Featured Project
IngredientInsight AI
AI-Powered Food Ingredient Analysis
About the project
Project Overview
IngredientInsight AI is a web-based food ingredient analysis platform that helps users understand packaged food products by scanning their ingredient labels. The system uses OCR to extract ingredient information from an uploaded image and analyzes the detected ingredients to provide risk levels, categories, a safety score, and an easy-to-understand health summary.
01
The Problem
Food product labels often contain long and complicated ingredient lists with preservatives, artificial colors, sweeteners, emulsifiers, and other additives. For an average consumer, understanding what these ingredients mean and whether they should be concerned about them can be difficult and time-consuming.
02
The Solution
IngredientInsight AI simplifies food label analysis by allowing users to capture or upload an ingredient-label image. The application extracts the text using OCR, identifies individual ingredients, compares them against an ingredient knowledge base, classifies their potential risk, and generates an overall safety score with a clear visual summary.
What it offers
Key Features
Food ingredient label image upload
Camera-based ingredient scanning
OCR-based text extraction
Automatic ingredient extraction and cleaning
Ingredient classification
Preservative and additive detection
Artificial color and flavor detection
Sweetener, stabilizer and emulsifier detection
Allergen identification
Ingredient risk-level analysis
Overall food safety score
Visual safety and risk indicators
Ingredient-wise explanations
AI-generated food analysis summary
Scan history
Mobile responsive interface
Built with
Technologies Used
My contribution
My Role
Designed and developed the full-stack application, including the responsive frontend, image-upload workflow, backend REST APIs, OCR processing pipeline, ingredient analysis logic, safety-score calculation, and result visualization.
What I solved
Challenges
Extracting accurate text from food labels with different fonts, layouts and image quality
Cleaning and separating OCR output into individual ingredients
Handling ingredient names, INS/E-numbers and different naming formats
Designing a meaningful and understandable ingredient risk classification system
Calculating an overall safety score from multiple ingredient risk levels
Creating a mobile-friendly scanning experience
Connecting the frontend image-upload workflow with the backend processing pipeline
Outcome
Results & Outcome
The project provides a simple workflow for turning a food ingredient-label image into structured and understandable information. Users can quickly view detected ingredients, their categories and risk levels, along with an overall safety score and analysis summary.
Inside the project
Project Screenshots
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