AI-Powered Recipe Generator from Food Images Using Deep Learning

Abstract

Theautomaticgenerationofcookingrecipesfromfoodimageshasgainedsignificantattentioninthe fieldoffoodcomputingandartificialintelligence.Thisresearchpresentsadeeplearning-basedapproach for recipe generation from food images, achieving an accuracy of 92.7%.The proposed model utilizes computer vision techniques to analyze food images and predict essential recipe components, including therecipetitle,ingredients,andstep-by-stepcookinginstructions.AcombinationofConvolutionalNeu- ral Networks (CNNs) and Transformer-based architectures enhances the system’s ability to understand complex food compositions.The dataset used comprises diverse food categories, ensuring robust gen- eralization across various cuisines.Performance evaluation against benchmark datasets highlights the model’ssuperiorityingeneratingcoherentandcontextuallyaccuraterecipes.Comparisonswithstate-of- the-artmodels,includingInverseCookingandFIRE,demonstrateimprovementsiningredientprediction and instruction coherence.Despite achieving high accuracy, challenges such as ingredient ambiguity and complex dish representations persist.Future work aims to refine multimodal learning approaches andintegratereal-timefoodrecognitionforenhanceduserexperience. Thisstudycontributestoadvanc- ing AI-driven food recommendation systems, bridging the gap between computer vision and culinary knowledge.

Introduction

Background

• Food is central to culture and daily life, and automated recipe generation from food images is an emerging field in food computing, AI, and computer vision.

• Previous models like Inverse Cooking and FIRE demonstrated the potential of multimodal learning (images + text), but faced issues with ingredient ambiguity, complex dishes, and instruction coherence.

• This study proposes an advanced deep learning model achieving 92.7% accuracy in generating recipes from images.

2. Objectives

• Develop a deep learning system that predicts recipe titles, ingredients, and instructions from food images.

• Evaluate performance using accuracy and BLEU scores.

• Compare results with state-of-the-art models (Inverse Cooking, FIRE).

• Address challenges like missing ingredients and multi-component dish complexity.

• Contribute to food computing via multimodal AI integration.

3. Significance

This research impacts multiple domains:

1. AI & Food Computing: Improves how AI understands food content, aiding recommendation systems.

2. Health & Nutrition: Helps in dietary tracking, calorie estimation, and personalized nutrition.

3. Human-Computer Interaction: Enables smart kitchen assistants and cooking guides using voice and vision.

4. Food Industry Support: Assists chefs, bloggers, and restaurants in automated recipe creation and content generation.

5. Multimodal AI & NLP: Advances the fusion of visual and textual data for better food recognition and recommendation.

4. Literature Survey

A. Food Image to Recipe Generation

• Inverse Cooking: Used a two-stage neural model (ingredient prediction + instruction generation). Faced issues with ambiguity and dish complexity.

• FIRE: Combined CNNs and transformers for better multimodal learning but still struggled with overlapping ingredients.

B. Deep Learning for Food Recognition

• CNN-based models by Kagaya et al. and Bolanos et al. improved food classification and calorie estimation.

C. Multimodal AI

• Cross-modal retrieval and contrastive learning enhanced image-text alignment and recipe recommendation.

D. Challenges

1. Ingredient ambiguity

2. Complex dish representations

3. Instruction coherence

5. Methodology

A. Data Pre-processing

• Resize images to 224×224

• Normalize pixel values

• Tokenize ingredients

• Remove meaningless stopwords

B. Data Augmentation

• Rotation, flipping, brightness adjustment, Gaussian noise to increase model robustness

C. Model Architecture

• Image Encoder: Fine-tuned ResNet-50, outputs a 512-dim feature vector

• Recipe Generator: Transformer-based sequence model for generating structured recipes (titles, ingredients, steps)

D. Training

• Loss functions: cross-entropy (ingredients), sequence loss (instructions)

• Batch size: 32, Epochs: 50, Optimizer: Adam, Learning rate: 0.0001

6. Results and Discussion

A. Performance

• Achieved 92.7% validation accuracy and nearly 95% training accuracy

• BLEU scores used to measure instruction generation quality

B. Key Observations

• High accuracy indicates successful use of CNN + transformer architecture.

• Minimal overfitting, as seen from close training and validation metrics.

• Scalability: Modular design supports larger datasets and potential multi-language support.

Conclusion

In conclusion, addressing current challenges and incorporating proposed advancements in food image-to- recipe systems can significantly enhance their utility and accessibility.By improving the accuracy and robustnessofimagerecognitionmodels,thesesystemscanhandlevariousfoodtypes—includingcomplex, poorly lit, or unconventional images—and expanding the recipe database to include diverse cultural, re- gional, and dietary variations ensures inclusivity for a wide range of preferences and restrictions.The integration of personalized recipe suggestions based on users’ health data, nutritional needs, and availableingredientsprovidesatailoredexperiencethatpromoteshealthierchoices.Furthermore, incorpo- rating voice and multimodal inputs, along with compatibility with smart kitchen devices, offers seamless, hands-free assistance.As these technologies evolve with real-time feedback, adaptive learning, and user- centric features, they will transform food preparation and meal planning—empowering users to cook with ease, creativity, and confidence.

References

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Copyright

Copyright © 2025 Deepak Ramakant Yannadle, Chirayu Rakesh Vartak, Disha Chandrakant Manjarekar, Sharadha Shah. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.