Optical character recognition (OCR)
Also known as: OCR, text recognition
Optical character recognition (OCR) is technology that detects text in images, such as scanned documents, photos, screenshots, and PDFs, and converts it into digital text that can be searched, copied, and edited.
It's used for digitizing paper records, reading receipts in expense apps, translating signs through phone cameras, and making scanned PDFs searchable. While modern OCR, boosted by AI, is very accurate on clear printed text, handwriting, poor scans, and unusual layouts still cause errors.
Optical character recognition (OCR), explained at three levels
At home
OCR is like a reader that looks at a picture of a page and types out the words for you, so your computer can understand them.
At work
OCR turns scanned paper files into searchable documents, saving staff time hunting through records.
At home
On an iPhone, long-press text in a photo to copy it with Live Text. On Android, Google Lens does the same. Both work on receipts, menus, and signs.
At work
Scanners and PDF tools can add a hidden text layer to scanned files, making them searchable. Accuracy improves with 300 DPI scans and clean originals.
At home
OCR can misread numbers, such as a 5 for an S. In my opinion, always double-check amounts and dates before relying on them.
At work
Validate extracted data with confidence scores and human review for critical fields, protect sensitive scanned documents, choose OCR that supports French accents and bilingual documents, and be careful sending confidential scans to cloud AI services.
Real-world examples
At home
Lena photographed a recipe from her grandmother's cookbook. Live Text turned it into text she could copy into her notes app.
At work
Jerome's Saskatoon MSP set up a client's scanner to save searchable PDFs, so staff could find decades of land records by typing a name.
How it compares
| feature | Scanned image | OCR |
|---|---|---|
| Output | Picture of a page | Searchable, editable text |
| Searchable | No | Yes |
| Accessible to screen readers | No | Yes |
| Accuracy | Exact visual copy | Can contain errors |
Scans capture appearance. OCR captures the words.
For analysts
In document digitization projects, I'd define accuracy targets and review steps for critical fields. OCR errors in names or amounts can cause real problems downstream.
History & origin
Early reading machines were developed in the early 20th century, and OCR was used commercially by the 1950s and 1960s for tasks such as reading postal codes and bank cheques. Ray Kurzweil's company introduced the Kurzweil Reading Machine in 1976, which read printed text aloud for blind people. Hewlett-Packard developed the Tesseract OCR engine in the 1980s, and Google released it as open source in 2005. Smartphones made OCR everyday technology through apps such as Google Lens, launched in 2017, and Apple's Live Text in 2021. Multimodal AI models in the 2020s extended OCR to complex documents and handwriting. In my view, OCR is the bridge that keeps paper from becoming a dead end.