Natural Scene Nutrition Information Acquisition and Analysis Based on Deep Learning

Title:Natural Scene Nutrition Information Acquisition and Analysis Based on Deep Learning

Volume: 15 Issue: 7

Author(s): Tianyue Zhang, Xu Wei, Zhi Li, Fangzhe Shi, Zhiqiang Xia, Mengru Lian, Ling Chen and Hao Zhang*

Affiliation:

• College of Computer Science and Technology, Jilin University, Changchun,China

Keywords: Scene text detection, scene text recognition, deep learning, precision medicine, nutritional requirements, healthy diet.

Abstract:

Background: In the field of personalized health, it is often difficult for individuals to obtain professional knowledge to solve their practical problems timely and accurately. While there are some applications that can get targeted information, they often fail to function properly in nonideal environments, and they cannot achieve precise answers to individual users. Therefore, how to establish an information capture model based on big data and combine it with intelligent search is an important issue in the field of personalized health.

Objective: This paper starts with the information acquisition and intelligent recommendation in the field of personalized health, and proposes a natural scene information acquisition and analysis model based on deep learning, focusing on improving the recognition rate of text in natural scenes and achieving targeted smart search to allow users to get more accurate personalized health advice.

Methods: In this model, natural scene information is processed from four aspects: targeted big data collection and search, connected text proposal network text detection algorithm and projectionbased text segmentation, capsule network text recognition and result analysis. The model reduces recognition bias due to problems such as special filming conditions and photographic techniques by using deep learning algorithms. At the same time, the data mining has also improved the pertinence of the results analysis.

Results: The proposed model is applied to analyze the user's nutrient intake requirements. The results show that the method achieves 83% prediction accuracy on the nutrient composition table dataset, and its performance is better than current convolutional neural network applications. And the model can get accurate personalized data to provide users with dietary advice.

Conclusion: This model combines deep learning and data mining methods to obtain intelligent solutions at a professional level by uploading target information images in non-ideal environments, and is suitable for accurate analysis of problems in personalized health area.

Cite this article as:

Zhang Tianyue , Wei Xu , Li Zhi , Shi Fangzhe, Xia Zhiqiang , Lian Mengru , Chen Ling and Zhang Hao *, Natural Scene Nutrition Information Acquisition and Analysis Based on Deep Learning, Current Bioinformatics 2020; 15 (7) . https://dx.doi.org/10.2174/1574893614666190723121610