Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/243632 
Erscheinungsjahr: 
2019
Quellenangabe: 
[Journal:] Energy Reports [ISSN:] 2352-4847 [Volume:] 5 [Publisher:] Elsevier [Place:] Amsterdam [Year:] 2019 [Pages:] 842-852
Verlag: 
Elsevier, Amsterdam
Zusammenfassung: 
The article describes the collection of the dataset of raw images of digital energy meters display, text detection and recognition of seven segment numerals from collected samples that may be helpful in reducing the cost of advanced metering infrastructure (AMI). The presented dataset has tremendous potentials in fully automated optical character recognition (OCR) based electricity billing. The dataset has been named as 'YUVA EB Dataset' that has the collection of digital energy meter images. The images have been captured under day and night light conditions. The research work on recognizing the text from seven segment display in energy meters has been carried out using our dataset under the challenging text recognition conditions like tilted position, blurred, day and night light captured images. MSER and labeling method based OCR algorithm has been used for text detection and recognition.
Schlagwörter: 
Data collection
Displays
Image databases
Optical character recognition
Smart metering
Text recognition
Persistent Identifier der Erstveröffentlichung: 
Creative-Commons-Lizenz: 
cc-by Logo
Dokumentart: 
Article
Erscheint in der Sammlung:

Datei(en):
Datei
Größe





Publikationen in EconStor sind urheberrechtlich geschützt.