What Is Data Extraction? A Technical Primer

From OCR to Intelligent Extraction

Data extraction has evolved dramatically. Traditional OCR (Optical Character Recognition) converts images of text into machine-readable characters. But modern data extraction goes far beyond character recognition — it understands document structure, identifies fields, extracts tables, and validates relationships.

Three Generations of Extraction Technology

Generation 1: Template-Based (Zonal OCR)

The oldest approach. You define zones on a document template — "the invoice number is always in this rectangle." Works perfectly on identical formats, breaks completely on variations. Tools like Docparser still use this approach.

Generation 2: Trainable Models

You feed examples to a machine learning model, and it learns to extract from similar documents. Better than templates at handling variation, but requires training data and time. Nanonets and ABBYY Vantage use this approach.

Generation 3: Template-Free AI

The latest generation uses large language models that understand documents like humans do — reading context, not just coordinates. No training, no templates, works on new formats immediately. Lido and Google Document AI represent this approach.

What Gets Extracted?

Modern platforms extract structured data from unstructured documents:

• Key-value pairs — Invoice number, date, total, vendor name
• Tables — Line items, transaction histories, schedules
• Entities — Names, addresses, account numbers
• Relationships — Which line items belong to which PO, which payments match which invoices

Accuracy Expectations

For digital-native PDFs, top platforms achieve 95-99% field-level accuracy. For scanned documents, expect 88-95%. For degraded scans or handwritten documents, accuracy drops to 75-90%.

Template-free AI platforms like Lido consistently score highest in our benchmarks because they combine OCR with contextual understanding — they don't just read text, they understand what it means.

The Bottom Line

If you're evaluating data extraction in 2026, start with template-free AI. Only fall back to trainable models if you have very specialized documents that require domain-specific training. Avoid template-based tools unless your documents never change format.