Targeted optical character recognition (OCR) for medical terminology

摘要

Embodiments of the present invention provide concepts for correcting optical character recognition (OCR) errors from and OCR scan result by sequentially applying an anagram hash (AH) and Levenshtein-Distance (LD) measurement for concurrent character identity-based (machine code) and character shape-based (OCR-Key) corrections. The OCR-Key classifies characters by shape into one or more disjoint and overlapping classes. Similar shaped-based classes appearing in consecutive characters are appended to a cardinality term, a repetition count of the class. The LD measurement groups OCR-Keys and differentiates on both class and cardinality to arrive at a shape-based mismatch error between competing candidate words from an associated dictionary and a target word from the OCR scan. The shape-based LD measurement errors are then functionally merged with the character identity-based deletion, substitution, and insertion errors to find a minimum error for the set of candidate words, corresponding to the preferred candidate word match to the target word.

主权项

1. A method for correcting optical character recognition (OCR) errors from an OCR scan result, comprising:
registering an OCR machine code from the OCR scan result; mapping the registered OCR machine code to one or more of an OCR-Key according to one or more character shape functions, wherein (a) the mapping comprises at least mapping to a set of character shape classes comprising disjoint classes, overlapping classes, and combinations thereof, and (b) the character shape classes are classified based at least on character cardinality and character orientation; selecting, from a dictionary, a set of candidate words as possible matches to the OCR-Key; calculating a set of errors between the set of candidate words and the OCR machine code and the OCR-Key; and selecting a preferred candidate word from the set of candidate words with the smallest set of errors.