What Is OCR and Why Does It Matter?
OCR (Optical Character Recognition) is the process of recognising and converting text in images into machine-readable characters. When you scan a physical document with a scanner, flatbed printer or smartphone camera, the result is a raster image -- a grid of pixels that looks like text to the human eye but contains no actual character codes that a computer can read, search or edit. OCR analyses the pixel patterns and identifies which characters they represent, producing a text string that can be stored, searched, copied and processed computationally.
PDF documents fall into two fundamental categories that determine whether OCR is needed. Text-based PDFs (created digitally by exporting from Word, LaTeX, accounting software or any application that generates PDF directly) contain actual text objects with Unicode character codes -- you can select, copy and search the text natively without OCR. Image-based PDFs (created by scanning physical documents, or by combining images into a PDF container) contain page images with no underlying text objects -- they appear visually identical to text-based PDFs but are actually photographs of text pages. OCR is required to extract text from image-based PDFs.
"A scanned PDF is a photograph of text. OCR is the process of teaching a computer to read that photograph -- a task that modern neural networks perform with remarkable accuracy on standard printed documents."
When to Use PDF OCR
Scanned Physical Documents
Physical documents that exist only on paper -- historical records, old contracts, handwritten notes, paper invoices, physical forms, printed reports -- must be scanned to create a digital copy. The scanned result is an image-based PDF with no searchable text. OCR converts these image PDFs into searchable, editable text documents, enabling full-text search, content analysis, database integration and programmatic processing.
Locked or Secured PDFs
Some PDFs have text content but restrict text extraction through PDF security settings (content restrictions, DRM, or encryption flags). When these restrictions prevent copying or selecting text, rendering the PDF pages to images and running OCR on those images provides an alternative path to accessing the text content for legitimate purposes such as accessibility, translation and archival.
Legacy Document Digitisation
Organisations digitising large archives of paper records -- legal files, patient records, HR documents, engineering drawings, government correspondence -- use OCR as the critical step that transforms scanned image PDFs into searchable, indexable content that can be integrated with document management systems (SharePoint, Documentum, OpenText), eDiscovery platforms and enterprise search systems.
Academic and Research Text Extraction
Researchers conducting systematic literature reviews, historians analysing archival documents, linguists building text corpora and data scientists creating training datasets frequently need to extract text from large volumes of scanned PDFs. Running OCR on these documents -- academic papers scanned from print journals, historical newspapers, legal case files, government reports -- produces the machine-readable text required for quantitative analysis, natural language processing and computational research methods.
Understanding Tesseract OCR
Tesseract is the world's most deployed open-source OCR engine, with a history spanning four decades. It was originally developed at HP Labs Bristol between 1984 and 1994, commercialised briefly, then released as open-source software by HP in 2005. Google took over maintenance in 2006 and has significantly enhanced it since then, adding LSTM (Long Short-Term Memory) neural network-based recognition in version 4 (2018) and version 5 (2021), dramatically improving accuracy on challenging documents.
Tesseract.js (used by our converter) is a pure JavaScript/WebAssembly port of the Tesseract 5 engine. It compiles the entire Tesseract C++ codebase to WebAssembly, allowing the same OCR model that powers enterprise document processing pipelines to run directly in your browser without any server-side processing. The accuracy of Tesseract.js on clean, well-scanned documents in major languages (English, Western European languages, Chinese, Japanese, Korean, Arabic) typically exceeds 98%.
Factors That Affect OCR Accuracy
Several factors influence how accurately Tesseract can recognise text from your scanned PDF:
- Scan resolution: The most important factor. Documents scanned at 300 DPI or higher produce significantly better OCR results than 150 DPI scans. Our 3x render scale (approximately 216 DPI from the canvas) produces the best accuracy for challenging documents.
- Image quality: Clean, high-contrast scans with sharp text recognition dramatically outperform faded, low-contrast or blurry scans. Scanning in greyscale rather than colour can improve recognition of light text on patterned backgrounds.
- Font type: Printed serif and sans-serif fonts are recognised with very high accuracy. Handwriting, decorative display fonts and scripts used in historical documents produce lower accuracy and may require manual correction after OCR.
- Page orientation: Tesseract requires pages to be correctly oriented (text reading left to right for Latin scripts, right to left for Arabic/Hebrew). Rotated scans -- a common problem with physical scanning -- significantly reduce accuracy. Deskewing the scan before OCR processing improves results.
- Language selection: Selecting the correct language for your document is critical. Tesseract uses language-specific character sets, linguistic models and word frequency tables to improve recognition accuracy. Using "English" for a French document will produce recognisable but lower-accuracy results.
Tips for Best OCR Results
- Use 3x scale for difficult documents: Faded ink, small fonts (below 10pt in the original scan), handwriting and low-quality scans all benefit significantly from maximum render resolution. The extra processing time is worth the accuracy improvement.
- Select the correct language: Each language model downloads from the Tesseract project CDN on first use (typically 3 to 30 MB depending on the language). English is the default and smallest download. Chinese, Japanese and Korean language models are larger (30+ MB) and take longer to download on first use.
- Use custom range for large documents: OCR is computationally intensive -- processing a 200-page document at 3x scale can take 20 to 40 minutes on a typical laptop. Extract only the pages you need using the custom range option to save significant time.
- Always proof-read the output: Even at 98% character accuracy, a 500-word page may contain 10 or more recognition errors. Common OCR errors include confusing lowercase l and the number 1, confusing O and 0, and missing spaces between words. The extracted text typically needs light editing before use in professional contexts.