Automatic Watermeter Reading in Presence of Highly Deformed Digits
- The task we face in this paper is to automate the reading of watermeters as can be found in large apartment houses. Typically water passes through such watermeters, so that one faces a wide range of challenges caused by water as the medium where the digits are positioned. One of the main obstacles is given by the frequently produced bubbles inside the watermeter that deform the digits. To overcome this problem, we propose the construction of a novel data set that resembles the watermeter digits with a focus on their deformations by bubbles. We report on promising experimental recognition results, based on a deep and recurrent network architecture performed on our data set.
Author: | Ashkan Mansouri Yarahmadi, Michael BreußGND |
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DOI: | https://doi.org/10.1007/978-3-030-89131-2_14 |
ISBN: | 978-3-030-89130-5 |
ISSN: | 978-3-030-89131-2 |
Title of the source (English): | Computer Analysis of Images and Patterns |
Publisher: | Springer |
Place of publication: | Cham |
Document Type: | Conference publication peer-reviewed |
Language: | English |
Year of publication: | 2021 |
Tag: | Underwater digit recognition / Sequence models / Connectionist Temporal Classification |
First Page: | 153 |
Last Page: | 163 |
Series ; volume number: | Lecture Notes in Computer Science book series ; volume 13053 |
Faculty/Chair: | Fakultät 1 MINT - Mathematik, Informatik, Physik, Elektro- und Informationstechnik / FG Angewandte Mathematik |