{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/convolutional-neural-networks-for-automatic","title":"Convolutional Neural Networks for Automatic Meter Reading","arxiv_id":"1902.09600","date":"2019-02-25","proceeding":null,"authors":["Rayson Laroca","Victor Barroso","Matheus A. Diniz","Gabriel R. Gonçalves","William Robson Schwartz","David Menotti"],"abstract":"In this paper, we tackle Automatic Meter Reading (AMR) by leveraging the high\ncapability of Convolutional Neural Networks (CNNs). We design a two-stage\napproach that employs the Fast-YOLO object detector for counter detection and\nevaluates three different CNN-based approaches for counter recognition. In the\nAMR literature, most datasets are not available to the research community since\nthe images belong to a service company. In this sense, we introduce a new\npublic dataset, called UFPR-AMR dataset, with 2,000 fully and manually\nannotated images. This dataset is, to the best of our knowledge, three times\nlarger than the largest public dataset found in the literature and contains a\nwell-defined evaluation protocol to assist the development and evaluation of\nAMR methods. Furthermore, we propose the use of a data augmentation technique\nto generate a balanced training set with many more examples to train the CNN\nmodels for counter recognition. In the proposed dataset, impressive results\nwere obtained and a detailed speed/accuracy trade-off evaluation of each model\nwas performed. In a public dataset, state-of-the-art results were achieved\nusing less than 200 images for training.","url_abs":"http://arxiv.org/abs/1902.09600v1","url_pdf":"http://arxiv.org/pdf/1902.09600v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":null,"task_name":"Counter Recognition"},{"task_slug":"data-augmentation","task_name":"Data Augmentation"},{"task_slug":"image-based-automatic-meter-reading","task_name":"Image-based Automatic Meter Reading"},{"task_slug":"meter-reading","task_name":"Meter Reading"},{"task_slug":"optical-character-recognition","task_name":"Optical Character Recognition (OCR)"}],"methods":[{"method_slug":"cr-net","method_name":"CR-NET"},{"method_slug":"fast-yolov2","method_name":"Fast-YOLOv2"}],"datasets_introduced":[{"slug":"ufpr-amr-dataset","name":"UFPR-AMR","full_name":""}],"methods_introduced":[],"results":[{"leaderboard":"/sota/meter-reading-on-ufpr-amr-dataset","task":"Meter Reading","dataset":"UFPR-AMR","model":"Fast-YOLOv2 + CR-NET","rank_in_archive_order":1,"of":3,"metrics":{"Rank-1 Recognition Rate":"94.13"},"uses_additional_data":false},{"leaderboard":"/sota/meter-reading-on-ufpr-amr-dataset","task":"Meter Reading","dataset":"UFPR-AMR","model":"Fast-YOLOv2 + CRNN","rank_in_archive_order":2,"of":3,"metrics":{"Rank-1 Recognition Rate":"92.3"},"uses_additional_data":false},{"leaderboard":"/sota/meter-reading-on-ufpr-amr-dataset","task":"Meter Reading","dataset":"UFPR-AMR","model":"Fast-YOLOv2 + Multi-task CNN","rank_in_archive_order":3,"of":3,"metrics":{"Rank-1 Recognition Rate":"87.69"},"uses_additional_data":false}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}