5 Ways to Extract Text from a PDF
1. Copy and paste
Open the PDF in any reader, press Ctrl+A (Cmd+A on Mac) and copy. This works for text-based PDFs and is fine for a page or two.
2. Convert to TXT online
Upload the PDF in the converter above. Text is extracted with Ghostscript (pdftotext as fallback) into a UTF-8 .txt file. Scanned PDFs are read with OCR automatically.
3. Google Docs (for scans)
Upload the PDF to Google Drive, right-click it and choose Open with → Google Docs. Drive runs OCR and opens the text for editing or copying.
4. Adobe Acrobat
Acrobat Pro can export a PDF as plain text from its Export PDF tool, and recognizes text in scans with its OCR (Scan & OCR) tool.
5. Command line and Python
pdftotext -layout input.pdf output.txt keeps the column layout. In Python: from pypdf import PdfReader; text = "\n".join(p.extract_text() for p in PdfReader("input.pdf").pages).
Text Extraction Methods
There are two fundamentally different approaches to getting text out of a PDF, depending on the type of PDF you have:
Direct Extraction (Native PDFs)
Native PDFs — those created from Word, web browsers, or other software — contain embedded text data. The extraction tool reads the text directly from the PDF's internal structure. This is fast, accurate, and preserves the original text exactly as written.
OCR Extraction (Scanned PDFs)
Scanned PDFs contain images of pages, not actual text. Extracting text requires OCR (Optical Character Recognition) to analyze the images and identify characters. OCR is slower, and accuracy depends on scan quality, resolution, and font clarity. If you need more than plain text, learn how to OCR a scanned PDF into an editable Word file.
Quick test: Open your PDF and try to select text with your mouse. If individual words highlight, it is a native PDF (direct extraction). If the entire page selects as one image, it is a scanned PDF (needs OCR).
What Is Preserved (and What Is Lost)
Plain text (.txt) is the simplest document format — just characters and line breaks. When converting PDF to text, you gain universal compatibility but lose visual formatting:
| Preserved | Lost |
|---|---|
| Text content (words, numbers) | Fonts and font sizes |
| Paragraph breaks | Bold, italic, underline styling |
| Basic line structure | Colors and highlighting |
| Page order | Images, charts, and graphics |
| Special characters (UTF-8) | Tables (structure lost, content kept) |
| Numbering (as text) | Headers and footers (mixed inline) |
Handling Multi-Column Layouts
Multi-column documents (academic papers, newspapers, newsletters) present a challenge for text extraction. The extractor must determine the reading order — should it read across both columns or down one column then the next?
Most extractors read content in the correct column order (left column first, then right column). However, elements that span both columns (titles, headers, footnotes) may appear in unexpected positions in the text output.
Tips for column handling:
- Review the output for scrambled reading order, especially at column boundaries.
- Headers spanning multiple columns usually extract correctly at the top of the text.
- Footnotes may appear mid-text rather than at the bottom, since they sit at the bottom of a column.
Tables in Plain Text
Tables lose their visual structure when converted to plain text. Cell content is preserved, but the grid layout disappears. Typical approaches include:
- Space-aligned columns: Cell content is padded with spaces to maintain visual column alignment. Works for simple tables with short cell content.
- Tab-separated: Cells are separated by tab characters, which can be imported into spreadsheet software.
- Sequential text: Cell content is output sequentially, row by row, with minimal structure markers.
For structured table data, consider converting to CSV or XLSX format instead of plain text, as these formats preserve the tabular structure.
Character Encoding
Character encoding determines how text characters are stored as bytes in the output file. The most important encoding options:
- UTF-8: The universal standard. Supports virtually every language and symbol, including Chinese, Arabic, Cyrillic, emoji, and mathematical symbols. This is the recommended encoding for almost all use cases.
- ASCII: Limited to 128 characters (basic English letters, numbers, punctuation). Non-ASCII characters are lost or replaced with question marks. Only use for legacy systems that cannot handle UTF-8.
- Latin-1 (ISO 8859-1): Supports Western European languages. Limited compared to UTF-8 but compatible with some older systems.
Recommendation: Always use UTF-8 encoding unless you have a specific reason not to. It handles every language and is the default for modern text processing tools, programming languages, and databases.
Common Use Cases for Text Extraction
Search indexing: Extract text from PDF archives to make them searchable. Full-text search engines (Elasticsearch, Solr, Lucene) can index the extracted text for fast document retrieval.
Data mining: Extract structured data from reports, invoices, and forms for analysis. Combine with regex patterns or NLP to identify specific data fields (dates, amounts, names).
NLP processing: Feed extracted text into natural language processing pipelines for sentiment analysis, topic modeling, entity extraction, or text classification.
Accessibility: Convert visual PDFs to plain text for screen readers and assistive technologies, making documents accessible to visually impaired users.
Content migration: Extract text from legacy PDF archives when migrating content to new systems, CMS platforms, or databases.
Plagiarism detection: Extract text from submitted documents for comparison against databases and other submissions.
Why You Can’t Copy Text from a PDF
- It is a scan. The page is a picture, so there is no text to select. Run OCR first.
- Copying is restricted. The PDF has a permissions (owner) password that blocks copying. The text is still in the file, and the owner can lift the restriction.
- The text comes out garbled. Some PDFs use fonts without a proper character map, so letters copy as symbols. OCR on the rendered page is the reliable workaround.
- The text is in an image or a form field. Logos, charts and stamped text are images; form fields need the form to be flattened first.