@inproceedings{e79fe71f9e85401886322b68800d4b64,
title = "The Enhancement of Graphic QR Code Recognition using Convolutional Neural Networks",
abstract = "The use of QR Code has been flourishing on mobile and tablet platforms. By scanning the code, we can obtain targeted information synchronously. The regular QR Code, which consists of black and white modules, is neither visually pleasing nor recognizable by human vision. The application of graphic QR Code to product packaging and promotion campaign in the market has skyrocketed nowadays. However, printed graphic QR Code accompanies noise phenomenon that interferes the recognition itself and causes failure when user scanning. Therefore, we produce graphic QR Codes by data hiding with error diffusion techniques that first become training data. Then, we apply convolution neural networks to improve the data point recognition of graphic QR Codes. The experimental results show the superiority of the performance in both accuracy and recognition ability in comparison with normal QR Code readers.",
keywords = "convolution neural networks, graphic QR Code, noise",
author = "Lee, {Jong Kai} and Wang, {Yu Mei} and Lu, {Chun Shien} and Wang, {Hsi Chun} and Chou, {Tzren Ru}",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 8th International Conference on Innovation, Communication and Engineering, ICICE 2019 ; Conference date: 25-10-2019 Through 30-10-2019",
year = "2019",
month = oct,
doi = "10.1109/ICICE49024.2019.9117525",
language = "English",
series = "Proceedings of the 2019 8th International Conference on Innovation, Communication and Engineering, ICICE 2019",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "94--97",
editor = "Shoou-Jinn Chang and Sheng-Joue Young and Lam, {Artde Donald Kin-Tak} and Liang-Wen Ji and Hao-Ying Lu and Prior, {Stephen D.}",
booktitle = "Proceedings of the 2019 8th International Conference on Innovation, Communication and Engineering, ICICE 2019",
}