Convert PDF to Text
Extract text from any PDF as a plain text file (.txt). Free, no signup.
.pdf · up to 2 GB
What to use PDF to text for
PDF to text: extract text content from any document
Text analysis
Feed NLP tools, sentiment analysis, and text mining with the content of your PDFs.
Indexing and search
Extract text to index it in Elasticsearch, Solr, or internal search engines.
Accessibility
Convert PDFs to text for screen readers, machine translation, or text processing.
Quick copy
Extract all text from a 100-page PDF in seconds without manual selection.
How it works
Three steps, no hassle
Upload your PDF
Drag or select your PDF file. Works with native text PDFs, forms, and digital documents.
Text extraction
The converter extracts all text from the PDF preserving reading order and basic paragraph structure.
Download the TXT file
Download the .txt file with all the text content of the PDF. Ready to copy, edit, index, or process with any application.
FAQ
Got questions?
PDF to plain text (TXT) conversion extracts only the text characters from the document, without preserving any formatting: no bold, italics, font sizes, columns, or tables. The result is pure text in linear order. PDF to Word (DOCX) conversion attempts to reconstruct the complete document structure including visual formatting. Plain text extraction is faster, more accurate in terms of textual content, and produces a much smaller file. It is the ideal option when you only need textual content for analysis, indexing, search, or copying excerpts.
Scanned PDFs contain no real text — they are page images. Extracting text from a scanned PDF requires applying OCR (Optical Character Recognition) first. Without OCR, extraction from a scanned PDF produces an empty TXT file or one with only document metadata. If your PDF was generated digitally (from Word, Excel, a management system, etc.), text extraction is direct and does not require OCR.
Text order in extraction depends on the PDF's internal text flow. In PDFs with multi-column layout, text may appear in the order it is stored internally, which may differ from the visual reading order. For example, in a two-column PDF, text may appear as complete left-column followed by complete right-column, rather than the natural line-by-line reading order. Advanced extractors apply layout analysis to reorder text according to visual flow, but results may vary depending on design complexity.
The most common use cases are: copying large text fragments from a PDF without manual selection; feeding natural language processing (NLP) or text analysis systems with PDF document content; indexing PDF content in internal search engines; performing full-text search on PDF documents; and processing PDF data with scripts or automation tools like Python, R, or ETL tools.
Yes, intentionally. All visual formatting is lost (fonts, sizes, colors, bold, italic), as well as images, charts, tables as structure (tables become text with spacing), and hyperlinks (link text is preserved but the destination URL is not if not visible). For cases where formatting matters, conversion to Word or direct PDF viewing is more appropriate.
Modern extractors generate the TXT file in UTF-8 encoding, which supports all characters from all languages including accented characters, Chinese, Arabic, Cyrillic, and all special symbols. UTF-8 has been the universal text encoding standard since the early 2000s and is compatible with virtually all modern text editors, IDEs, databases, and text processing systems.
Extract text from PDF: technical guide to getting clean textual content from any document
Extracting text from a PDF is the most fundamental operation in PDF document processing, and also the one most frequently performed incorrectly or inefficiently. The PDF format (ISO 32000) stores text as a series of objects in page content streams, where each character has associated page coordinates, a font, a size, and transformation properties. Text extraction consists of reading these objects, identifying the glyphs corresponding to Unicode characters, and ordering them into a readable text stream. The most widely used open-source libraries for this operation are PyMuPDF (Python binding for MuPDF), pdfminer.six (Python, specialized in text extraction and layout analysis), PDFBox (Java, maintained by the Apache Software Foundation since 2008), and the poppler-utils library which includes the pdftotext command-line tool. Extraction quality varies significantly between these tools depending on PDF type.
Professional use cases for PDF text extraction have grown exponentially with the rise of natural language processing (NLP) and generative AI. Legal analytics applications (contract analytics, automated due diligence) process thousands of contracts in PDF, extracting their text for semantic analysis with language models like GPT-4 or LLaMA. Corporate knowledge management systems index company archive PDFs to enable semantic search. Legal e-discovery platforms — which process millions of documents in litigation — depend on PDF text extraction as a basic operation. AI model training pipelines that use PDF documents as data sources (Common Crawl includes millions of PDFs) require text extraction at scale. In all these contexts, extraction accuracy — including correct text order in multi-column documents and correct handling of special characters and typographic ligatures — is critical.
A frequent problem in PDF text extraction is incorrect handling of font encodings. Some PDFs, especially those generated by older software or professional typesetting systems (InDesign, QuarkXPress), use fonts with non-standard character maps where internal character codes do not directly correspond to Unicode codepoints. In these cases, the extractor may produce text with incorrect characters, especially with typographic ligatures (fi, fl, ffi), typographic quotes, and special spacing characters. Modern extractors like pdfminer.six and MuPDF have mechanisms to resolve these non-standard character maps, but not all cases are covered. For PDFs generated from modern software (Word, LibreOffice, web browsers), text extraction is invariably accurate. Convertir.ai uses modern extraction engines that correctly handle font encoding and reading order, producing clean, accurate plain text from most PDFs.