Abstract
AbstractThere exist various types of information on retail food packages, including use by date, food product name and so on. The correct coding of use by dates on food packages is vitally important for avoiding potential health risks to customers caused by erroneous mislabelling of use by dates. It is extremely tedious and laborious to check the use by dates coding manually by a human operator, which is prone to generate errors thus an automatic system for validating the correctness of the coding of use by dates is needed. In order to construct such a system, firstly it needs to correctly automatic recognize use by dates on food packages. In this work, we propose a novel dual deep neural networks-based methodology for automatic recognition of use by dates in food package photographs recorded by a camera, which is a combination of two networks: a fully convolutional network for use by date ROI detection and a convolutional recurrent neuron network for date character recognition. The proposed methodology is the first attempt to apply deep learning for automatic use by date recognition. From comprehensive experimental evaluations, it is shown that the proposed method can achieve high accuracies in use by date recognition (more than 95% on our testing dataset), given food package images with varying lighting conditions, poor printing quality and varied textual/pictorial contents collected from multiple real retailer sites.
Publisher
Springer Science and Business Media LLC
Subject
Electrical and Electronic Engineering,Signal Processing
Reference18 articles.
1. Eurostat: Manufacturing statistics—NACE Rev. 2. http://ec.europa.eu/eurostat/statistics-explained/index.php/ (2014)
2. WHO FAO: http://www.fao.org/docrep/012/a1552e/a1552e00.html (2009)
3. Mori, S., Suen, C., Yamamoto, K.: Historical review of OCR research and development. Proceedings of the IEEE 80(7), 1029–1058 (1992)
4. Epshtein, B., Ofek, E., Wexler, Y.: Detecting text in natural scenes with stroke width transform. In: Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, USA (2010)
5. Chen, H., Tsai, S., Schroth, G., Chen, D., Grzeszczuk, R., Girod, B.: Robust text detection in natural images with edge-enhanced maximally stable extremal regions. In: 18th IEEE International Conference on Image Processing (ICIP), Brussels, Belgium (2011)
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