OCR Tool

The OCR tool runs Tesseract.js entirely in the browser to extract plain text from JPG, PNG, GIF, WEBP, and PDF files, with no upload to a server. PDF pages render to images before recognition. A global language applies across a batch, with 13 languages supported and a per-file override for any file that needs a different one. A high-accuracy or fast model choice trades recognition quality against speed, and deskew, denoise, and auto-rotate correct tilted, noisy, or sideways scans.

• Up to 8 files per batch can be added by drag-and-drop, file selection, or pasting from the clipboard.
• Quality is adjustable between fast and high-accuracy models, and optional processing includes deskew and denoise, auto-rotate, and a low-memory mode for large batches.
• Any file that needs a different language can have it set individually before recognition starts.
• Files process one at a time within a batch, with progress reported per file.
• Recognized text copies per file or all at once to consolidate outputs.
• Output downloads per file as a .txt, or as a single .zip with a manifest for the whole batch.
• Settings persist between sessions via local storage, so preferred defaults carry over.

Limits and logic (plain text for reference): Max files per batch = 8; Max file size = 20 MB; Max combined batch size = 80 MB; Max total PDF pages = 60. PDFs are rendered to images with PDF.js, then passed to Tesseract.js for recognition. Confidence shows an approximate quality score (0 to 100). High accuracy mode uses higher-quality language models; fast mode uses lighter models that favor speed. Preserve interword spaces to maintain readable spacing in output.

OCR extraction speeds up study, research, recordkeeping, and content creation. Text extracted from lecture slides, whiteboard photos, invoices, or printed forms moves into notes, spreadsheets, or writing apps. Deskew, denoise, and auto-rotate improve recognition from mobile snapshots and older scans, and batching files into a single ZIP export saves time with consistent, plain-text outputs.

• Study and notes: Convert class handouts, textbook excerpts, and whiteboard photos into searchable notes for exam prep.
• Workplace documents: Capture text from receipts, packing slips, contracts, and reports for bookkeeping and audits.
• Research and archiving: Digitize magazine clippings and historical pamphlets to create a plain text corpus for analysis.
• Content repurposing: Turn scanned articles or print designs into editable text for blogs, newsletters, and accessibility versions.
• Localization prep: Extract source text for translation workflows, then feed it to translation tools or CAT platforms.
• Developer utilities: Generate plain text from screenshots of logs or terminal output to share snippets and error messages.
• Accessibility support: Provide text alternatives for images and scanned PDFs to improve screen reader compatibility.
• Administrative efficiency: Batch scan and convert recurring forms so teams can paste clean text into CRMs or spreadsheets.

A consistent routine, in scanning, file naming, and language selection, improves accuracy over time. High-accuracy mode suits important documents; fast mode suits quick drafts. Plain-text output simplifies QA, version control, and downstream formatting. Re-running a single file with a different language, or with deskew and denoise enabled, can improve results without repeating the entire batch.