# Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines

arXiv: https://arxiv.org/abs/2005.03106 · HTML: https://mlsanity.com/paper/2005.03106/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 6 May 2020.

## Built on by (most cited first, 9 of 9)

- [It's About Time: Analog Clock Reading in the Wild](https://mlsanity.com/paper/2111.09162/) (2021)
- [Open-set Face Recognition for Small Galleries Using Siamese Networks](https://mlsanity.com/paper/2105.06967/) (2021)
- [Image-based Automatic Dial Meter Reading in Unconstrained Scenarios](https://mlsanity.com/paper/2201.02850/) (2022)
- [DialBench: Towards Accurate Reading Recognition of Pointer Meter using Large Foundation Models](https://mlsanity.com/paper/2511.21982/) (2025)
- [NRC-GAMMA: Introducing a Novel Large Gas Meter Image Dataset](https://mlsanity.com/paper/2111.06827/) (2021)
- [Flying By ML -- CNN Inversion of Affine Transforms](https://mlsanity.com/paper/2312.17258/) (2023)
- [State Beyond Appearance: Diagnosing and Improving State Consistency in Dial-Based Measurement Reading](https://mlsanity.com/paper/2604.26614/) (2026)
- [InSituMeasure: Probing Situated Measurement Grounding in Industrial Scenes with Multimodal Large Language Models](https://mlsanity.com/paper/2609.04014/) (2026)
- [Type-Balanced Federated Learning for Visual Analog Meter Reading](https://mlsanity.com/paper/2609.31998/) (2026)

## Abstract (arXiv)

Smart meters enable remote and automatic electricity, water and gas consumption reading and are being widely deployed in developed countries. Nonetheless, there is still a huge number of non-smart meters in operation. Image-based Automatic Meter Reading (AMR) focuses on dealing with this type of meter readings. We estimate that the Energy Company of Paran'a (Copel), in Brazil, performs more than 850,000 readings of dial meters per month. Those meters are the focus of this work. Our main contributions are: (i) a public real-world dial meter dataset (shared upon request) called UFPR-ADMR; (ii) a deep learning-based recognition baseline on the proposed dataset; and (iii) a detailed error analysis of the main issues present in AMR for dial meters. To the best of our knowledge, this is the first work to introduce deep learning approaches to multi-dial meter reading, and perform experiments on unconstrained images. We achieved a 100.0% F1-score on the dial detection stage with both Faster R-CNN and YOLO, while the recognition rates reached 93.6% for dials and 75.25% for meters using Faster R-CNN (ResNext-101).