An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging

De Sousa Ribeiro, Fabio and Gong, Liyun and Caliva, Francesco and Gudmundsson, Kjartan and Swainson, Mark and Yu, Miao and Leontidis, Georgios and Ye, Xujiong and Kollias, Stefanos (2018) An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging. In: Image Processing, IEEE International Conference on, 7-10 October 2018, Athens, Greece.

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An End-to-End Deep Neural Architecture for Optical Character Verification and Recognition in Retail Food Packaging
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Item Type:Conference or Workshop contribution (Paper)
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Abstract

There exist various types of information in retail food packages, including food product name, ingredients list and use by date. The correct recognition and coding of use by dates is especially critical in ensuring proper distribution of the product to the market and eliminating potential health risks caused by erroneous mislabelling. The latter can have a major negative effect on the health of consumers and consequently raise legal issues for suppliers. In this work, an end-to-end architecture, composed of a dual deep neural network based system is proposed for automatic recognition of use by dates in food package photos. The system includes: a Global level convolutional neural network (CNN) for high-level food package image quality evaluation (blurry/clear/missing use by date statistics); a Local level fully convolutional network (FCN) for use by date ROI localisation. Post ROI extraction, the date characters are then segmented and recognised. The proposed framework is the first to employ deep neural networks for end-to-end automatic use by date recognition in retail packaging photos. It is capable of achieving very good levels of performance on all the aforementioned tasks, despite the varied textual/pictorial content complexity found in food packaging design.

Keywords:deep learning, Optical Character Verification, machine learning, image processing
Subjects:G Mathematical and Computer Sciences > G400 Computer Science
Divisions:College of Science > National Centre for Food Manufacturing > Lincoln Institute for Agri-Food Technology
College of Science > School of Computer Science
ID Code:31978
Deposited On:10 Jul 2018 13:30

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