PDF OCR
Extract searchable text from scanned PDFs and document images directly in your browser. Powered by client-side WebAssembly OCR engine with multi-language recognition and 100% data privacy.
Drag & drop scanned PDF
Select or drag any scanned PDF file to extract text. Processed locally in memory.
Client-Side Optical Character Recognition for Scanned PDFs
The PDF OCR tool uses a client-side WebAssembly neural optical character recognition engine to extract selectable text from scanned paper documents, flattened forms, and image-based PDF files directly in your browser. It converts static, non-selectable PDF pages into editable plain text transcriptions and structured JSON data without uploading your files to remote servers.
During recognition, each selected PDF page is rasterized onto an offscreen HTML5 Canvas at 150 DPI (2.08x PostScript point scale) and analyzed inside a dedicated browser Web Worker using Long Short-Term Memory (LSTM) neural network character recognition. While language recognition models are fetched over the network when first needed and cached by the browser, all page rendering and pixel analysis execute locally on your device CPU.
The workspace supports 9 document languages (English, Spanish, French, German, Italian, Portuguese, Simplified Chinese, Japanese, and Korean) and lets you process either all pages or a custom page range such as 1-3, 5. After recognition completes, you can inspect the full concatenated transcription or individual page tabs with per-page confidence percentages, copy text to the clipboard, or download `.TXT` and `.JSON` output files.
Core Tool Capabilities
- 150 DPI Canvas Page Rasterization & WebAssembly OCR: Renders each target PDF page onto an HTML5 Canvas at 150 DPI and executes LSTM neural character recognition inside a background Web Worker thread. Running recognition in a worker keeps the browser interface responsive while multi-page documents are transcribed.
- Nine Supported Document Languages: Recognizes printed typography across 9 selectable languages: English, Spanish, French, German, Italian, Portuguese, Chinese (Simplified), Japanese, and Korean. Selecting the matching primary document language before running OCR improves character segmentation and diacritic accuracy.
- All-Pages or Custom Range Selection: Choose between processing All pages in the loaded PDF or switching to Custom Range mode to target specific pages using comma-separated numbers and hyphenated ranges (such as `1-3, 5`). Selective range processing saves time when extracting text from specific chapters or appendices.
- Per-Page Confidence Scoring & Tabbed Review: Computes an individual recognition confidence percentage (0% to 100%) for every processed page alongside an overall document average confidence score and elapsed processing time. You can switch between the Full Document view and individual Page tabs to review and copy transcriptions page by page.
- Plain Text, Clipboard, & Normalized JSON Export: Copy the active view directly to your clipboard, download a plain text file (_ocr.txt) with PAGE N delimiters, or export a structured JSON dataset (_ocr_data.json). The JSON export includes full and per-page text, confidence scores, processing duration, and line-level bounding boxes normalized from 0 to 1 relative to page width and height.
- Local Browser Document Processing: Performs all PDF decoding, 150 DPI canvas rendering, and character recognition inside local browser memory. No PDF buffers, page images, or extracted text transcriptions are transmitted to external OCR servers.
Local Browser Privacy
Your files are processed locally on your device. Files are not uploaded, transmitted, or stored on remote servers.
Once the page has loaded, you can disconnect your network connection and continue using this tool offline.
Target Use Cases
- • Digitizing Scanned Contracts & Legal Discovery
- • Archiving Historical Books & Academic Papers
- • Invoice & Receipt Line-Item Extraction
- • Transcribing Flattened Forms & Certificates
Technical Specifications & Platform Limits
Single and multi-page scanned, flattened, or image-based PDF documents.
Rasterizes PDF pages at 150 DPI and runs neural line and character recognition locally.
English, Spanish, French, German, Italian, Portuguese, Simplified Chinese, Japanese, and Korean.
Includes page confidence scores and normalized (0 to 1) line bounding box coordinates in JSON.
Processes PDF pages locally in browser memory after loading language model assets.
Step-by-Step Workflow
Select a Scanned PDF File
Drag and drop a scanned PDF file into the workspace dropzone, click Choose PDF File to browse your device, or click Try Sample Document. The workspace reads the file locally and displays the active filename, byte size, and total page count.
Choose Document Language & Page Range
Select the primary language of your document from the 9-language dropdown menu in the configuration panel. Choose All pages to transcribe the entire file, or select Custom Range and enter page numbers or ranges such as `1-3, 5`.
Run Optical Character Recognition
Click the Run Optical Character Recognition button to rasterize each target page at 150 DPI and start the local WebAssembly worker. The button displays real-time stage messages and recognition progress percentages as each page is analyzed.
Inspect Confidence & Export Results
Switch between the Full Document tab and individual Page tabs to inspect extracted text and per-page confidence percentages. Click Copy to copy the active transcription, Download .TXT for a plain text file, or JSON to save structured text with normalized line bounding boxes.
Practical Real-World Use Cases
Digitizing Scanned Contracts & Legal Discovery
Legal professionals and paralegals extract selectable text from scanned affidavits, signed agreements, and court exhibits locally in the browser. Using Custom Range mode allows rapid transcription of specific exhibit pages without uploading confidential case files to cloud OCR services.
Archiving Historical Books & Academic Papers
Researchers and archivists convert older image-only PDF journal articles and archival scans into plain text for citation quoting and full-text indexing. Multi-language support handles European and East Asian scholarly texts directly on the workstation.
Invoice & Receipt Line-Item Extraction
Accounting teams and operations analysts run OCR on scanned vendor bills and expense receipts to copy line items and totals into spreadsheets. Exporting the structured JSON file provides normalized line bounding boxes (`x0, y0, x1, y1`) for downstream layout parsing.
Transcribing Flattened Forms & Certificates
Administrators recover readable text from flattened PDF forms or scanned certificates where the selectable text layer was stripped prior to distribution. Per-page confidence percentages immediately highlight any low-contrast pages that require manual verification.
Frequently Asked Questions
- How does client-side WebAssembly PDF OCR work?
- The tool renders each selected PDF page onto an HTML5 Canvas at 150 DPI and passes the pixel buffer to a WebAssembly LSTM optical character recognition engine running inside a browser Web Worker. The worker segments text lines, recognizes character patterns on your local CPU, and returns plain text along with normalized line coordinates.
- Are my scanned PDF documents uploaded to a server for recognition?
- No. Your PDF files, rendered page canvases, and extracted text stay in local browser memory on your device. If a selected language model is not yet cached by your browser, the static language training file is fetched over the network, but zero document content is ever transmitted.
- Which languages are supported by the PDF OCR tool?
- The workspace supports 9 languages selectable from the Document Language menu: English (`eng`), Spanish (`spa`), French (`fra`), German (`deu`), Italian (`ita`), Portuguese (`por`), Simplified Chinese (`chi_sim`), Japanese (`jpn`), and Korean (`kor`).
- What data is included in the exported JSON file?
- The `_ocr_data.json` export contains the full concatenated document text, total processed page count, average confidence percentage, processing duration in milliseconds, and a per-page results array. Each page entry includes its 1-based page number, extracted text, page confidence score, and a recognized lines array with each line's text and normalized bounding box coordinates (`x0`, `y0`, `x1`, `y1` scaled from 0 to 1).
- How can I improve recognition accuracy on blurry or low-contrast scans?
- OCR accuracy depends on scan contrast, page skew, and character legibility at 150 DPI. If a scanned document has dark shadows, skewed pages, or faint ink, process the pages through the PDF Document Scanner with the B&W Document or Magic Color filter first, and if you need to OCR standalone `.jpg` or `.png` files directly, use the Image OCR tool.
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