# Learning to Read Analog Gauges from Synthetic Data

arXiv: https://arxiv.org/abs/2308.14583 · HTML: https://mlsanity.com/paper/2308.14583/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 36 references that are in the map.
- First posted on arXiv 28 Aug 2023.

## Builds on (most cited first, 7 of 7)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks](https://mlsanity.com/paper/1905.11946/) (2019)
- [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://mlsanity.com/paper/1801.04381/) (2018)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation](https://mlsanity.com/paper/1802.02611/) (2018)
- [The Kinetics Human Action Video Dataset](https://mlsanity.com/paper/1705.06950/) (2017)

## Built on by (most cited first, 4 of 4)

- [Do Vision-Language Models Measure Up? Benchmarking Visual Measurement Reading with MeasureBench](https://mlsanity.com/paper/2510.26865/) (2025)
- [Lost in Motion: Vision Language Models Fail the Dynamic Gauges Test](https://mlsanity.com/paper/2604.22829/) (2026)
- [Type-Balanced Federated Learning for Visual Analog Meter Reading](https://mlsanity.com/paper/2609.31998/) (2026)
- [RealityBench: Simulated Environments to Evaluate a Vision Language Model's Ability to Document Human Physical Work](https://mlsanity.com/paper/2610.14397/) (2026)

## Abstract (arXiv)

Manually reading and logging gauge data is time inefficient, and the effort increases according to the number of gauges available. We present a computer vision pipeline that automates the reading of analog gauges. We propose a two-stage CNN pipeline that identifies the key structural components of an analog gauge and outputs an angular reading. To facilitate the training of our approach, a synthetic dataset is generated thus obtaining a set of realistic analog gauges with their corresponding annotation. To validate our proposal, an additional real-world dataset was collected with 4.813 manually curated images. When compared against state-of-the-art methodologies, our method shows a significant improvement of 4.55 in the average error, which is a 52% relative improvement. The resources for this project will be made available at: https://github.com/fuankarion/automatic-gauge-reading.