Multi-source Food Names Mapping Using OpenAI vision, Manual Dictionary and Fuzzy Matching Techniques

Abstract

Accurate harmonization of food names across heterogeneous and multilingual datasets remains a major challenge in food informatics, dietary assessment systems, and data-driven public health research. Modern AI-based food recognition models such as LogMeal, FoodSAM, and OpenAI Vision can identify multiple components within complex dishes, but they frequently produce inconsistent, culturally specific, and multilingual labels. These inconsistencies complicate downstream tasks including nutritional analysis and cross-dataset integration. In this study, we evaluated practical methods for mapping AI-generated food component names to Finnish menu-based ground truth in a real-world restaurant setting. We collected 320 meal images using an integrated camera–scale system; 167 images containing multi-component dishes were selected for detailed evaluation against Finnish lunch-line menu labels. We compared (i) a segment-aware, menu-constrained mapping approach that uses LogMeal segmentations and prompts OpenAI Vision to select the best-matching item from the daily menu for each segment, and (ii) a hybrid manually curated canonical dictionary and fuzzy string matching pipeline applied separately to labels from different AI sources. Mapping performance is measured using precision, recall, and F1-score. The segment-aware OpenAI Vision approach achieved the best overall results (Precision = 0.90, Recall = 0.70, F1 = 0.79), while the hybrid dictionary+fuzzy method also improved consistency over direct label matching. These results indicate that menu-aware segment-level reasoning and lightweight lexical normalization are effective for food-name harmonization and can support scalable dietary monitoring and menu analytics.

Keywords: Name Mapping, Fuzzy Matching, Entity Reconciliation, Data Integration, Name Standardization, String Similarity, Multilingual Datasets

DOI: 10.54941/ahfe1007316