OCR PDF

Optical Character Recognition (OCR) converts raster images of text — like scanned pages or photographed documents — into machine-readable character data. At a high level, an OCR engine analyzes pixel patterns to identify character shapes, applies dictionary and language models to resolve ambiguous glyphs, and outputs Unicode text mapped to the original image positions. Modern OCR pipelines use convolutional neural networks trained on millions of document samples to handle varied fonts, languages, and image quality levels.

For PDFs specifically, OCR produces a text layer overlaid on top of the original scanned image. This text-invisible-to-the-eye approach means the document looks identical to the original scan but becomes selectable, copyable, and indexable by search engines and document management systems — an important feature for making archival material discoverable.

Our tool uses Tesseract.js, a WebAssembly port of Google's Tesseract OCR engine — one of the most accurate open-source OCR systems available, supporting 100+ languages. Processing runs inside your browser via WebWorkers so large documents are handled without freezing the UI and without any data leaving your device.

OCR accuracy depends heavily on input image quality. Resolution is the biggest factor — images scanned at 300 DPI or higher produce significantly better recognition rates than lower-resolution scans. Skew (tilted pages) and noise (specks, shadows, coffee stains) reduce accuracy, though Tesseract includes deskewing and denoising preprocessing steps. Font type matters too: clean serif and sans-serif typefaces are recognized more reliably than handwriting, decorative fonts, or heavily stylized text. For optimal results, ensure your scans are high-contrast, properly oriented, and at least 300 DPI before running OCR.