There 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 recognise 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 photos recorded by a camera, which is a combination of two networks: a fully convolutional network (FCN) for use by date ROI detection and a convolutional recurrent neuron network (CRNN) 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.

中文翻译：

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一种新颖的统一深度神经网络方法，用于零售食品包装图像中的日期识别

零售食品包装上存在各种类型的信息，包括按日期使用、食品名称等。食品包装上按日期正确编码对于避免因日期错误标注错误而对客户造成的潜在健康风险至关重要。人工检查按日期编码的使用极其繁琐和费力，容易产生错误，因此需要一种自动验证按日期编码的正确性的系统。为了构建这样的系统，首先需要正确地自动识别食品包装上的按日期使用。在这项工作中，我们提出了一种新的基于双深度神经网络的方法，用于自动识别由相机记录的食品包装照片中的日期使用，这是两个网络的组合：用于日期 ROI 检测的全卷积网络 (FCN) 和用于日期字符识别的卷积递归神经元网络 (CRNN)。所提出的方法是首次尝试将深度学习应用于日期识别的自动使用。综合实验评估表明，在给定照明条件不同、印刷质量差和文本/图片内容不同的食品包装图像的情况下，所提出的方法可以通过日期识别实现高精度（在我们的测试数据集上超过 95%）从多个真实零售商网站收集。