Many European businesses still process documents manually—invoices, contracts, forms—wasting time and creating errors. AI document processing automates these workflows, extracting structured data from unstructured documents. Modern systems combine optical character recognition, natural language processing, and validation rules for accurate, efficient document digitization.
Document Processing Pipeline
Effective document processing follows a multi-stage pipeline. Document classification determines document type enabling specialized processing. OCR extracts text from images. Named entity recognition identifies key information like dates, amounts, and parties. Validation rules check extracted data against business logic. Human review handles edge cases requiring judgment.
- Classify documents automatically routing them to appropriate processing workflows
- Use specialized OCR models trained on document types you process frequently
- Extract structured data using template-based or machine learning approaches
- Implement confidence scoring routing uncertain extractions to human review
- Validate extracted data against business rules catching errors early
Accuracy Optimization
Document processing accuracy directly impacts business value. Pre-processing improves OCR quality through image enhancement. Domain-specific training on your document types improves extraction. Post-processing corrections handle common OCR errors. Continuous learning from human corrections systematically improves accuracy over time.
Integration and Automation
Extracting data provides limited value without downstream integration. APIs push extracted data into business systems—ERP, CRM, accounting software. Workflow automation routes documents based on content. Exception handling manages documents requiring special processing. End-to-end automation transforms manual document processes into efficient digital workflows.