Effective Digital Technology Enabling Automatic Recognition of Special-Type Marking of Expiry Dates

Abdulkabir Abdulraheem and Im Y. Jung ()
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Abdulkabir Abdulraheem: School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea
Im Y. Jung: School of Electronic and Electrical Engineering, Kyungpook National University, Daegu 41566, Republic of Korea

Sustainability, 2023, vol. 15, issue 17, 1-22

Abstract: In this study, we present a machine-learning-based approach that focuses on the automatic retrieval of engraved expiry dates. We leverage generative adversarial networks by augmenting the dataset to enhance the classifier performance and propose a suitable convolutional neural network (CNN) model for this dataset referred to herein as the CNN for engraved digit (CNN-ED) model. Our evaluation encompasses a diverse range of supervised classifiers, including classic and deep learning models. Our proposed CNN-ED model remarkably achieves an exceptional accuracy, reaching a 99.88% peak with perfect precision for all digits. Our new model outperforms other CNN-based models in accuracy and precision. This work offers valuable insights into engraved digit recognition and provides potential implications for designing more accurate and efficient recognition models in various applications.

Keywords: classifier algorithm; CNN; deep learning; engraved digit recognition; hybrid CNN (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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