# Towards Image-based Automatic Meter Reading in Unconstrained Scenarios: A Robust and Efficient Approach

arXiv: https://arxiv.org/abs/2009.10181 · HTML: https://mlsanity.com/paper/2009.10181/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Sep 2020.

## Built on by (most cited first, 12 of 12)

- [On the Cross-dataset Generalization in License Plate Recognition](https://mlsanity.com/paper/2201.00267/) (2022)
- [A First Look at Dataset Bias in License Plate Recognition](https://mlsanity.com/paper/2208.10657/) (2022)
- [It's About Time: Analog Clock Reading in the Wild](https://mlsanity.com/paper/2111.09162/) (2021)
- [Combining Attention Module and Pixel Shuffle for License Plate Super-Resolution](https://mlsanity.com/paper/2210.16836/) (2022)
- [Leveraging Model Fusion for Improved License Plate Recognition](https://mlsanity.com/paper/2309.04331/) (2023)
- [Improving Small Drone Detection Through Multi-Scale Processing and Data Augmentation](https://mlsanity.com/paper/2504.19347/) (2025)
- [Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation](https://mlsanity.com/paper/2601.07671/) (2026)
- [Toward Unified Fine-Grained Vehicle Classification and Automatic License Plate Recognition](https://mlsanity.com/paper/2604.05271/) (2026)
- [Evaluation of Different Annotation Strategies for Deployment of Parking Spaces Classification Systems](https://mlsanity.com/paper/2207.11372/) (2022)
- [DACov: A Deeper Analysis of Data Augmentation on the Computed Tomography Segmentation Problem](https://mlsanity.com/paper/2303.05912/) (2023)
- [Image-based Automatic Dial Meter Reading in Unconstrained Scenarios](https://mlsanity.com/paper/2201.02850/) (2022)
- [Light In The Black: An Evaluation of Data Augmentation Techniques for COVID-19 CT's Semantic Segmentation](https://mlsanity.com/paper/2205.09722/) (2022)

## Abstract (arXiv)

Existing approaches for image-based Automatic Meter Reading (AMR) have been evaluated on images captured in well-controlled scenarios. However, real-world meter reading presents unconstrained scenarios that are way more challenging due to dirt, various lighting conditions, scale variations, in-plane and out-of-plane rotations, among other factors. In this work, we present an end-to-end approach for AMR focusing on unconstrained scenarios. Our main contribution is the insertion of a new stage in the AMR pipeline, called corner detection and counter classification, which enables the counter region to be rectified -- as well as the rejection of illegible/faulty meters -- prior to the recognition stage. We also introduce a publicly available dataset, called Copel-AMR, that contains 12,500 meter images acquired in the field by the service company's employees themselves, including 2,500 images of faulty meters or cases where the reading is illegible due to occlusions. Experimental evaluation demonstrates that the proposed system, which has three networks operating in a cascaded mode, outperforms all baselines in terms of recognition rate while still being quite efficient. Moreover, as very few reading errors are tolerated in real-world applications, we show that our AMR system achieves impressive recognition rates (i.e., > 99%) when rejecting readings made with lower confidence values.