OCR.space offers online OCR and an API for extracting text from images and documents. Strawberry can compare extracted text with the source file, identify high-risk fields and unreadable regions, organise exception handling, and prepare a document-processing review before OCR output is used in a record, search index, or customer workflow.
01
Treat extracted text as a draft when the field carries consequence.
OCR can make a document searchable quickly, but a misread account number, date, total, name, or clause can change what a team does next. Strawberry can compare OCR.space output with the source image and create a focused review list around the fields that carry financial, legal, or customer impact.
02
Check the document before debugging the text.
Poor scans, rotated pages, handwriting, skewed tables, faint stamps, and missing pages create errors that no downstream parser can explain away. Strawberry can inspect the source batch and group failures by image quality or layout pattern, helping the team fix intake rather than hand-correcting symptoms.
03
Route the exception to the person who can resolve it.
A generic “OCR failed” queue delays work because it does not say whether the source needs re-scanning, a vendor must clarify a number, or an operations owner should correct a record. Strawberry can turn exceptions into an owner-specific queue with the source snippet and proposed resolution path.
04
Review exceptions at the end of each intake day.
A 16:00 pass catches held documents before they silently roll into tomorrow’s backlog, while giving the intake team a full day’s patterns to diagnose. Strawberry can prepare the OCR.space exceptions and corrections without importing or altering any records.
OCR.space Strawberry