How OCR Works: Extract Text from Scanned PDFs

What is Optical Character Recognition (OCR)?

Optical Character Recognition is a computer vision technology that converts images of typed, handwritten, or printed text into editable, searchable machine data (such as ASCII or Unicode text characters).

Modern OCR engines (such as Google’s open-source Tesseract engine, which powers FileTools in-browser OCR via WebAssembly) do not simply match letters against static font templates. Instead, they use neural network feature extraction and line-finding algorithms to detect word baselines, segment character strokes, and evaluate lexical language models to predict the most probable word matches.

How to Maximize OCR Accuracy: Scan Preparation

The quality of OCR output is overwhelmingly governed by the quality of the input scan. Follow these best practices:

1. Scan at 300 DPI Resolution: Scans captured below 150 DPI lack sufficient pixel definition for algorithms to distinguish similar characters (such as "e", "c", and "o", or "1", "l", and "I"). Scanning above 400 DPI increases processing time significantly without meaningful accuracy gains.

2. Ensure Even Illumination: When photographing documents with a smartphone, avoid casting hard hand shadows across pages. Shadows distort binarization thresholds.

3. Flatten Book Spines & Curvature: When scanning thick textbooks, page curvature near the center spine distorts word baselines. Press pages as flat as possible against the scanner glass.

4. Select the Correct Primary Language: OCR engines rely on linguistic dictionaries to correct minor character ambiguities. If your document is in Spanish or French, selecting that language ensures accented letters (é, à, ñ) are identified.

Current Technical Limitations of In-Browser OCR

While in-browser WebAssembly OCR is remarkably convenient and private, it has realistic technical limitations:

• Cursive & Unstructured Handwriting: While OCR excels at printed typography and computer fonts, handwritten doctor notes, cursive signatures, and informal script often yield low confidence scores.

• Complex Multi-Column Tabular Data: Highly nested financial tables without clear borders can occasionally be merged horizontally during plain text extraction. Review extracted tables carefully.

• Device Memory Constraints: Because OCR runs entirely inside your browser tab without cloud supercomputers, processing 100-page scanned documents simultaneously can take several minutes. For optimal speed, process scanned files in batches of 10 to 25 pages.