• Srikande Sowmya
• Sreya Basavaraju
• Phalguni Raparla
• Bhuvan Boyina

This project proposes a novel approach to automatically generate recipes from food images utilizing the powerful image recognition capabilities of the Google Inception v3 model. In recent years, with the proliferation of social media platforms and food-related applications, there has been a growing demand for automated systems capable of analyzing food images and providing detailed recipes. Leveraging the deep learning architecture of Google Inception v3, which has been pre-trained on a vast dataset including food images, our system extracts meaningful features from input images to accurately identify various food items. The key contribution of this project lies in its ability to seamlessly integrate image recognition technology with recipe generation, providing users with an efficient and user-friendly solution for obtaining recipes from food images. The utilization of Google Inception v3 ensures high accuracy in ingredient recognition, while the incorporation of natural language processing techniques enhances the quality of the generated recipes. Through experimental evaluations, we demonstrate the effectiveness and feasibility of our approach in generating accurate and diverse recipes from food images. This project has the potential to significantly simplify the process of discovering and preparing new dishes, thereby catering to the needs of food enthusiasts, amateur cooks, and culinary professionals alike.

Recipe Generation, Image Recognition, Food Images, Inception v3, CNN, Deep Learning.

"Recipe Generation Using Food Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 3, page no.k15-k23, March-2024, Available :http://www.jetir.org/papers/JETIR2403A02.pdf

"Recipe Generation Using Food Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 3, page no. ppk15-k23, March-2024, Available at : http://www.jetir.org/papers/JETIR2403A02.pdf