The download contains OCR response metadata and text, not fabricated key-value relationships. Human review is required before mapping spatial form fields into a schema.
This workflow prevents a common overclaim by separating recognition from schema mapping and distinguishing blank, unchecked, not applicable, and unreadable states.
Recognition is only the first stage. Use Form OCR: Checkboxes, Blank Fields and Repeated Labels for the relevant control checks and Numeric OCR Validation for Dates, Totals and Identifiers for values that must be exact. If the output structure changes, compare Invoice and Receipt OCR with Table to Markdown.
Validate labels, values, checkboxes, and absence
| Review point | Likely failure | Validation action |
|---|---|---|
| Repeated labels | Several Name or Date fields collide | Include section, page, and region in each key mapping |
| Checkboxes | No text is mistaken for an unchecked state | Inspect the mark and record an explicit state |
| Blank fields | Blank, unreadable, and not applicable collapse | Use separate null reasons and a review flag |
Define fields before turning text into JSON
Define the destination schema before extracting fields.
Distinguish blank, unchecked, not applicable, and unreadable states.
Validate each key-value pair against its position on the source.
Form mapping scenario
For an intake form with two address sections, do not map both City labels to one generic key. Anchor each value to the applicant or mailing section, keep the raw OCR token, and record whether an empty field was visibly blank or could not be read.
When spatial form extraction needs another system
Use a document-understanding system with coordinate-aware field extraction when forms must be processed at scale. Require human review for signatures, corrections, handwriting, ambiguous marks, or fields that trigger consequential decisions.