Machine-Vision-Based Bangladeshi Geographical Indication (GI) Dessert Recognition

Abstract

Geographical Indication (GI) certification preserves products whose identity and value are linked to their geographical origin. Despite the cultural and economic importance of Bangladeshi GI-certified desserts, no benchmark dataset or automated recognition system previously existed, creating risks of counterfeiting and mislabeling. This thesis addresses the gap by introducing BD-GI-Desserts13, a dataset of 11,692 images across 13 GI dessert classes, and proposing a machine-vision framework for fine-grained dessert recognition. The study develops a lightweight Custom Convolutional Neural Network (CCNN) and compares it with transfer-learning and traditional machine learning models. Experimental results show that the CCNN achieves 99.37% test accuracy while remaining computationally efficient for real-time web and mobile applications. The proposed framework provides a reproducible baseline for future GI food recognition research in Bangladesh and serves as a decision-support tool rather than a legal certification system.