# Convolutional Neural Networks for Automatic Meter Reading

## TL;DR

This paper introduces a CNN-based approach for automatic meter reading, utilizing a new large public dataset and data augmentation, achieving high accuracy with minimal training data.

## Contribution

The paper presents a new large public dataset for AMR, a two-stage CNN approach with data augmentation, and demonstrates state-of-the-art results with limited training data.

## Key findings

- New UFPR-AMR dataset with 2,000 images introduced
- High accuracy achieved with less than 200 training images
- Effective data augmentation improves CNN training for AMR

## Abstract

In this paper, we tackle Automatic Meter Reading (AMR) by leveraging the high capability of Convolutional Neural Networks (CNNs). We design a two-stage approach that employs the Fast-YOLO object detector for counter detection and evaluates three different CNN-based approaches for counter recognition. In the AMR literature, most datasets are not available to the research community since the images belong to a service company. In this sense, we introduce a new public dataset, called UFPR-AMR dataset, with 2,000 fully and manually annotated images. This dataset is, to the best of our knowledge, three times larger than the largest public dataset found in the literature and contains a well-defined evaluation protocol to assist the development and evaluation of AMR methods. Furthermore, we propose the use of a data augmentation technique to generate a balanced training set with many more examples to train the CNN models for counter recognition. In the proposed dataset, impressive results were obtained and a detailed speed/accuracy trade-off evaluation of each model was performed. In a public dataset, state-of-the-art results were achieved using less than 200 images for training.