Convert PDFs & Images to text.
Privately.

Upload your PDF to a local OCR tool like PDF to OCR, choose the document language, and click Extract Text. The file is rendered in your browser with PDF.js and processed by Tesseract.js — no uploads, fully private. Text-based PDFs can often be selected and copied directly, while scanned or image PDFs are converted with OCR.

Open a browser-based OCR tool such as PDF to OCR, drop in a PNG, JPEG, or WEBP image, pick the language, and run extraction. Tesseract.js recognizes the characters directly in your browser, so nothing is uploaded to a server.

Excel cells already hold text or values, so click the cell and copy from the formula bar. If a cell contains a screenshot or picture, OCR it first. To pull a specific substring from a cell, use formulas like LEFT, RIGHT, MID, FIND, TEXTBEFORE, or TEXTSPLIT.

Download the PPT or PPTX (or open it in Google Slides), export the slides to PDF, then run PDF text extraction or OCR on each page. If the slides are image-based, OCR will recover the text.

Use an OCR tool with handwriting support. PDF to OCR uses Tesseract.js, which can read neat handwriting from clear, high-contrast scans. For messy handwriting, a dedicated handwriting OCR model may give better results.

Use a free online OCR tool like PDF to OCR that processes the image locally in your browser — no upload, no account, fully private. Just drag in a PNG, JPEG, or WEBP and extract the text.

Install pytesseract and the Tesseract OCR engine, then: from PIL import Image; import pytesseract; text = pytesseract.image_to_string(Image.open('image.png')); print(text). For batches, loop over files in a folder.

An image PDF is simply scanned pages. Render each page (via PDF.js or pdf2image) and OCR it with Tesseract. PDF to OCR does this automatically in your browser — upload the PDF and click Extract Text.

In OneNote, right-click the picture (image) and choose Copy Text from Picture. OneNote has built-in OCR that extracts the text, which you can then paste anywhere.

If you inserted a picture into Excel, use the Insert Data from Picture feature: right-click the image and choose Extract Text, then review and insert the recognized values into cells.

On macOS, upload the image to a browser OCR tool like PDF to OCR — it works in Safari, Chrome, or Firefox with no install. Preview can copy text from PDFs (Tools > Text Selection) but not from plain images, so use an OCR app or our web tool for photos.

PNG is fully supported by OCR engines. Open PDF to OCR, drop in the PNG, choose a language, and extract. Tesseract reads the pixels directly and returns editable text.

Use Excel text functions to pull specific parts: LEFT, RIGHT, MID with FIND or SEARCH, or the newer TEXTBEFORE, TEXTAFTER, and TEXTSPLIT to split a cell into columns.

For text-based PDFs use pdfplumber or PyPDF2: import pdfplumber; print(pdfplumber.open('file.pdf').pages[0].extract_text()). For scanned PDFs, convert pages to images with pdf2image and OCR them using pytesseract.

Upload your PDF to PDF to OCR and click Extract Text. The tool renders each page with PDF.js and runs Tesseract.js OCR locally in your browser. All extracted text appears in the workspace area for copying, editing, or exporting — no uploads, fully private.

Use a local OCR tool like PDF to OCR. Drop in your PDF, select the document language, and run extraction. The engine pulls readable words from every page — whether the PDF is text-based or a scanned image — and presents them as editable text in your browser.

The best PDF text extractor depends on your needs. For privacy and zero cost, PDF to OCR runs entirely in your browser with no uploads, unlimited pages, and 100+ languages. For enterprise features, Adobe Acrobat Pro offers advanced layout analysis but requires a subscription.

Yes — PDF to OCR is a completely free PDF extractor. There are no file limits, no registration, no watermarks. All processing happens locally via WebAssembly, so you can extract text from unlimited PDFs at no cost.

For quick one-off extraction, use a browser-based tool like PDF to OCR — drag, click, and copy. For automated bulk extraction, use Python with pdfplumber (text PDFs) or pytesseract (scanned PDFs). The best approach depends on file volume, format, and whether you need ongoing automation.

A PDF with images of text is a scanned PDF. Upload it to PDF to OCR — the tool renders each page and applies Tesseract.js OCR to recognize text within the images. The extracted text becomes selectable, searchable, and exportable as TXT or DOC.

PDF to OCR supports image files too — drop in a PNG, JPEG, or WEBP and extract text directly in your browser. For desktop apps, Adobe Acrobat, Microsoft OneNote (right-click > Copy Text from Picture), and Google Keep also offer image-to-text extraction.

Open PDF to OCR, upload your scanned PDF (which is essentially images of pages), choose the language, and click Extract Text. The OCR engine reads the text from each page image and returns editable text you can copy or export as a .txt or .doc file.

The easiest way is to use PDF to OCR: drag your PDF onto the page, click Extract Text, then copy the result or download it as a .txt file. No software installation, no account creation — just two clicks from upload to TXT output.

Use an OCR tool like PDF to OCR. Upload the image (PNG, JPEG, or WEBP), select the language, and run extraction. Tesseract.js reads the pixels and recognizes characters entirely in your browser — nothing is sent to a server.

If the PDF has a text layer (is not scanned), you can click and drag to highlight text directly in most PDF viewers. If the PDF is scanned or image-based, run OCR first using PDF to OCR to generate a selectable text layer, then copy the extracted text from the workspace.

Yes. If the PDF contains native text, simply select and copy (Ctrl+C / Cmd+C) in any PDF viewer. For scanned PDFs, run OCR with PDF to OCR first to extract the text, then copy from the results workspace. The tool also supports one-click copy-to-clipboard.

Use PDF to OCR for quick text and table extraction from any PDF — just upload and extract. For structured data extraction (forms, invoices), consider using Python libraries like pdfplumber, Camelot, or Tabula. Our tool gives you the raw text; specialized tools can parse it into structured fields.