A Novel Camera Based Approach for Automatic Expiry Date Detection and Recognition on Food Packages

Gong, Liyun, Yu, Miao, Duan, Wenting , Ye, Xujiong, Gudmundsson, Kjartan and Swainson, Mark (2018) A Novel Camera Based Approach for Automatic Expiry Date Detection and Recognition on Food Packages. In: Iliadis L., Maglogiannis I., Plagianakos V. (eds) Artificial Intelligence Applications and Innovations. AIAI 2018. IFIP Advances in Information and Communication Technology. Springer, Cham. ISBN 978-3-319-92006-1

Full content URL: https://doi.org/10.1007/978-3-319-92007-8_12

A novel camera based approach for automatic expiry date detection and recognition on food packages
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There is abundant of information on food packages, which include the food name, the expiry date and the ingredients. These information, especially the expiry date needs to be coded correctly before the products can be released into the market/supply chains. Failure of printing the correct expiry date can lead to both the health issues to the public and financial issues for recalling product back and even reimbursement. In this paper, we develop an automatic system that can achieve the expiry date region detection and recognition in an efficient and effective way. A deep neural network (DNN) based approach is firstly applied to find the expiry date region on the food package. The date characters are then extracted and recognized through the image processing and machine learning methods from the expiry date region. The system is the first camera based automatic system for recognizing expiry date on food packages. And the results tested on different types of food packages show that the system can achieve good performance on both detection and recognition of the expiry date.

Keywords:deep learning, fully convolutional network, expiry date recognition
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > School of Computer Science
ID Code:34274
Deposited On:27 Nov 2018 08:43

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