What is OCR and how does it work?

The problem OCR solves

A computer sees a scanned page as a grid of coloured dots. It has no idea that a cluster of dark pixels is the letter e, so there is nothing to search, select or copy. The same happens with a phone photo of a receipt, a screenshot or a PDF made from scans.

OCR closes that gap. It looks at the pixels, decides which characters they form and returns real text. Once text exists, everything you expect from a digital document becomes possible: search, copy, translation, indexing and conversion to Word or plain text.

How OCR works, step by step

Modern OCR engines follow a similar pipeline. The exact methods differ between engines, but the stages are recognizable.

• Preparation: the image is converted to black and white or grayscale, cleaned of specks and sometimes straightened if the page is slightly skewed.
• Layout analysis: the engine finds blocks of text, lines and words, and works out the reading order.
• Recognition: each line is compared with what the engine has learned about the shapes of the chosen language. Tesseract, the open-source engine used by our OCR tool, uses a neural network for this stage.
• Language checking: the candidate words are checked against language knowledge, which helps choose between similar-looking options.
• Output: the recognized text is returned, either as a text file or as a hidden layer aligned with the original image.

What decides the accuracy

The image matters most. A sharp scan at 300 DPI, straight, with dark text on a light background, can be recognized very well. Blurry photos, shadows, low resolution, faded ink, coloured backgrounds and busy patterns behind the text all lower accuracy.

The second factor is the language setting. The engine needs to know which alphabet to expect. Choosing English for a French document, for example, produces wrong or missing accented letters. The third factor is the typeface: clean printed fonts are easy, decorative fonts and very small print are harder.

Typical mistakes and what they look like

OCR rarely fails completely; it makes small, predictable errors. The digit 0 and the letter O, the digit 1, the lowercase l and the capital I, or the pair rn read as m are the classic ones. Stray marks on the paper turn into odd punctuation.

This is why numbers, codes and names deserve a second look, particularly in invoices, contracts and identification numbers. For anything where one wrong digit matters, compare the text with the original image instead of trusting it blindly.

Handwriting, tables and scripts

OCR works best on printed text. Handwriting is not reliably recognized, and cursive is much harder than neat block capitals. Tables, columns and forms can also confuse the reading order, because the engine sees text blocks rather than the logic of the table.

Scripts matter too. Arabic is joined and written right to left, so it is harder than plain Latin text. In our OCR PDF tool, Arabic and Russian are recognized, but you receive the result as a .txt file rather than as a searchable PDF layer. For Latin-script languages, the tool can produce a searchable PDF as well.

What OCR produces

There are two common outputs. Plain text is just the recognized words in a .txt file, useful when you want to reuse the content. A searchable PDF keeps the original page image and adds an invisible text layer, so the document looks unchanged but can be searched and copied.

OCR PDF can produce either or both, in English, French, Spanish, Arabic, German, Italian, Portuguese, Dutch, Turkish and Russian. It runs in your browser with Tesseract.js, and your documents are not uploaded. The OCR engine and language data are downloaded once from a public CDN the first time you use it, and recognition takes seconds per page.

OCR compared with related tools

OCR is needed only when a page has no text. If your PDF was exported from Word or another program, it already contains text, and PDF to Text or PDF to Word can read it directly. Running OCR on such a file only adds work, which is why OCR PDF can skip pages that already have text.

Think of OCR as the first step for scanned files. Recognize the pages, then choose the format you actually need: a searchable PDF for archiving, a text file for reuse, or an editable Word document for changes.