Dietary Supplement Products Registration Verification Application Using Thai FDA Number Images with OpenAI Vision API

Arnon Phongrusamepane; Nattapat Thongkaew; Teetach Chuenkamonrat; Chalothon Chootong; Chatchai Kasemtaweechok

doi:10.46604/peti.2026.15693

Authors

Arnon Phongrusamepane Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Bangkok, Thailand
Nattapat Thongkaew Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Bangkok, Thailand
Teetach Chuenkamonrat Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Bangkok, Thailand
Chalothon Chootong Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Bangkok, Thailand
Chatchai Kasemtaweechok Department of Computer Science and Information, Faculty of Science at Sriracha, Kasetsart University, Bangkok, Thailand

DOI:

https://doi.org/10.46604/peti.2026.15693

Keywords:

Thai FDA number, dietary supplement products verification, OpenAI vision API, optical character recognition

Abstract

Thai Food and Drug Administration (FDA) registration numbers displayed on product labels are essential for verifying the authenticity of dietary supplements. This study aims to develop a Dietary Supplement Verification Application (DSVAPP) that enables consumers to upload images of product labels containing FDA registration numbers to authenticate products. The system uses the OpenAI vision Application Programming Interface (API) to extract registration numbers, thereby reducing error propagation between object detection and Optical Character Recognition (OCR). DSVAPP is evaluated using 118 images and nine combinations of object detection and OCR methods. The results demonstrate that the DSVAPP incorporating the GPT-5.2 vision model achieves superior performance compared to most evaluated methods.

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