What is intelligent document processing? Text recognition is only one stage
Explain recognition-to-extraction pipelines and separate OCR, layout and business validation.
Understand the concept
Intelligent document processing converts files into usable business data through recognition, parsing or models, potentially including OCR, classification and extraction. Products differ. Structured outputs can misassociate values, so verify originals and distinguish recognition, valid fields and downstream acceptance.
Document structure requires relationships beyond text
Microsoft and Google provide document analysis and extraction. Table values need headers and units; concatenated text can lose meaning. Preset and custom processors have bounded scope, and invoice demos do not validate arbitrary contracts. Define fields and source locations and retain reading order. Business totals are separate from character recognition.
Verify fields and evidence in one file
Use a public form, preserve fields and page regions and verify locations. Test missing dates and multi-page tables for unknowns and relationships. Downstream code needs types and business checks rather than nonempty values alone. No document service was used here. Separate reading-order, extraction and rule errors before changing models.
Replay cases after templates and sources change
New scanners or templates change inputs. Version files and processors and replay difficult cases. Define errors and review states before bulk ingestion. Include correction and recovery, not just page fees. Start with stable formats and expand from evidence. Trace critical fields to sources; automation does not remove data-accuracy responsibility.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.