When is it advisable to use a Machine Learning (ML) model–based solution for document processing?
An ML model solution is recommended for structured or semi-structured documents where the layout differs significantly from one document provider to another, since a trained model can generalize across varying field positions and formats rather than depending on a single fixed template. In contrast, simple, predictable, fixed-layout documents with little layout variation are typically better suited to OCR or template/rule-based extraction methods, which are less flexible but sufficient when the document structure does not change.
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