Glossary term
Optical Character Recognition
Technology that turns scanned images or image-based PDFs into machine-readable text.
What it is
OCR is the technology that converts scanned or image-based text into machine-readable text, but it does not provide the understanding, evidence, or workflow judgment that modern document review requires.
Key Takeaways
- OCR solves text capture, not document understanding.
- Buyers often over-index on OCR accuracy when the real workflow problem is reviewability and reasoning.
- The strongest platforms combine OCR with layout understanding, extraction, and fast review.
Why it matters
OCR is the foundational layer that makes scanned documents searchable and processable. It matters because many enterprise workflows still begin with paper, screenshots, scanned PDFs, or low-quality image files. But OCR is only the text-capture step. It does not tell you what the text means, which clause matters, whether two documents contradict each other, or whether the extracted field is safe to trust.
How OdysseyGPT uses it
OdysseyGPT uses OCR as an ingestion step, not as the product outcome. The platform captures text from scans and images, then layers layout analysis, extraction, question answering, and cited review on top. That means teams get both the text and the reasoning layer required to use it safely in production workflows.
Evaluation questions
Why is OCR still relevant?
Because many enterprise documents still arrive as scans, photos, or image PDFs, and no downstream AI workflow works well until the text is captured.
Why is OCR not enough on its own?
Because teams still need to understand context, compare passages, verify findings, and route uncertain output for review after the text is recognized.
How does OdysseyGPT use OCR?
OdysseyGPT uses OCR as one part of a broader stack that adds understanding, extraction, and answers with visible evidence on top.