What is Document Processing?
Document processing is the pipeline that ingests, cleans, and extracts information from documents (PDFs, images, office files, HTML). It prepares content for indexing, analytics, or workflows.
How It Works (quick)
- Ingest: Watch folders, APIs, email, scanners.
- OCR & parsing: Recognize text from scans; parse PDFs/HTML; detect tables and headings.
- Normalization: Fix encodings, languages, diacritics; remove boilerplate; deduplicate.
- Extraction: Pull entities/fields (order #, SKU, price, dates), topics, and keyphrases.
- Enrichment: Classify type, set permissions, add taxonomy tags.
- Output: Write structured JSON plus the cleaned text for indexing.
Why It Matters in E-commerce
- Searchable manuals & policies: Customers and agents find answers quickly.
- Catalog ops: Extract specs from vendor PDFs into attributes.
- Compliance: Redact PII; apply retention and access rules.
- Automation: Route documents to the right teams and systems.
Best Practices
- Use high-quality OCR; validate with confidence thresholds.
- Keep a controlled vocabulary and mapping for attributes.
- Preserve layout when tables matter; export both text and structure.
- Log provenance; version pipelines; keep replayable runs.
- QA with spot checks and golden sets; monitor extraction accuracy.
Challenges
- Low-quality scans, complex tables, mixed languages, and inconsistent vendor formats.
Examples
- Auto-extract specs from supplier PDFs to populate PIM attributes.
- Index returns policy PDFs with headings and anchors for instant answers.
Summary
Document processing converts messy files into clean, structured, permissioned data. With OCR, parsing, and enrichment, your documents become searchable and automatable.
