Upload a PDF, extract the text. Free, no signup required. Powered by the PDFBase API.
Drop a PDF here or click to browse
PDF files up to 10MB
Extraction failed
This tool uses the same API you can call from your code. Extract text from PDFs at scale. One endpoint, structured output, zero infrastructure.
import PDFBase from 'pdfbase'
const client = new PDFBase()
const result = await client.extract.text({
source: { url: 'https://example.com/doc.pdf' }
})
// result.text → extracted text content
// result.pages → number of pages
// result.words → word count
PDF text extraction is the process of pulling the readable text content out of a PDF file programmatically. Most PDFs created digitally — from word processors, web browsers, reporting tools, or applications — contain an embedded text layer alongside the visual rendering. Text extraction reads this layer directly, returning the text as a string you can search, index, or process further in code.
This is fundamentally different from OCR (Optical Character Recognition). OCR applies machine learning to recognize characters in images — it's designed for scanned documents where text exists only as pixels. Text extraction is faster, more accurate, and significantly cheaper because the text data is already there; it just needs to be decoded from the PDF's internal structure. If your PDF was created digitally (exported from Word, generated from HTML, downloaded from a SaaS app), text extraction is what you want. If someone took a photo of a printed page and saved it as PDF, that's where OCR comes in.
The PDFBase text extraction tool above uses the same API that powers our production service. Upload any digital PDF and get the full text content back instantly — no dependencies, no libraries, no server-side setup.
Drag and drop a PDF file onto the upload area, or click to browse your files. The file is read entirely in your browser using the FileReader API and encoded as base64. No file is uploaded until you click "Extract Text."
Click the button to send the base64-encoded PDF to the PDFBase API. The API parses the PDF's internal structure, decodes the text layer from each page, and returns the full text content along with metadata (page count, word count, character count).
The extracted text appears in a read-only text area below. Copy it to your clipboard with one click, or download it as a .txt file. The metadata bar shows page count, word count, and character count at a glance.
PDFs are the lingua franca of business documents — invoices, contracts, reports, filings, specs. But they're opaque to software by default. Extracting the text unlocks a PDF's content for code to work with. Here are the most common reasons developers need text extraction:
Make PDF content searchable. Extract text from uploaded documents and feed it into Elasticsearch, Typesense, or your database's full-text search. Without extraction, PDFs are black boxes to your search engine — users upload documents but can never find them.
Pull structured data out of PDF reports, statements, and filings. Financial data, regulatory filings, and procurement documents often arrive as PDFs. Text extraction is the first step before parsing the content into structured records for your data warehouse.
Moving legacy content into modern systems. Organizations sitting on years of PDF archives need to migrate that content into CMSes, knowledge bases, or document management platforms. Bulk text extraction turns thousands of static PDFs into indexable, editable content.
Feed document content into LLMs, classification models, or summarization pipelines. RAG (Retrieval Augmented Generation) systems need text chunks from source documents. Text extraction turns PDFs into the raw text that embedding models and vector databases consume.
There are several ways to extract text from PDFs in code. Each has different tradeoffs around accuracy, setup complexity, and language support. Here's how the main options compare:
pdf-parse
Node.js
Popular npm package wrapping Mozilla's pdf.js. Simple API, zero native dependencies. Good for basic extraction from well-structured PDFs.
pdfjs-dist
JavaScript
Mozilla's PDF.js library directly. The foundation that pdf-parse wraps. More control, actively maintained, but lower-level API.
Apache Tika
Java / REST
Heavy-duty content extraction framework. Handles PDFs plus dozens of other formats (DOCX, XLSX, etc.). Runs as a Java process or REST server.
PDFBase API
REST API
Managed API — send a PDF, get text back. No library to install, no binary to manage, no server to scale. Works from any language that can make HTTP requests.
Check if your PDF is digital or scanned. Open the PDF and try selecting text with your mouse. If you can highlight individual words, it's a digital PDF with an embedded text layer — text extraction will work perfectly. If selecting text grabs the entire page as an image, it's scanned and you need OCR instead.
Watch for encoding issues. Some PDFs use custom font encodings or CID fonts (common in CJK documents) that map characters non-standardly. If extracted text shows garbled characters, the PDF may use a font subset without proper Unicode mapping. Re-exporting the source document with standard fonts usually fixes this.
Tables won't extract as tables. Text extraction returns a linear text stream — it doesn't preserve the row/column structure of tables. If you need structured table data, use our PDF Table Extractor which returns data in CSV or JSON format with column alignment preserved.
Multi-column layouts may interleave. PDFs with multiple columns (newspapers, academic papers) store text in reading order, which sometimes interleaves columns. The extracted text may alternate between left and right columns within a page. Post-processing or layout-aware extraction handles this better.
Headers and footers repeat per page. Recurring elements like page numbers, headers, and footers will appear in the extracted text for every page. If you're building a pipeline, strip these with regex patterns after extraction. Look for repeated short strings at consistent positions in the text.
/v1/extract/text endpoint and get structured text back. Get started with 100 free API credits, no credit card required.
Extract tables from PDFs as structured CSV or JSON data
ToolsHTML to PDF, Markdown to PDF, URL to PDF, and more developer tools
DocsFull reference for the PDFBase API with code examples in every language
Ready to extract text from PDFs in your code?
Try the API free — 100 credits, no card