Christoph Brosch

Christoph Brosch, Alexander Bouwens, Sebastian Bast et al.

The enormous progress in the field of artificial intelligence (AI) enables retail companies to automate their processes and thus to save costs. Thereby, many AI-based automation approaches are based on machine learning and computer vision. The realization of such approaches requires high-quality training data. In this paper, we describe the creation process of an annotated dataset that contains 1,034 images of single food products, taken under studio conditions, annotated with 5 class labels and 30 object detection labels, which can be used for product recognition and classification tasks. We based all images and labels on standards presented by GS1, a global non-profit organisation. The objective of our work is to support the development of machine learning models in the retail domain and to provide a reference process for creating the necessary training data.

Christoph Brosch, Sian Brumm, Rolf Krieger et al.

Generative AI and large language models (LLMs) offer significant potential for automating the extraction of structured information from web pages. In this work, we focus on food product pages from online retailers and explore schema-constrained extraction approaches to retrieve key product attributes, such as ingredient lists and nutrition tables. We compare two LLM-based approaches, direct extraction and indirect extraction via generated functions, evaluating them in terms of accuracy, efficiency, and cost on a curated dataset of 3,000 food product pages from three different online shops. Our results show that although the indirect approach achieves slightly lower accuracy (96.48\%, $-1.61\%$ compared to direct extraction), it reduces the number of required LLM calls by 95.82\%, leading to substantial efficiency gains and lower operational costs. These findings suggest that indirect extraction approaches can provide scalable and cost-effective solutions for large-scale information extraction tasks from template-based web pages using LLMs.