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An Attention based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition

Alkhaled, Lama; Roy, Ayush; Palaiahnakote, Shivakumara

An Attention based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition Thumbnail


Authors

Lama Alkhaled

Ayush Roy



Abstract

Digital water meter digit recognition from images of water meter readings is a challenging research problem. One key reason is that this might be a lack of publicly available datasets to develop such methods. Another reason is the digits suffer from poor quality. In this work, we develop a dataset, called MR-AMR-v1, which comprises 10 different digits (0 to 9) that are commonly found in electrical and electronic water meter readings. Additionally, we generate a synthetic benchmarking dataset to make the proposed model robust. We propose a weighted probability averaging ensemble-based water meter digit recognition method applied to snapshots of the Fourier transformed convolution block attention module (FCBAM) aided combined ResNet50-InceptionV3 architecture. This benchmarking method achieves an accuracy of 88% on test set images (benchmarking data). Our model also achieves a high accuracy of 97.73% on the MNIST dataset. We benchmark the result on this dataset using the proposed method after performing an exhaustive set of experiments.

Citation

Alkhaled, L., Roy, A., & Palaiahnakote, S. (2023). An Attention based Fusion of ResNet50 and InceptionV3 Model for Water Meter Digit Recognition. #Journal not on list, https://doi.org/10.47852/bonviewAIA32021197

Journal Article Type Article
Acceptance Date Oct 10, 2023
Publication Date Oct 24, 2023
Deposit Date Nov 15, 2024
Publicly Available Date Nov 18, 2024
Journal Artificial Intelligence and Applications
Peer Reviewed Peer Reviewed
DOI https://doi.org/10.47852/bonviewAIA32021197

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