OCR Value Codification
Centralized normalization engine that converts raw OCR text into standardized dates, measurements, and domain codes, eliminating scattered parsing logic across the pipeline.
The Business Problem
Raw OCR outputs strings for dates, numbers, units, and codes; downstream databases need normalized, structured values. Many date and measurement formats exist, and OCR typos make parsing fragile.
Ad-hoc parsing scattered across the pipeline led to inconsistent values and frequent parsing failures.
The Technical Solution
I built a normalization engine using regex, rule engines, and fuzzy matching to handle dates (multiple input formats), measurements, currencies, and domain-specific codes, including OCR typo tolerance.
The engine sits after post-processing and before extraction, outputting clean values for storage and analytics.
The Scalability Factor
Single Python service in the pipeline with no external dependencies. Rule sets are version-controlled and extensible for new formats without redeploying other stages.
Business Impact
Clean, structured values for databases and analytics; fewer parsing failures and downstream errors.
Single place for normalization logic; easier to extend for new formats and domains.