The hard ones are things like contracts, leases, and financial documents which 1) don’t have a common format 2) are filled with numbers proper nouns and addresses which it’s really important not to mess up 3) cannot be inferred from context.
Typical OCR pipeline would be to pass the doc through a character-level OCR system then correct errors with a statistical model like an LLM. An LLM can help correct “crodit card” to “credit card” but it cannot correct names or numbers. It’s really bad if it replaces a 7 with a 2.
Typical OCR pipeline would be to pass the doc through a character-level OCR system then correct errors with a statistical model like an LLM. An LLM can help correct “crodit card” to “credit card” but it cannot correct names or numbers. It’s really bad if it replaces a 7 with a 2.