Buyer problem
Hebrew document systems must process right-to-left text, numbers, punctuation, bilingual fields, irregular tables, low-quality scans, and complex layouts. Character accuracy alone does not prove that extracted business data is usable.
Suitable use cases
- OCR accuracy evaluation
- Layout and reading-order extraction
- Invoice and form field extraction
- Table structure recognition
- Document classification and routing
- Regression testing across model versions
Inputs required from the client
- Document types and target fields
- Model outputs or access to the extraction pipeline
- Ground-truth requirements and scoring rules
- Image quality and source constraints
- Permitted data sources and privacy requirements
Method and workflow
A representative pilot set is selected or created. Ground truth is defined, outputs are compared at text, field, table, layout, and document levels, and defects are classified by severity and business impact.
Deliverables
- Document test set and metadata
- Ground-truth fields or annotations
- OCR and extraction defect report
- Error examples by category
- Quantitative accuracy summary
- Remediation and coverage recommendations
Acceptance criteria and quality metrics
Metrics may include character or word accuracy, field precision and recall, table cell accuracy, reading-order accuracy, classification accuracy, malformed-output rate, and critical-field failure rate.
Example output
Review the sample OCR defect analysis.
Data-handling considerations
Document projects frequently contain sensitive fields. The source policy, redaction rules, retention period, and secure transfer method must be agreed before files are exchanged.
Request a Hebrew OCR or document AI pilot
A pilot can evaluate one document family, a representative sample, and the highest-value fields before scaling to broader coverage.