Volume 12, Issue 5 · IJARST · ISSN 2319-1783 / 2320-1126

Recipe Generation from Food Images

Recipe Generation from Food Images is an open-access, peer-reviewed research paper by V.Varun, Y. Sai Pranay, B. Athirath, Mr.CH. Srinath Reddy, published in Volume 12, Issue 5 of the International Journal of Advanced Research in Science and Technology (IJARST), a UGC-approved journal (Print ISSN 2319-1783, Online ISSN 2320-1126).

Author

V.Varun, Y. Sai Pranay, B. Athirath, Mr.CH. Srinath Reddy

Abstract

Many people enjoy food photography because it highlights the beauty of food. Images of food do not, however, provide any information regarding the method of preparation or the difficulty of the recipe used to create each dish. Convolutional Neural Network (CNN) is used to create an inverse cooking system that creates cooking instructions from food photographs. Without prescribing any order, the system uses a novel architecture to forecast elements and their dependencies. Then, while concurrently taking into account the image and implied ingredients, it provides cooking directions. On the Recipe 1M dataset, the system's performance was carefully assessed, and the results showed that ingredient prediction was more accurate than with earlier techniques. By utilising both the image and the inferred ingredients, the system was also able to generate high-quality recipes. Human review revealed that these recipes were more interesting than those produced by retrieval-based methods.

DOI

https://doi.org/10.62226/ijarst20230538

PAGES : 984-987 | 23 VIEWS | 43 DOWNLOADS

How do you cite this paper?

V.Varun, Y. Sai Pranay, B. Athirath, Mr.CH. Srinath Reddy — “Recipe Generation from Food Images.” International Journal of Advanced Research in Science and Technology (IJARST), Volume 12, Issue 5. DOI: https://doi.org/10.62226/ijarst20230538.

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