1
Add a readable scan or image
Choose the supported source file and check that text is sharp, correctly oriented and not hidden by shadows.
Private browser OCR text extractor
How to extract editable text from scans and images
Use optical character recognition to read supported scans, document images and image-based pages, review the recognized text and save an editable text result.
2
Choose the OCR language
Select the language that best matches the document so the recognition model can interpret characters more accurately.
3
Run text recognition
Allow the OCR engine to analyze the supported pages locally in the browser.
4
Review and save the text
Correct names, numbers, punctuation and formatting before copying or downloading the recognized result.
Key features
What you can do with OCR to Text
Practical capabilities for this document workflow.
Editable recognized text
Turn visible characters from supported document images into text you can review and reuse.
Language-aware OCR
Use the available recognition languages to improve character interpretation for supported documents.
Scan-quality review
Check rotation, contrast and clarity before recognition to reduce avoidable OCR errors.
Multi-page processing
Extract text from supported document pages within one browser session.
Manual correction stage
Review uncertain words, tables, columns and handwriting before relying on the result.
Local recognition engine
Run supported OCR work on your device after required engine and language assets load.
Privacy
OCR processing in your browser
The current OCR to Text workflow is designed to analyze source pages on your device. The browser may download OCR engine or language assets, but the selected document does not need to be uploaded to a Fylvexa OCR server.
Browser-first
Fast workflow
No desktop app
Frequently asked questions
OCR to Text FAQ
Select a question to view the answer.
Sharp, upright scans with high contrast, common printed fonts and clear spacing usually produce the most reliable recognized text.
Results for handwriting can be limited and should be reviewed carefully. The workflow is generally more reliable with clearly printed or typed text.
The language model influences which characters and word patterns the engine expects, so the closest available match can reduce recognition errors.
OCR to Text prioritizes recognized text rather than exact visual reconstruction. Columns, tables, spacing and decorative layouts may need manual correction.
Every page must be rendered and analyzed. High-resolution or multi-page sources require more browser memory and processing time.
The current implementation is designed to recognize supported content in your browser. OCR engine and language data may be downloaded when required.
Related tools
Continue your document workflow
Open another Fylvexa tool that works well with this task.