OCR-Based Ingredient Recognition for Consumer Well-Being

Many companies utilize retail tactics to entice consumers into purchasing their products, all the while obscuring the potential risks associated with certain ingredients used in these products. This lack of transparency places consumers at risk of unforeseen consequences. The development of mobile applications for recognizing text from images and processing it for various purposes has become an increasingly important research area. In this paper, we present an OCR-based Android application designed to recognize ingredients on product labels and match them with a database of toxic substances. The application uses the Google ML Kit to recognize text and the Levenshtein algorithm to calculate the similarity between the recognized ingredients with the database. The application retrieves information on toxic substances from the Firebase database, which is used to validate the recognized ingredients. The application also implements a user interface for displaying the list of toxic ingredients found in the product, providing a warning to the user. Our application provides a simple and effective method to check for toxic ingredients, ensuring consumer safety.