CER
The most commonly used accuracy metric in OCR and speech recognition, computed by dividing the total number of substituted, deleted, and inserted erroneous characters between the model output and the reference answer by the total number of reference characters. A lower value indicates more accurate transcription.
Timeline (last 90 days)
CER = (S + D + I) / N, where S is substitutions, D is deletions, I is insertions, and N is the total number of characters in the reference answer.
The main scoring metric is the paired Nastaliq CER minus Naskh CER, where a positive value means the same source string produced more character errors in Nastaliq.
Traditional document parsing evaluations commonly use CER (Character Error Rate) or BLEU scores, metrics that are statistically reasonable but difficult to map to business-relevant questions.
Academia typically measures OCR quality using Character Error Rate (CER) and Word Error Rate (WER), and these metrics have already approached human-level performance on standard printed-text datasets.
OCR accuracy is typically measured using two metrics: Character Error Rate (CER) and Word Error Rate (WER)
Character Error Rate (CER) uses the same calculation logic as WER but operates at the character level, making it more suitable for languages such as Chinese and Japanese that lack clear word boundaries.
Commonly used evaluation metrics in the OCR field include character-level accuracy, word-level accuracy, and Character Error Rate (CER) based on edit distance.
All Facts (7)
CER = (S + D + I) / N, where S is substitutions, D is deletions, I is insertions, and N is the total number of characters in the reference answer.
50%UnverifiedThe main scoring metric is the paired Nastaliq CER minus Naskh CER, where a positive value means the same source string produced more character errors in Nastaliq.
50%UnverifiedTraditional document parsing evaluations commonly use CER (Character Error Rate) or BLEU scores, metrics that are statistically reasonable but difficult to map to business-relevant questions.
50%UnverifiedAcademia typically measures OCR quality using Character Error Rate (CER) and Word Error Rate (WER), and these metrics have already approached human-level performance on standard printed-text datasets.
50%UnverifiedOCR accuracy is typically measured using two metrics: Character Error Rate (CER) and Word Error Rate (WER)
50%UnverifiedCharacter Error Rate (CER) uses the same calculation logic as WER but operates at the character level, making it more suitable for languages such as Chinese and Japanese that lack clear word boundaries.
50%UnverifiedCommonly used evaluation metrics in the OCR field include character-level accuracy, word-level accuracy, and Character Error Rate (CER) based on edit distance.
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