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.