What is Text Extraction?
Also called Document Parsing, Content Extraction.
Text extraction is the step that recovers readable text and structure from source formats such as PDFs, word processor files, spreadsheets, slides, web pages, images, and audio. Its output quality sets an upper bound on everything downstream, since a passage never extracted correctly cannot be chunked, embedded, or retrieved correctly.
Formats differ in how much structure they preserve. Word processor and markup formats carry headings, lists, and tables explicitly, so extraction is largely a matter of mapping them. Page description formats such as PDF store positioned glyphs rather than logical structure, so reading order, column layout, and table boundaries must be inferred.
Scanned documents and images contain no text at all and require optical character recognition, which introduces its own error rate on low quality scans, unusual fonts, and handwriting. Audio and video require speech recognition, which adds transcription errors and speaker attribution problems. Both cases benefit from storing a confidence signal alongside the text.
Tables are the recurring difficulty. Flattening a table into a line of text destroys the association between headers and cells, so a question about one figure retrieves an unreadable string of numbers. Preserving tables as markup, or rendering each row as a sentence with its column names, generally retrieves and reads far better.
Extraction should also capture structure alongside content: heading hierarchy, page numbers, section titles, and document metadata. These support better chunking, more useful citations, and filters. Silent extraction failures are common and worth monitoring, since a file that yields a few characters of text will otherwise sit in the index looking healthy.
Key points
- Recovers text and structure from documents, images, and audio.
- Page formats require inferring reading order and columns.
- Scans and audio add recognition error rates.
- Flattened tables lose the header to cell relationship.
- Monitor for files that extract almost no text.
In practice
A quarterly report arrives as a scanned PDF. Optical character recognition recovers the narrative sections accurately but garbles a financial table into a run of unlabeled numbers. The pipeline flags the low confidence table, and a table aware extractor reprocesses that page, emitting each row as a labeled line so the figures become retrievable and readable.