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Pages
- Home
- Changelog
- Formats
- Every file type txtfetch parses, checked against the build we run
- What comes back: text, Markdown, or JSON
- Playground
- How it works
- Docs
- Pricing
- Blog
- Benchmarks
- See the extracted text, side by side
- Compare
- Solutions
- Integrations
- Extract by format
- Tools
- Extract by programming language
- Document languages
- Security
- Compliance
- Status
- Contact
- This site as plain text
- Search the whole site
Tools
- Convert any file to text, automatically
- Free OCR-readiness checker for scans and photos
- Free PDF text-layer checker
- Free in-browser PDF-to-text converter
- Chunk previewer
- Clean up extracted text
- Free document-to-text converter
- Free spreadsheet-to-text converter
- Free slide-deck-to-text converter
- Free legacy-document-to-text converter
- Free legacy-spreadsheet-to-text converter
- Free legacy-slide-deck-to-text converter
- Free message-to-text converter
- Free web-page-to-text converter
- Free subtitle & caption-to-transcript converter
- Free ebook-to-text converter
- Free OpenDocument-text-to-text converter
- Free OpenDocument-spreadsheet-to-text converter
- Free OpenDocument-presentation-to-text converter
Docs
- Quickstarts
- RAG recipe
- Async jobs & webhooks
- Idempotency
- Re-indexing changed documents
- Quality checks
- Personal data
- Untrusted documents
- Handwriting and txtfetch
- Equations and math in txtfetch
- Fillable forms in txtfetch
- Barcodes and QR codes in txtfetch
- Text in pictures and scans in txtfetch
- Links and URLs in txtfetch
- Attachments and archives in txtfetch
- Headers, footers, footnotes and comments in txtfetch
- Do tables come back from txtfetch?
- Password-protected files in txtfetch
- Do tracked changes come back from txtfetch?
- Page numbers and citations in txtfetch
- Leaving txtfetch
- Where your file goes
- Error reference
- Limits and quotas
Formats
- Extract text from any PDF, one request.
- Every Office format, one endpoint.
- Email body and attachments, one call.
- Any web page, fetched and reduced to clean text.
- OCR that's just another curl request.
- Ebooks in, chapter text out.
- One ZIP, every file's text back.
- Scanned PDFs, OCR'd without a flag.
- Slide text and speaker notes, together.
- Spreadsheets, resolved not raw.
- CSV, decoded from the bytes.
- The 97-2003 binary formats, read properly.
- OpenDocument, zipped or flat.
- Outlook .msg isn't email, structurally.
- RTF, parsed properly, not regex-stripped.
- Every page of the TIFF, not just the first.
- Markup stripped, order preserved.
- Where the text in a video or audio file actually lives.
Solutions
Integrations
Compare
- txtfetch vs Unstructured.io
- txtfetch vs LlamaParse
- txtfetch vs AWS Textract
- txtfetch vs Azure AI Document Intelligence
- txtfetch vs Mindee
- Why per-page pricing punishes long documents
- Why not just run Apache Tika myself?
- Why not just send the file to the model?
- Open-source document parsers, compared to txtfetch
- txtfetch vs Docling
- txtfetch vs MarkItDown
- txtfetch vs PyMuPDF4LLM
- txtfetch vs Marker
- txtfetch vs MinerU
Ingest
- Extract text into a vector store or search index
- Extract text into pgvector
- Extract text into Pinecone
- Extract text into Qdrant
- Extract text into Chroma
- Extract text into Weaviate
- Extract text into Elasticsearch
- Extract text into OpenSearch
- Extract text into Meilisearch
- Extract text into Typesense
- Extract text into Postgres full-text search
Sources
Migrate
Code languages
Blog
- Ingesting large documents and big batches without falling over
- Chunking strategies for RAG: from clean text to good retrieval
- Extracting tables from PDFs and spreadsheets for RAG
- How to extract text from a PDF for RAG (without maintaining a parser)
- Using txtfetch as a LangChain and LlamaIndex document loader
- OCR scanned documents and images through one API call
- Parsing DOCX, PPTX, and XLSX into clean text for LLM ingestion
- Per-document vs per-page pricing for document extraction
- Getting text out of video and audio: extraction vs. transcription
Fixes
- Fixes for broken extracted text
- Why did my PDF come back with no text at all?
- My PDF renders fine but extracts as gibberish.
- My extracted text is full of ’ and “.
- Two words fused together with no space, over and over.
- My extracted text still has inter-\nnational split across a line break.
- My extracted text has fi and fl instead of plain letters.
- My two-column PDF extracted with both columns mixed together.
- My PDF table extracted as a wall of misaligned text.
- The same header shows up at the top of every single chunk.
- Every line in my extracted text ends at the same width, mid-sentence.
- My extracted text is one giant block with no paragraph breaks anywhere.
- The text is definitely in the file. It's just not in extracted_text.
Glossary
- Glossary: extraction terms defined
- Glossary: PDF text layer
- Glossary: Reading order
- Glossary: OCR (optical character recognition)
- Glossary: Searchable PDF
- Glossary: Font subsetting
- Glossary: DPI (dots per inch)
- Glossary: Character encoding
- Glossary: Mojibake
- Glossary: Byte order mark (BOM)
- Glossary: Ligature
- Glossary: Replacement character
- Glossary: Media type (MIME type)
- Glossary: Magic bytes
- Glossary: OOXML (Office Open XML)
- Glossary: Document metadata
- Glossary: Caption track
- Glossary: Chunking
- Glossary: Token
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