Fast and accurate recognition for codes on complex backgrounds for real-life industrial applications
Joint Authors
Sun, Wei
Zhang, Dan
Wang, Yaonan
Liang, Qiaokang
Ge, Qiao
Zou, Kunlin
Source
Journal of Engineering Research
Issue
Vol. 11, Issue 1 A (31 Mar. 2023), pp.340-354, 15 p.
Publisher
Kuwait University Academic Publication Council
Publication Date
2023-03-31
Country of Publication
Kuwait
No. of Pages
15
Main Subjects
Abstract EN
In the food and beverage industry, the existing recognition of code characters on the surface of complex packaging usually suffers from low accuracy and low speed.
This work presents an efficient and accurate inkjet code recognition system based on the combination of the deep learning and traditional image processing methods.
The proposed system mainly consists of three sequential modules, i.e., the character’s region extraction by modified YOLOv3-tiny network, the character processing by the traditional image processing methods such as binarization and the modified character projection segmentation, and the character recognition by a Convolutional recurrent neural network (CRNN) model based on a modified version of MobileNetV3.
In this system, only a small amount of tag data has been made and an effective character data generator is designed to randomly generate different experimental data for the CRNN model training.
To the best of our knowledge, this report for the first time describes that deep learning has been applied to the recognition of codes on complex background for the real-life industrial application.
Experimental results have been provided to verify the accuracy and effectiveness of the proposed model, demonstrating a recognition accuracy of 0.986 and a processing speed of 100 ms per bottle in the end-to-end character recognition system.
American Psychological Association (APA)
Liang, Qiaokang& Ge, Qiao& Sun, Wei& Zhang, Dan& Wang, Yaonan& Zou, Kunlin. 2023. Fast and accurate recognition for codes on complex backgrounds for real-life industrial applications. Journal of Engineering Research،Vol. 11, no. 1 A, pp.340-354.
https://search.emarefa.net/detail/BIM-1494694
Modern Language Association (MLA)
Liang, Qiaokang…[et al.]. Fast and accurate recognition for codes on complex backgrounds for real-life industrial applications. Journal of Engineering Research Vol. 11, no. 1 A (Mar. 2023), pp.340-354.
https://search.emarefa.net/detail/BIM-1494694
American Medical Association (AMA)
Liang, Qiaokang& Ge, Qiao& Sun, Wei& Zhang, Dan& Wang, Yaonan& Zou, Kunlin. Fast and accurate recognition for codes on complex backgrounds for real-life industrial applications. Journal of Engineering Research. 2023. Vol. 11, no. 1 A, pp.340-354.
https://search.emarefa.net/detail/BIM-1494694
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 353-354
Record ID
BIM-1494694