Seker, Ahmet Cagatay Ahn, Sang Chul 2024-01-19T11:02:55Z 2024-01-19T11:02:55Z 2022-08-11 2022-10 0957-4174 https://pubs.kist.re.kr/handle/201004/114526 It is important to understand the expiration date. However, it is challenging for machines to understand it. Most previous methods recognize expiration dates in limited conditions. To address this problem, a generalized framework for detecting and understanding expiration dates has been proposed. This framework handles challenging cases and distinguishes 13 different date formats. Unlike previous methods, a neural network -based date parser is adopted in the framework to understand the meaning of an expiration date by identifying the day, month, and year. The experimental results demonstrate the proposed framework achieves 97.74% recognition accuracy for expiration dates in various formats and challenging cases. Since there is no publicly available dataset of expiration dates, a novel dataset collection named ExpDate was created and opened. English Pergamon Press Ltd. A generalized framework for recognition of expiration dates on product packages using fully convolutional networks Article 10.1016/j.eswa.2022.117310 1 Expert Systems with Applications, v.203 Expert Systems with Applications 203 Y scie scopus 000830886800004 Computer Science, Artificial Intelligence Engineering, Electrical & Electronic Operations Research & Management Science Computer Science Engineering Operations Research & Management Science Article Expirationdate Recognition Dateparser Foodsafety Convolutionalnetwork Deeplearning