基于深度神经网络的过期日期识别

Traian Rebedea, V. Florea
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引用次数: 3

摘要

本文提出了一种用于光学字符识别的深度学习解决方案,专门用于检测印在食品包装上的过期日期。这种方法可用于减少食物浪费,对智能冰箱的设计产生重大影响,并且通过将其与语音合成引擎相结合,可以证明对视力有困难的人特别有用。设计有效的过期日期识别解决方案的主要问题是缺乏足够大的数据集来训练深度神经网络。为了解决这个问题,我们建议使用一个由合成生成的图像组成的额外数据集。本文详细介绍了合成图像和真实图像数据集,并表明所提出的方法比单独使用真实图像的精度提高了9.4%。
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Expiry date recognition using deep neural networks
This paper proposes a deep learning solution for optical character recognition, specifically tuned to detect expiration dates that are printed on the packaging of food items. This method can be used to reduce food waste, having a significant impact on the design of smart refrigerators and can prove especially useful for persons with vision difficulties, by combining it with a speech synthesis engine. The main problem in designing an efficient solution for expiry date recognition is the lack of a large enough dataset to train deep neural networks. To tackle this issue, we propose to use an additional dataset composed of synthetically generated images. Both the synthetic and real image datasets are detailed in the paper and we show that the proposed method offers a 9.4% accuracy improvement over using real images alone.
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