Conductor vs Traditional OCR
Traditional OCR extracts text using templates and coordinate-based zones. AI document intelligence understands meaning and context. This guide explains when each approach makes sense.
How each approach works
Understanding the fundamental differences helps explain why these technologies behave differently in practice.
Traditional OCR
Template-based extraction using coordinate zones
- 1Image processing
Document scanned or converted to image format for analysis
- 2Character recognition
Pattern matching identifies individual characters and words
- 3Template matching
Document matched against known templates to determine type
- 4Zone extraction
Data extracted from predefined coordinate regions
- 5Output mapping
Extracted text mapped to predefined output fields
AI Document Intelligence
Semantic understanding of document content and structure
- 1Document parsing
Multi-format parsing extracts text, tables, and structure
- 2Semantic indexing
Content understood and indexed by meaning, not position
- 3Query or extract
Ask questions or request specific information in natural language
Key difference: No templates needed. The system understands what information means, not just where it appears.
Technical differences
These fundamental architectural differences affect how each system handles real-world documents.
Recognition approach
Pattern matching against known character shapes, using coordinates to locate data
Language model understanding of text meaning and document structure
Document model
Document as an image with text regions to be identified and extracted
Document as structured information to be understood and queried
Error handling
Confidence scores on character recognition; manual review for low-confidence extractions
Semantic validation; can explain reasoning and cite sources
Output format
Predefined fields based on template configuration
Flexible output; answers to questions, structured data, or summaries
Detailed comparison
A side-by-side look at how each approach handles common document processing requirements.
When to use each approach
Both technologies have valid use cases. The right choice depends on your specific requirements, document types, and operational constraints.
Traditional OCR works well when:
High-volume identical documents
When processing millions of documents with identical layouts (e.g., a single form type), template-based OCR can be highly efficient once configured.
Regulatory requirements
Some regulated industries require specific extraction methods or audit trails that template-based systems provide.
Existing infrastructure
Organisations with significant investment in OCR infrastructure may benefit from incremental improvements rather than replacement.
Extremely simple documents
Single-field extraction from standardised forms (e.g., scanning barcodes or single data points) may not need AI capabilities.
Conductor works well when:
Document variety
When dealing with diverse document types, formats, and layouts that would require extensive template libraries.
Need for understanding
When you need to ask questions, find information, or understand context rather than just extract predefined fields.
Changing documents
When source documents frequently change format, making template maintenance burdensome.
Limited technical resources
When you lack dedicated resources for ongoing template development and maintenance.
Time-sensitive deployment
When you need to process documents quickly without weeks of template configuration.
Complex extraction needs
When you need to extract from tables, understand relationships, or handle semi-structured content.
Migration considerations
If you are considering moving from traditional OCR to AI document intelligence, here are key factors to evaluate.
Parallel operation
Most organisations run both systems in parallel during transition, comparing outputs and building confidence before full migration. This allows validation without disrupting existing workflows.
Template investment
Existing OCR templates represent significant configuration effort. With AI document intelligence, this configuration is not needed, but the transition means leaving that investment behind.
Process changes
Moving to AI document intelligence often enables new capabilities (querying, cross-document analysis) that may require workflow adjustments to fully utilise.
Compliance review
Regulated industries should review whether AI-based extraction meets their specific compliance requirements, including audit trails and explainability needs.
See the difference with your documents
The best way to understand how AI document intelligence compares to your current OCR setup is to see it handle your actual documents. We can run a comparison using your real use cases.