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

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.