What type of documents does the Semi-structured AI extractor primarily handle?

The Semi-structured AI extractor is designed to effectively handle documents that do not fit neatly into a fully structured format but still contain some organized elements. These documents often include specific fields or sections that can be consistently identified, even though they might vary in layout or contain irregularities. Typical examples of semi-structured documents include invoices, receipts, and forms where certain pieces of information are structured (like dates, total amounts, etc.), but the overall document may change from one instance to another.

By managing both regular and complex document types, the Semi-structured AI extractor can address a wide variety of scenarios. This flexibility is crucial for automating the extraction of data from documents where full consistency is not guaranteed but where some level of structural coherence exists. Other options do not reflect this broader capability, as structured documents are overly rigid, unstructured documents wouldn't have defined sections for data extraction, and images with handwritten content are typically outside the realm of semi-structured data, requiring different processing techniques.