What is OCR? Image text extraction still needs layout checks
Explain OCR outputs and limits, distinguish table parsing and vision-language models and verify small text and reading order.
Understand the concept
OCR converts text in images or scans into processable text, possibly with lines and coordinates. It does not automatically reconstruct tables or reading order. Vision-language models can also recognize text through different methods; a multimodal label does not guarantee accuracy. Check numbers, names and columns against originals.
Character recognition differs from document meaning
Microsoft returns text and positions and OpenAI notes small-text and spatial limitations. OCR focuses on text regions while vision models use broader imagery but can generate plausible errors. One digit or missing header can change meaning. Preserve image-text links and avoid treating editable output as a verified original. Handle punctuation and line breaks according to use.
Compare numbers and reading order on the same page
Use a public two-column page and check line ordering and nearby notes. Verify table rows and units, not number presence. Mark blurred or occluded text unreadable rather than guessing. No OCR or upload occurred here. Match crop boundaries and fix compression before claiming equal-condition comparisons.
Keep originals for review after recognition updates
Replay small-text and column cases after tool or format changes and record corrections. Fix meaning-changing errors before summaries or extraction propagate them. Include proofreading and structure work. Start with locatable OCR for clear text and evaluate vision for complex diagrams. Broad readability does not excuse critical amount or name errors.
Checked sources
These sources support the concepts and methods described here. Examples and test scope are labeled separately; check the version of the tools you use.