Reading measurement instruments is effortless for humans and requires relatively little domain expertise, yet it remains surprisingly challenging for current vision-language models (VLMs) as we find in preliminary evaluation. In this work, we introduce MeasureBench, a benchmark on visual measurement reading covering both real-world and synthesized images of various types of measurements, along with an extensible pipeline for data synthesis. Our pipeline procedurally generates a specified type of gauge with controllable visual appearance, enabling scalable variation in key details such as pointers, scales, fonts, lighting, and clutter. Evaluation on popular proprietary and open-weight VLMs shows that even the strongest frontier VLMs struggle with measurement reading in general. We have also conducted preliminary experiments with reinforcement finetuning (RFT) over synthetic data, and find a significant improvement on both in-domain synthetic subset and real-world images. Our analysis highlights a fundamental limitation of current VLMs in fine-grained spatial grounding. We hope this resource and our code releases can help future advances on visually grounded numeracy and precise spatial perception of VLMs, bridging the gap between recognizing numbers and measuring the world.
@article{arxiv.2510.26865,
title = {Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench},
author = {Fenfen Lin and Yesheng Liu and Haiyu Xu and Chen Yue and Zheqi He and Mingxuan Zhao and Miguel Hu Chen and Jiakang Liu and JG Yao and Xi Yang},
journal= {arXiv preprint arXiv:2510.26865},
year = {2026}
}