The practical answer
OCR recognizes document text. Structured capture maps relevant information into fields. Validation checks those fields against business rules. A receiving workflow also needs exception review and an approved output; none of these layers alone proves the delivery is correct.
Text recognition is one layer
OCR turns the marks on a scan or image into machine-readable text. Layout information can help identify where that text appears. Recognition quality matters: a misread digit or unclear scan can affect a downstream quantity.
OCR and structured extraction are not mutually exclusive products. Some document-processing systems already extract key-value pairs and tables. Evaluate what an existing tool can do on your documents before adding another model.
Field capture gives the text a record structure
A receiving record needs agreed fields, not just a block of text. Capture may map a document reference, item description, unit, quantity and printed total into a consistent structure. Document layouts and terminology can vary by supplier.
AI assistance can be evaluated for this mapping when it is useful. Keep a source reference for the proposed value and a review path for missing or ambiguous information. Do not treat a well-formatted answer as proof that the extracted fields are correct.
Business validation asks a different question
Validation checks whether the proposed fields meet explicit rules. Are required values present? Is a carton count a whole number? Do line quantities agree with the printed total? These checks can be deterministic even when capture uses AI.
The synthetic example on wrkflwai starts with two line quantities of 60 and 40 and a printed total of 98. Adding the line values exposes the discrepancy. This check does not recognize text from a document; it checks already structured counts.
Review and approval connect the layers to real work
A reviewer needs to see the original source, the proposed record and the reason for a flag. Resolving an inconsistency may require checking the physical delivery, contacting the supplier or following an internal escalation process.
The receiving demo enables a local CSV only after matching values and an explicit approval. Editing a value clears that approval. A production system must additionally specify reviewer identity, permissions and approval history.
Choose the smallest useful workflow
Start by checking your existing document-processing and warehouse tools. If they already capture the fields, the remaining problem may be validation, exception handling or the handoff to your system.
Compare approaches using representative examples and expected correct records. Measure the review effort and the cost of getting a field wrong, alongside extraction correctness. Scope the first destination and the acceptance criteria before building.
- Basic OCR: useful when the primary need is readable text.
- Structured document capture: useful when consistent fields or table rows are needed.
- AI-assisted interpretation: evaluate where layout or wording variability warrants it.
- Business checks and staff review: define them around the operational decision in every approach.