Recipe Generation From Food Image

DOI

10.2991/978-94-6463-858-5_106How to use a DOI?

Keywords

Image analysis; Recipie classification; Recipie Generation

Abstract

Recipe generation from food images represents a complicated multimodal challenge that needs the system to both identify visual components and realize cooking process sequences. A system composed of three principal procedures starts with ingredient detection followed by instruction sequence creation then performs cross-modal synchronization. Concurrently we review two advanced models including im2recipe and Recipe1M + where deep convolutional neural networks (CNNs) extract image features while recurrent neural networks (RNNs) or transformers create language outputs. Missing components of cooking models perform better after tuning them with culinary sources to deliver improved results in ingredient detection and recipe generation. The addition of GPT or BART generative models allows users to input ingredients for natural human-like cooking instructions. Despite these advances, challenges remain. Two dishes that look alike might include totally different ingredients and preparation procedures because of ambiguity. Generalization suffers from the shortage of extensive annotated culinary database entries. The assessment process for generated recipes proves to be non-trivial since complete accuracy evaluation often requires human reviewers to perform assessments. The abstract outlines the main techniques together with the existing difficulties and future development possibilities of creating recipes from food images. Modern kitchen assistants along with dietary tracting systems and food logging applications and personalized meal planning solutions form the core applications of this technology. The capabilities of these systems will improve substantially when the research adds more complex information about food and cultural background as well as sensory data including taste and smell.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Gugulothu Venkanna
AU  - C. Madhu Kumar
AU  - P. Veenapani
AU  - P. Savya Reddy
PY  - 2025
DA  - 2025/11/04
TI  - Recipe Generation From Food Image
BT  - Proceedings of International Conference on Computer Science and Communication Engineering (ICCSCE 2025)
PB  - Atlantis Press
SP  - 1271
EP  - 1282
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-858-5_106
DO  - 10.2991/978-94-6463-858-5_106
ID  - Venkanna2025
ER  -