OCR Accuracy: Character and Word Error Rate

OCR output can be scored by aligning it with a reference transcript and counting edit operations. Character error rate divides character substitutions, deletions, and insertions by the number of characters in the reference. Word error rate uses the same edit-distance components at the word level and divides by the number of reference words. These measures provide a reproducible way to compare systems on the same labeled examples, but their meaning depends on how text is normalized and tokenized. For example, a pipeline might lowercase both strings, remove repeated spaces, or ignore punctuation before computing CER. A WER evaluation must define what counts as a word, especially across languages, scripts, contractions, and writing systems without spaces. Different normalization rules can produce different scores even when the OCR output is unchanged. The denominator is the reference length, and insertions can make either rate exceed 1.0. A low overall score can also hide a critical error in one account number or a missed line in a table. Report the reference source, preprocessing, tokenization, scoring implementation, and whether you aggregate per document or globally. Use CER for character-level fidelity and WER for word-level text, then inspect errors for the task. For document extraction, also measure field-level correctness and layout or reading-order quality. Do not compare published OCR scores unless their datasets and normalization rules are comparable. No single metric captures every kind of document error or downstream risk.