Training, validation and test splits for the datasets by [1], [2], [3] and [4]. Also cross-validation splits are also included.
Some annotations are incomplete for detecting several classes of barcodes, such as QR-Codes and EAN-Codes, other annotations are corrupted in terms of misplaced or only partly correct annotations. This data should generally be excluded from the training and test process. If applicable each dataset folder contains a *_corrupted_annotations.csv which marks corrupted data.
[1]
S. Wachenfeld, S. Terlunen, und X. Jiang, „Robust 1-D Barcode Recognition on Camera Phones and Mobile Product Information Display“, in Mobile Multimedia Processing: Fundamentals, Methods, and Applications, X. Jiang, M. Y. Ma, und C. W. Chen, Hrsg. Berlin, Heidelberg: Springer, 2010, S. 53–69. doi: 10.1007/978-3-642-12349-8_4.
[2]
G. Sörös und C. Flörkemeier, „Blur-resistant joint 1D and 2D barcode localization for smartphones“, in Proceedings of the 12th International Conference on Mobile and Ubiquitous Multimedia - MUM ’13, Dez. 2013, S. 1–8. doi: 10.1145/2541831.2541844.
[3]
A. Zamberletti, I. Gallo, und S. Albertini, „Robust Angle Invariant 1D Barcode Detection“, gehalten auf der 2013 2nd IAPR Asian Conference on Pattern Recognition, Nov. 2013.
[4]
P. Bodnár, T. Grósz, L. Tóth, und L. G. Nyúl, „Efficient visual code localization with neural networks“, Pattern Anal Applic, Bd. 21, Nr. 1, S. 249–260, Feb. 2018, doi: 10.1007/s10044-017-0619-6.