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Extract text from scanned PDFs using optical character recognition. Free online OCR PDF tool converting image-based PDFs into searchable, selectable text with high accuracy. No signup required.
Note: AI can make mistakes, so please double-check it.
Common questions about this tool
OCR (Optical Character Recognition) extracts text from scanned PDFs or image-based PDFs. It converts images of text into searchable, selectable, and editable text that you can copy, search, and edit.
OCR works best with printed text. Handwritten text recognition is less accurate and depends on handwriting clarity. For best results, use clearly printed documents.
OCR accuracy depends on image quality. Clear, high-resolution scans produce better results. Blurry, low-quality, or skewed images may result in recognition errors that need manual correction.
Yes, once OCR extracts text, the PDF becomes searchable. You can use Ctrl+F (or Cmd+F) to search for specific words or phrases throughout the document.
OCR accuracy is typically 95-99% for clear printed text. Accuracy depends on font clarity, image quality, and document complexity. Always review and correct extracted text for important documents.
Verified content & sources
This tool's content and its supporting explanations have been created and reviewed by subject-matter experts. Calculations and logic are based on established research sources.
Scope: interactive tool, explanatory content, and related articles.
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Learn what this tool does, when to use it, and how it fits into your workflow.
This tool turns scanned or image-based PDF pages into selectable, searchable text using optical character recognition (OCR). You upload a PDF, the tool converts each page into a high-resolution image, runs OCR in the browser, and then presents both the page images and the extracted text side by side, and when your workflows start with paper originals you can first create clean input files with a capture step that uses a dedicated tool to turn scanner output into standard PDFs before running OCR on them. It can also send the raw OCR text to an AI refinement service that cleans up typos and formatting.
The problem it solves is straightforward but important: many PDFs are not “real text” documents. They are scans or screenshots that look like text but behave like pictures. You cannot search inside them, copy sentences, or use them in other tools. This makes it hard to reuse information, quote from documents, or make them accessible for screen readers, and when that text ultimately needs to flow into structured spreadsheets you can combine this step with a converter that moves recognized tables and fields into Excel format for further analysis. This OCR PDF tool bridges that gap by extracting machine-readable text from those pages.
The tool is suitable for office staff, researchers, students, legal and finance professionals, and anyone who works with scanned documents. It is designed for users with beginner to intermediate technical skills. The interface guides you through upload, processing, review, and export, while still exposing more detailed elements like word-level confidence and AI refinement for users who want deeper control.
Optical character recognition is the process of reading text from images. When you scan a printed page or take a photo of a document, the result is an image made of pixels, not characters. OCR software looks at these pixels, finds shapes that look like letters, and guesses which characters they represent. Modern OCR engines like Tesseract can handle many fonts and layouts, but results still depend on scan quality and contrast.
PDF files often contain a mix of text and images. Some PDFs are “born digital” and already contain selectable text. Others come from scanners or fax systems and store each page as an image. In such files, the only way to turn the content into usable text is to run OCR on the page images. Once you have text, you can search across a document, copy sections into notes, or feed the content into other systems, and when part of that pipeline requires editable word-processing layouts you can pass either the original or OCR-enriched PDFs through a converter that rebuilds them as Word documents for further revision.
Doing OCR manually is tedious. You might have to export each PDF page as an image, then run an OCR program page by page, and finally gather all the results into one text file. It is also easy to lose track of which page came from where, or to mis-handle large documents. This tool automates the whole flow in a browser: it uses a PDF rendering library to draw each page into a canvas, converts each canvas to an image data URL, and then passes those images to Tesseract.js for recognition, and when you need image outputs instead of text you can take the same PDF inputs and run them through tools that convert pages into PNG images for archival or review while OCR focuses on text extraction.
Once OCR finishes, the tool structures the output into pages and words. Each page includes a base64 image, page-level text, a list of words with bounding boxes, and a confidence value. The tool merges all page texts into a single full-text result for download. On top of that, an AI service can refine the text: it keeps the structure but fixes mistakes and improves readability when needed.
A frequent use case is extracting text from scanned contracts, letters, or invoices that were saved as PDFs. Instead of retyping long passages, you can send the file through the OCR tool, copy out the key clauses, and paste them into your notes or internal systems.
Another common scenario is making archival documents searchable. Historical records and older reports are often only available as scans. Running these through OCR allows you to build searchable text archives, which saves time when you need to find a specific name or phrase later, and for archives that also need picture-based navigation you can generate parallel image sets with converters that turn each PDF page into a PNG snapshot alongside the extracted text. Teachers and students can also benefit. Printed worksheets, old exams, or textbook pages can be scanned and then processed through OCR so they can be imported into note-taking tools, learning management systems, or translation utilities, and when those materials include structured tables it is often useful to follow OCR with a step that maps recognized tabular data into Excel sheets for grading or analysis. Support teams or compliance officers may use OCR when they receive image-based PDFs from clients. They can quickly turn these into text, search for key terms, and feed the content into their existing workflows without manual typing, sometimes pairing the process with a scanning utility that produces consistent PDF inputs from mixed paper sources.
The progress logic splits the workflow into two halves. When converting PDF pages to images, the conversion function reports progress from 0 to 1, which is scaled to cover the first 50% of the overall bar. When running OCR, each page reports its own recognition progress; the tool divides this by the number of pages and adds it to a baseline based on the current page index, mapping everything into the remaining 50% of the bar.
File size limits are enforced with simple comparisons. The PDF loader checks that the number of bytes is greater than zero and less than or equal to a configured maximum in megabytes. Page count checks compare the PDF’s total pages against a fixed cap. These numeric thresholds act as hard safety limits to prevent processing documents that are too large for a browser-based OCR engine.
Confidence scores from the OCR engine are normalized per word and per page. If the engine returns undefined or out-of-range values, they are clamped between 0 and 100. A helper function then classifies these values into three bands: greater than or equal to 85 is treated as high confidence, 60 to under 85 as medium, and below 60 as low. The viewer uses these bands to assign colors and labels.
The text length limit is another protective measure. If the OCR engine produces more than a set number of characters for a page, the tool truncates the result and appends an ellipsis, rather than storing or rendering all text. Similarly, the number of word entries stored per page is capped; extra words are dropped to keep memory use predictable.
For AI refinement, the client validates input length and emptiness before making a backend call. The backend constructs a prompt that asks the model to fix typos, grammar, and formatting while preserving overall structure. The model output is concatenated from response parts, and the backend returns either the refined text or the original text if something goes wrong. The client treats this as a single string and does not perform extra transformations.
| Limit or Scale | Description |
|---|---|
| Maximum PDF file size | Upper bound on input size (for example, 50 MB) to keep OCR processing safe and responsive in the browser. |
| Maximum page count | Cap on the number of pages (for example, 50 pages) that can be processed in a single run to avoid overloading the OCR engine. |
| High confidence (≥ 85%) | Words that the OCR engine is very sure about; usually need little or no correction. |
| Medium confidence (60–84%) | Words that might contain minor errors; worth scanning when accuracy is critical. |
| Low confidence (< 60%) | Words that are likely wrong or incomplete; should be double-checked or corrected manually. |
| AI input length limit (50,000 characters) | Maximum OCR text length that the AI refinement function will accept in a single request. |
For best OCR results, start with the cleanest possible PDF. High-resolution scans with good contrast and minimal skew will give higher confidence and fewer errors. If your original scans are faint or blurry, consider rescanning before running OCR.
Keep an eye on the confidence colors. Use the high-confidence regions as a quick check that the engine is working well, and focus your manual review on low-confidence words, especially in critical sections like totals, names, or legal terms.
Remember that OCR is an approximation, not a perfect transcription. Always proofread important text after extraction, especially when working with legal documents, invoices, or other sensitive information that must be exact.
Use AI refinement when you want cleaner prose or better formatting, but do not treat it as a substitute for human review. The AI is designed to fix obvious errors and smooth out language while keeping structure, not to judge correctness of content.
Be aware of performance limits. Very large or heavily illustrated PDFs may still be too demanding for in-browser OCR. If you frequently hit size or page caps, consider splitting the document into smaller parts before processing or using specialized offline tools where appropriate.
Finally, consider privacy. Although OCR itself runs in the browser using Tesseract.js, AI refinement involves sending text to a backend service. Review your data handling requirements and decide whether AI refinement is suitable for each document you process.
Articles and guides to get more from this tool
You receive a scanned contract as a PDF, but you cannot search for specific clauses, copy text to quote in an email, or edit the document wi…
Read full articleSummary: Extract text from scanned PDFs using optical character recognition. Free online OCR PDF tool converting image-based PDFs into searchable, selectable text with high accuracy. No signup required.