Abstract:
Understanding every ingredient in a food is
crucial for everyone who has food allergies and
consumes packaged foods. However, the
component or ingredient lists are unable to
recognized by only reading due to the lack of
education related to coding conventions among
the public. Despite the availability of certain
allergen recommendation applications that might
aid in pinpointing ingredients, the available
applications are not personalized and are not
supported efficiently. This research aims to
develop a personalized allergen recommender
system that helps with safe food selections
through ingredient list scanning. The final
deliverable is a mobile application that scans the
list of ingredients, classifies allergen ingredients
for
the consumer, and provides allergen
recommendations to the user. The application
can identify high-risk and the most common 25
food ingredients that can cause allergic. The
Tesseract OCR has been used to extract text from
the captured image of ingredients. After the text
is extracted, the application compares it with a
user-predefined list of allergens and based on the
result provides the recommendation to the user.
The developed application was able to
successfully
extract
ingredient
text
and
accurately identify allergenic ingredients in real
time. This study demonstrates that a AI -enhanced
personalized allergen recommender system can
improve user safety and awareness, supporting
informed decisions about food products.
