Choose the language used by most of the image, prepare the text region, and review accented letters, alphabets, numbers, and punctuation before using the result.
Drag an image here, choose a file, or paste an image from the clipboard.
The selected model affects alphabets, diacritics, and likely word patterns. Choose the language used in most of the image rather than the language you want for a later translation.
If one image contains two languages, crop each region and run it with the matching model. This is usually easier to review than one mixed result.
Low resolution, compression, glare, and decorative fonts can change accented characters or similar shapes. Compare the complete result with the image.
Keep names, dates, codes, amounts, URLs, and reference numbers under close review, even when the overall confidence estimate is high.
Picture2Txt currently provides models for English, Portuguese, Romanian, French, Indonesian, Danish, Polish, German, Spanish, and Russian.
Use a sharp image, keep text horizontal, crop unrelated graphics, and avoid shadows or reflections. The image text enhancer can prepare faint or rotated text.
A document may contain an English heading, French paragraph, and product code. Extract each meaningful region with the matching model when accuracy matters.
The tool attempts to reproduce the source text. It does not translate the result. Correct the extracted draft before passing it to a translation service.
Picture2Txt uses Tesseract.js. The browser downloads the selected static model resource. The model does not contain your image or extracted result.
Language selection does not guarantee complete accuracy. Handwriting, mixed scripts, vertical text, decorative fonts, small accents, and compressed screenshots still need careful correction.
These short explanations cover common tasks, limits, and result-checking steps.
Choose the language used by most of the visible text before starting recognition.
The current selector includes English, Portuguese, Romanian, French, Indonesian, Danish, Polish, German, Spanish, and Russian.
The model affects alphabets, accents, character shapes, and common word patterns.
Yes. Select the matching language and verify every accent and punctuation mark against the image.
Yes. Select Russian, use a clear source, and review similar Cyrillic character shapes carefully.
Yes, but separate crops and separate runs often produce a clearer result than one mixed-language pass.
No automatic detection is promised. Select the language that appears in the source image.
Picture2Txt returns source text. Correct it first, then use a separate translation service when needed.
Multilingual optical character recognition uses a selected language model to identify visible characters and likely word patterns in an image.
A language model can still help with surrounding labels and punctuation. Verify every digit and decimal point.
Handwriting results vary widely. Clear printed text is generally more dependable than connected or stylized writing.
The browser may need to download and initialize the selected language resources before extraction begins.
Yes. Run it with one model, switch languages, and compare both drafts with the source image.