Images and PDFs supported
OCR stands for Optical Character Recognition — the technology that reads text from images. This converter uses a powerful OCR engine running entirely in your browser. Feed it a photo of a document, a scanned PDF, a screenshot, or even a picture of a whiteboard, and it extracts the text into editable formats like TXT, DOCX, searchable PDF, HTML, JSON, or CSV.
It supports 18 languages out of the box, from English and Spanish to Japanese, Arabic, and Hindi. And because the engine does all the heavy lifting in your browser, your documents never get sent to a cloud service for processing. That alone sets it apart from most OCR tools online.
Tip: For best results, use clear, well-lit images at 300 DPI or higher. Skewed or blurry photos will still work, but accuracy drops noticeably.
Think about what you typically OCR: tax forms, medical records, contracts, ID documents, receipts. These are some of the most sensitive files you handle. Most online OCR services upload your images to their servers, process them remotely, and may retain copies for "quality improvement."
This tool takes a fundamentally different approach. The OCR engine loads the language model data once into your browser, then performs all recognition locally. Your images stay in your browser's memory, the extracted text stays in your browser's memory, and when you close the tab, everything disappears. No server logs, no temporary files on someone else's infrastructure.
With clean, high-resolution images of printed text, accuracy typically exceeds 95%. Factors that reduce accuracy include low resolution, unusual fonts, poor lighting, heavy background noise, and handwritten text. Tesseract performs well with standard documents but is not designed for cursive or artistic handwriting.
On the first run, the engine needs to download the language model data (about 2–4 MB depending on the language). This is cached by your browser, so subsequent conversions in the same session are much faster. The actual text recognition speed depends on image size and complexity.
Yes. The tool renders each page of a PDF as an image, then runs OCR on each page sequentially. For a 10-page document, expect the process to take 30–60 seconds depending on your hardware. The extracted text from all pages is combined in the output.
A searchable PDF embeds the recognized text as an invisible layer behind the original image. Visually, it looks identical to the input, but you can select text, search within it, and copy from it. This is the standard format used by document management systems for archiving scanned documents.
Absolutely. The engine loads a trained model specific to each language, and these models recognize different character sets, letter shapes, and common word patterns. Running English OCR on a German document will miss umlauts and ß, and the word-level confidence drops significantly. If your document mixes languages (say, a French letter with English technical terms), pick the primary language — the engine handles occasional foreign words reasonably well, but optimizing for the dominant language gives the best overall accuracy.
Image quality is everything. 300 DPI minimum, sharp text, clean background, no shadows or glare. Black text on white background works best. Use good lighting if photographing with a phone.
TXT for raw text. DOCX for editable documents. Searchable PDF keeps original images with invisible text layer. HTML preserves layout. JSON for programmatic processing. CSV for tabular data.
The engine is optimized for printed text and performs best with clean, typed documents. Neat handwriting in block letters may produce usable results, but cursive writing or messy notes will likely be unreliable. If you need to digitize handwritten content, start with the clearest possible scan at high resolution. Results vary significantly depending on how consistent and legible the handwriting is.