Extract Text from Scanned PDF Free
Extract text from scanned PDF documents using OCR. Adjust preprocessing settings for better accuracy — all processing happens locally in your browser.
📱 Detected: desktop · Max file size: 20 MB
How to Extract Text from a Scanned PDF
Scanned PDFs contain images of text rather than actual selectable text. This makes them difficult to search, edit, or copy from. OCR (Optical Character Recognition) technology solves this by analyzing the visual patterns in each page and converting them into machine-readable text that you can copy, edit, and search.
Step-by-Step Instructions
Step 1: Upload Your Scanned PDF. Drag and drop your scanned PDF file or click to browse. The tool loads and displays thumbnail previews of all pages so you can verify the correct document is selected.
Step 2: Adjust Preprocessing Settings. Enable preprocessing options to improve OCR accuracy. For most scanned documents, enabling Grayscale and Contrast Enhancement produces the best results. If your scan has speckles or noise, enable Noise Removal. For tilted scans, enable Deskew Detection.
Step 3: Select Language and Extract Text. Choose the language of your document from the dropdown menu. The OCR engine supports eight languages including English, Spanish, French, German, Portuguese, Chinese, Arabic, and Hindi. Click "Extract Text" to begin processing.
Step 4: Copy or Download the Extracted Text. Once processing is complete, review the extracted text in combined or per-page view. Check the confidence scores to gauge accuracy. Copy the text to your clipboard or download it as a .txt file.
Tips for Better OCR Accuracy
OCR accuracy depends heavily on the quality of the original scan. Higher resolution scans produce better results. For low-quality scans, experiment with different preprocessing combinations. Enabling both Grayscale and Contrast Enhancement at 150% intensity is a good starting point. For documents with colored backgrounds, try enabling Binarize with a threshold around 128 to convert the image to pure black and white before recognition.