Document Digitization

Document digitization is the broad discipline of transforming documents from formats that humans read (paper, PDFs, scanned images) into formats that software systems can process, search, and analyze. While the term historically referred to scanning paper documents into digital image files, modern digitization encompasses the full pipeline from raw document to structured, actionable data in a database or API response.

A typical digitization pipeline has several stages. Ingestion handles accepting documents in various formats — PDF, DOCX, scanned images — and normalizing them for processing. Text extraction converts the visual or encoded content into plain text, using native text parsing for digital PDFs or OCR for scanned documents. Classification identifies the document type (invoice, contract, medical record) to determine which extraction schema to apply. Extraction pulls specific fields from the text using rules, templates, or AI models. Validation checks extracted values against business rules and confidence thresholds. Finally, export delivers the structured data to downstream systems in formats like CSV, Excel, JSON, or direct API integration.

DocumentIQ covers the full pipeline from ingestion through export. Users upload PDF or Word documents, the system extracts text per page (storing it in document_pages for granular access), users define their extraction schema, and LLMs handle the extraction step. The platform adds layers that pure digitization tools lack: a feedback loop for correcting and improving extractions, document annotations for few-shot learning, a chat assistant for querying across documents, and configurable export formats. The result is not just digitized documents but a structured, searchable, queryable knowledge base built from document content.