Overview
anydoc turns mixed office, text, and PDF inputs into GitHub-Flavored Markdown by separating content detection, source-specific semantic resolution, shared-model rendering, and explicit failure handling. The series explains what survives normalization, why PDF and OCR follow different contracts, and how to judge fit against privacy, resource, and corpus needs.
Learning outcomes
- anydoc’s main value is a semantic boundary: frontends resolve source meaning, while one serializer applies a common GFM policy.
- Content-based detection improves identity decisions, but it does not prove that a document is parseable or complete.
- GFM preserves useful structure but cannot reproduce every source layout, merged-cell geometry, list marker, or embedded asset in Markdown.
- Trustworthy adoption depends on explicit OCR, privacy, failure, resource-limit, and corpus-validation policies—not format coverage alone.
Keywords
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PlansFAQ
What problem does anydoc solve?
anydoc converts heterogeneous office, text, and PDF inputs into structured GitHub-Flavored Markdown through explicit detection and format-specific handling.
Does detecting a file format mean anydoc can convert it?
No. Detection selects a parser family; the input can still be encrypted, malformed, incomplete, unsupported, or over a fixed resource limit.
What can be lost when anydoc converts documents to Markdown?
Presentation details that GFM cannot express can be lost, including merged-cell geometry and embedded-image bytes in the text, although some assets remain on the model.
How should a pipeline choose OCR for scanned PDFs?
Use local extraction for text-bearing PDFs; send OCR-required PDFs to hosted OCR only when whole-document transfer and the service dependency are acceptable.