International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 04 | Apr 2024
p-ISSN: 2395-0072
www.irjet.net
Image-to-Recipe Generation with Reverse Engineering Priyanka Manke1, Samit Shivtarkar2, Vaibhav Kundekar3, Hrishikesh Prabhu4 ,Divesh Masurkar5 1Professor, Dept. of Information Technology, Vasantdada Patil Pratishthan’s College of Engineering Mumbai, India 2-5Student, Dept. of Information Technology, Vasantdada Patil Pratishthan’s College of Engineering Mumbai, India
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Abstract - The Image-to-Recipe Generator is a
creative cooking. This technological breakthrough helps professional chefs looking for inspiration as well as home cooks who wish to produce meals that rival those seen in fine dining establishments. It also reduces food waste, contributes significantly to nutritional planning initiatives, and even aids in the preservation of local and traditional culinary traditions.
revolutionary advancement in culinary technology that uses artificial intelligence and computer vision to bridge the gap between visual stimuli and culinary competence. This technology offers a game-changing answer for both pros and foodies in an increasingly digitalized world where convenience and innovation coexist. The generator uses state-of-the-art deep learning algorithms to evaluate food photographs and determine ingredients, quantities, and cooking methods with remarkable precision. This complete approach integrates image processing, contextual comprehension, recipe synthesis, and item identification to smoothly convert photos into detailed culinary instructions. The generator uses a large dataset of food photographs and recipe cards to mimic popular dishes and promote creativity and experimentation in the kitchen. The generator can be applied to more than just meal planning and recipe discovery; it can also be helpful for cultural exchange, nutrition management, and culinary instruction. This innovative instrument, which sits at the intersection of technology and gastronomy, marks the progress of AI-powered culinary solutions and ushers in a new era of accessibility, innovation, and involvement for the food industry.
As we explore the realm of image-to-recipe generation further, we find a point of confluence between AI and creative cooking that offers a fresh and insightful perspective on how we perceive, comprehend, and savour the culinary delights that are all around us. This combination gives us a fresh viewpoint that enables us to appreciate and cherish the many different culinary experiences that enhance our life.
1.1 OBJECTIVE Designing, creating, and testing a sophisticated Image-toRecipe Generation system using the latest Convolutional Neural Networks (CNN) in combination with JSON data format is the main goal of this project. The system is designed to handle incoming photos with ease, taking advantage of CNNs' capabilities to extract pertinent features and patterns. The next step is to use a well selected JSON file database to reverse engineer these visual clues into structured recipe data.
Key Words: Deep learning, computer vision, artificial intelligence, image-to-recipe generator, and culinary technology.
the IMDb ratings they give. This correlation can help us predict IMDb ratings based on comments. The research aims to showcase the effectiveness and resilience of machine learning methods in automating the creation of recipes based on visual inputs, utilizing this novel approach. The research also aims to evaluate the system's scalability, accuracy, and efficiency, which will further the development of natural language processing and computer vision technologies in the field of culinary arts and recipe recommendation systems.
1.INTRODUCTION At the nexus of artificial intelligence and culinary arts, this Image-to-recipe creation offers a unique and intriguing take on cooking. This creative notion goes against the grain of the conventional cooking procedure, which starts with a recipe. Using sophisticated machine learning techniques deep learning and computer vision being two of the most notable image-to-recipe generation starts with an image of a finished dish and breaks down the recipe that produced it. With the use of this state-of-the-art technology, it is possible to precisely inspect a picture of food and determine the individual ingredients, preparation techniques, and steps required to accurately recreate the dish. It's similar to having your own private chef investigator, exposing every gastronomic mystery contained in a single picture. There are many different consequences for image-to-recipe generation. It might make food more accessible, streamline the process of preparing meals, and create new opportunities for
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1.2 MOTIVATION In a world where convenience and creativity are becoming more and more important, technology is also having a disruptive effect on the culinary industry. Conventional approaches to finding and developing recipes frequently depend on text-based searches, which can be laborious and restrictive for people looking for ideas or who have certain dietary requirements.
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