Image to text

Getting accurate text out of an image

Optical character recognition (OCR) turns the pixels of letters into text you can copy, search, and edit. The results can be excellent or frustrating, and the difference usually comes down to the picture you feed it. This guide explains what the OCR step is doing and the handful of factors that decide how accurate it will be. For more background, see what OCR is and how to extract accurate text from an image.

How browser OCR works here

PixTools runs the Tesseract OCR engine compiled to run inside your browser. When you add an image, the engine downloads its runtime and language data the first time, then scans the picture for shapes it recognizes as characters and assembles them into lines of text — all on your device, so the images are not uploaded to a PixTools server. Because the work is local, the first run is slower while the engine loads, and a large batch takes proportionally longer.

What makes recognition accurate — or not

OCR is happiest with clear, high-contrast, upright printed text: dark letters on a light background, in focus, at a reasonable size. Accuracy drops with low resolution, glare or shadows, skewed or rotated pages, tiny fonts, decorative or stylized typefaces, and busy backgrounds behind the words. Handwriting, dense tables, multi-column layouts, and text printed over photos are the hardest cases and often come back incomplete. Choosing the language that matches the text also matters, because the engine uses it to resolve ambiguous characters.

Simple ways to improve the result

A few habits make a real difference: capture or scan straight-on so the text is not tilted, get as close as you can while keeping the words sharp, and make sure the lighting is even with no glare across the page. If only part of an image matters, crop away the rest before running OCR so the engine is not distracted by logos, borders, or background clutter. Oversized scans can be resized down before adding them, and picking the closest language usually lifts accuracy more than any other single change.

Languages, numbers, and tricky characters

OCR leans on the chosen language to decide between characters that look almost identical, so a mismatch is a common cause of odd output: the engine may read a zero as the letter “O,” a one as a lowercase “l,” or accented letters as their plain versions if the wrong language is selected. Pick the language that matches the bulk of the text. Be extra careful with anything where a single wrong character matters — serial numbers, codes, prices, addresses, and quantities on a receipt — and check those by eye against the image. Mixed languages on one page, symbols, and unusual fonts are inherently harder, so treat the result as a strong draft rather than a perfect transcription.

Outputs, batches, and PDFs

You can extract text from one image or a whole batch. Results can be copied straight to the clipboard, downloaded as individual TXT files, bundled into a ZIP, or merged into one combined TXT file when you want everything in a single document. PDF input is read for its first page, so split a long PDF or convert the pages you need first — the Image Converter turns PDF pages into images you can then run through OCR. Whatever the source, always proofread the output, since even a strong scan can misread a stray character.