Amey-Thakur Document parsing
Extracting text from documents is deceptively hard. A PDF may be text, images of text, or both, with reading order that does not match visual order. Robust parsing means detecting what you have and routing accordingly rather than applying one extractor to everything.
Method
- Detect the document type properly. By content inspection rather than extension, since the extension is frequently wrong and occasionally hostile (see file-upload-safety).
- Try text extraction before OCR. Embedded text is accurate and cheap; OCR is neither. Falling back only when extraction yields little is the right order.
- Preserve structure, not just characters. Headings, lists, tables, and page boundaries carry meaning that a flat string discards, and downstream chunking depends on it.
- Handle multi-column and mixed layouts explicitly. Naive extraction interleaves columns into nonsense, which is invisible until someone reads the output.
- Treat tables as a separate problem. They rarely survive generic extraction and often need a dedicated approach or a documented limitation.
- Sandbox and resource-limit the parser. Document parsers are a classic exploit surface, and malformed files can consume unbounded memory and time.
- Record extraction confidence. Downstream features should know whether they are working with clean text or noisy OCR output (see full-text-search-design).
Boundaries
Extraction quality is bounded by the source: a poor scan yields poor text however good the pipeline. Complex layouts, handwriting, and equations are unreliable and should be flagged rather than silently mangled. Parsed content inherits the source document's confidentiality (see data-classification).