OCR solutions on word boxes: PDF to text, table extraction, redaction
Text is rarely the goal. The goal is an archive you can search, a table in a spreadsheet, a name that no longer shows. Every solution here starts from the same result: the text of each page, a box around every word and a confidence, so you know where each word sits and which ones to check. With fast reading (scan-ctc), the same page gives the same result next year. Pick the job that is yours.
- Text from PDFs, scans, photos
- Tables and regions
- Scan stacks
- Redact, translate, chat
- Custom OCR pipeline

Get the text out of PDFs, scans and photos
PDF to text: OCR for scanned and digital PDFs
You learn in one test whether your PDF already carries text, and what to do in each case.
PDF to text →
Image to text: OCR for photos and screenshots
A photo, screenshot or scan becomes text with a confidence for every word, so you check only the doubtful ones.
Image to text →
OCR text cleanup: join lines into paragraphs
You join chopped lines into paragraphs without changing a single word.
Text cleanup →
Table extraction and zonal OCR
Document splitting, sorting and comparison for scan stacks
Split a scan stack into documents
You find the first page of every document in one scanner run, cut the stack there and sort the parts by what they say.
Split and sort →
Compare two scanned documents
You compare two scans word by word, because a fast reading pipeline reads the same page the same way every time.
Comparison →
Redact, translate and chat with scanned documents
Redact scanned documents
You place a black bar exactly where a word sits, and know the status of every page.
Redaction →
Chat with scans: LLM on OCR, forms and translation
You chat with a scanned PDF, fill a form from it or translate it block by block, and every answer points to its place on the page.
Chat, forms, translation →
Custom OCR pipeline for pages that are not typical
Not sure where to start? The OCR demo gallery shows a page being read.