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A Framework to Identify Allergen and Nutrient Content in Fruits and Packaged Food using Deep Learning and OCR

机译:使用深度学习和OCR鉴定水果和包装食品中过敏原和营养含量的框架

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Allergic reactions to food can depend on a wide range of factors and hence, the proportionate reactions of the same can vary. With such a wide range of unpredictability, classifying allergens, and the rate at which it would effect is what the scientists have been working on for years. To bring awareness of the food we consume and the potential threats it can cause us, in this paper we propose a 2-Tab Deep Learning based Application to provide the nutrient and allergen content in fruits and vegetables and, to display allergen information in packaged food using OCR. Through a novel Deep Learning Framework, the picture of the Fruit or Vegetable captured via an application is classified and recognized and the nutritional facts and allergen information is presented. The fine-tuned deep learning model which is deployed in cloud, obtained a good accuracy of 97.37 percentage on our dataset. For Packaged food, the picture of the Ingredient Index is captured via the application and the allergen information is presented after the text is recognized through Optical Character recognition which would be carried out in a remote server.
机译:对食品的过敏反应可以取决于各种因素,因此,相同的比例反应可以变化。具有如此广泛的不可预测性,分类过敏原,以及它将产生的速度是科学家多年来一直在努力的方式。为了提高我们消耗的食物的认识以及它可能导致我们的潜在威胁,在本文中,我们提出了一个2个标签的基于深度学习的应用程序,为水果和蔬菜提供营养和过敏原含量,并在包装​​食品中显示过敏原信息使用OCR。通过新的深层学习框架,通过申请捕获的水果或蔬菜的图片被分类和认可,并呈现营养事实和过敏原信息。在云中部署的微调深度学习模型,在我们的数据集中获得了97.37个百分比的良好准确性。对于包装食品,通过应用程序捕获成分索引的图像,并且在通过光学字符识别识别文本之后呈现过敏原信息,该光学字符识别将在远程服务器中执行。

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