A Structured Excel Dataset for International Trade Invoice Information Extraction and Anomaly Detection

On the premise of protecting privacy, provide research samples with diverse formats, complete field hierarchy, and traceable task labeling, including standard invoices, JSON files of invoice recognition results, question and answer labeling, enhanced invoices, and error labeling in various data forms.

It can support multiple research tasks such as complex table understanding, fine-tuning of big language models, structured generation, anomaly detection, and intelligent question and answer, providing a reusable data foundation for intelligent processing of cross-border logistics documents. The dataset is based on 99 Excel invoice templates from real business, combined with a virtual entity database covering 30 countries and regions (3022 enterprise records, 3028 address records) and a product database containing 2915 real HS Code constraints.