, using a unified keypoint detection approach and synthetic data augmentation for robust, real-time measurements.

Contribution

The paper introduces RulerNet, a novel, perspective-invariant keypoint detection method for ruler reading, combined with synthetic data generation and a lightweight regression network for real-time scale estimation.

Findings

Achieves accurate scale estimation in challenging real-world scenarios.

Generalizes well across different ruler types and imaging conditions.

Enables real-time measurements on mobile and edge devices.

Abstract

Accurately converting pixel measurements into absolute real-world dimensions remains a fundamental challenge in computer vision and limits progress in key applications such as biomedicine, forensics, nutritional analysis, and e-commerce. We introduce RulerNet, a deep learning framework that robustly infers scale "in the wild" by reformulating ruler reading as a unified keypoint-detection problem and by representing the ruler with geometric-progression parameters that are invariant to perspective transformations. Unlike traditional methods that rely on handcrafted thresholds or rigid, ruler-specific pipelines, RulerNet directly localizes centimeter marks using a distortion-invariant annotation and training strategy, enabling strong generalization across diverse ruler types and imaging conditions while mitigating data scarcity. We also present a scalable synthetic-data pipeline that combines graphics-based ruler generation with ControlNet to add photorealistic context, greatly increasing training diversity and improving performance. To further enhance robustness and efficiency, we propose DeepGP, a lightweight feed-forward network that regresses geometric-progression parameters from noisy marks and eliminates iterative optimization, enabling real-time scale estimation on mobile or edge devices. Experiments show that RulerNet delivers accurate, consistent, and efficient scale estimates under challenging real-world conditions. These results underscore its utility as a generalizable measurement tool and its potential for integration with other vision components for automated, scale-aware analysis in high-impact domains. A live demo is available at https://huggingface.co/spaces/ymp5078/RulerNet-Demo.