Supply chain settlement documents often contain complex tables with merged cells, multi-level headers, and irregular layouts, making them difficult to parse for AI systems. This post presents a method for restoring table structure and binding fields to specific data points, enabling accurate extraction for downstream agents. Key techniques include layout analysis, OCR post-processing, and semantic field mapping. The approach is demonstrated on real-world settlement sheets, showing improved accuracy over generic parsers. Developers can adapt these techniques for similar document types in logistics, finance, and procurement.
A technical deep dive into parsing complex supply chain settlement tables, with structure restoration and field binding for AI agents.