Index
Reading manuscripts varies with handwriting, capture, and form structure. A responsible proposal needs to test the actual documents authorized before estimating how much work can be automated.
How to evaluate this decision
Separate text recognition, field association and information validation. A correctly read number may be in the wrong column. Define which fields are most consequential and require mandatory review. Do not use a confidence calculated by the model as proof of success without comparing it with verified data.
Criteria for comparing proposals
- Sample: include different people, image quality, erasures and incomplete fields.
- Interface: show original excerpt next to the extraction to facilitate correction.
- Target: Prevent unconfirmed fields from triggering processes that require precision.
A scenario to discuss with the supplier
Hypothetical example: two similar digits change a quantity. The tool should allow you to review the image cropping and record the correction before sending the data to the operating system.
What to validate upon delivery
Build a human-checked reference sample and evaluate error by field. Also measure review time, as an extraction that requires checking everything may not reduce effort.
Prepare the conversation about the project
Quantum9 can run a bounded proof and design the assisted review. Bring anonymized forms, critical fields and target system; do not assume perfect reading for any handwriting.
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