By combining Kognitos and GPT3, Optical Character Recognition is taking a huge leap forward
Watch Kognitos rescue OCR with a plain-English location rule — and save the technique as a learning that never needs to be re-taught.
What's in this video
A 3-minute demonstration of how Kognitos combines traditional OCR with English-defined logic to extract fields that template-based OCR cannot — and how every fix becomes a permanent learning.
The OCR problem this video solves
Most OCR pipelines need a hand-built template per document layout. Damaged, unstructured, or unfamiliar documents break them. A human reading the document would say something like “the customer ID is always the line below the trailer number” — simple logic, but until now hard for automation to use.
How Kognitos teaches OCR new tricks
- Base OCR pass: Kognitos runs traditional OCR first and reports a confidence score for every field, alongside the values it could and could not extract.
- The exception: in this run, the customer ID could not be extracted, so the brain pauses and surfaces the gap.
- Mini playground: a sandboxed test environment where the user types phrases like “grab the line below the document's trailer number” and verifies the result without touching the real system of record.
- Validate and save: once the value comes out correct, the user clicks Teach a Technique and the rule is saved.
- Fallback at scale: every future document from that vendor runs regular OCR first; if the customer ID is still missing, Kognitos applies the saved location-based rule automatically.
- Manage techniques: all learnings for a process live in one place — they can be reviewed, edited, or removed.
Why pair OCR with an LLM brain
OCR alone can read pixels. An LLM alone hallucinates. Kognitos combines them: OCR does the deterministic reading, the LLM-driven brain understands the English logic that fills the gaps, and a learnings library makes the combination repeatable on damaged or variable documents.