Data Capture

What is Data Capture?

Data capture is the process of acquiring information from physical or digital documents and converting it into machine-readable, structured data for downstream processing and system integration.

How Data Capture Works

Data capture encompasses the full input-to-output chain, covering every stage from document receipt to structured output.

Document input channels

Data capture begins by receiving documents from multiple channels, including:

• Scanners
• Email
• APIs
• Mobile devices
• Cloud storage

Core processing steps

Once documents are received, the pipeline applies a series of steps to extract and structure information:

• Image enhancement: Prepares documents for accurate recognition by improving quality and clarity.
• Optical character recognition (OCR) and intelligent character recognition (ICR): Converts document content into machine-readable text.
• Structuring and validation: Organizes the extracted information for validation and downstream system integration.

Evolution of Data Capture

Data capture was the dominant term for document digitization and extraction workflows before machine learning transformed the field.

As AI and machine learning were integrated into document processing pipelines, new capabilities emerged alongside basic recognition, including:

• Document classification
• Contextual extraction
• Automated validation

These advances gave rise to broader terms such as intelligent document processing (IDP) and Document AI, which describe this more capable, AI-powered approach to handling documents.