In-Browser PDF OCR
Recognise and extract editable text from scanned PDF pages and documents locally. No file uploads, no cloud APIs, and complete data confidentiality.
Initialising OCR Engine...
Loading WebAssembly recognition neural network...
Inspect Rendered Page Canvases ▼
Scanned Books & Paper
Convert non-selectable photocopies, printed book scans, and letters into fully searchable and editable text.
Invoices & Receipts
Pull billing dates, vendor details, and amounts from PDF receipts into spreadsheets or accounting software.
Instant Copy & Export
Copy recognised text directly to your clipboard or download structured .TXT or .JSON files with confidence scores.
100% In-Browser
Powered by client-side WebAssembly neural models. Sensitive legal contracts and bank statements never leave your device.
⚡ How to Extract Text from PDF Documents
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1
Select Language
Choose the document's primary language (defaults to English) from the selector dropdown.
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2
Load PDF File
Drop your PDF into the upload zone or click to browse your files.
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3
Neural Text Recognition
The in-browser AI worker scans each page canvas and recognises characters sequentially.
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4
Copy or Download
Review results, check accuracy confidence, copy text with one click, or download .TXT.
💡 Practical OCR Tips
Pages are automatically rendered at double resolution to enhance subtle letterforms and tiny footnote text.
Select the correct language model (e.g. French, German, Spanish) to ensure accents, umlauts, and special characters are recognised correctly.
For multi-page documents, extracted text streams live as each page completes so you don't have to wait for the whole file to finish.
Under the Hood: WebAssembly Tesseract LSTM Neural Pipeline Show technical specifications ↓
Two-Stage Client-Side Recognition Architecture
The optical character recognition pipeline operates in two synchronised stages: PDF viewports are first parsed into raw pixel buffers via HTML5 Canvas using pdf.js at an upscaled factor of 2.0x to maximise character edge contrast. The canvas pixel data is then transferred into a multi-threaded Web Worker running Tesseract's compiled WebAssembly neural engine with LSTM character models.
| Parameter | Specification | Recommended Usage |
|---|---|---|
| Supported Input | Standard & Scanned PDF (.pdf) | Multi-page documents up to 50 pages |
| Output Formats | Plain Text (.txt), JSON (.json) | Document indexing, data entry, copy-pasting |
| Language Models | ENG, FRA, DEU, SPA, ITA, NLD, POR | Latin script scanned materials |
| Processing Rate | ~1.2 to 2.8 sec / page | Varies by client CPU and page layout complexity |
Frequently Asked Questions
Are my confidential documents uploaded to any server?
No. The entire process runs 100% in your local browser runtime. No PDF pages, rendered canvas frames, or extracted strings ever leave your computer.
How can I improve character recognition accuracy?
Ensure the source PDF has good contrast and lighting. Our engine automatically applies a 2.0x resolution upscale during rasterisation to maximise recognition fidelity.
Can I process password-protected PDFs?
Encrypted files must first be unlocked before OCR processing. You can remove restrictions using our client-side PDF Protect utility prior to running character recognition.