Yes, but that's context specific. If your goal with OCR to make text indexable and searchable with regular text search, then transcribing "lesser" as "lesfer" is bad. And handwriting can often be so bad that you need context to make the call about what the scribbles actually are trying to say.
Evaluation methods, too, are bad because they don't think critically about what the downstream task is. Word Error Rate and Character Error Rate are terrible metrics for most historical HTR, yet they're what people use because of habit.
It's a bit like how for a long time BLEU was the metric for translation quality. BLEU is based on N-gram similarity to a reference translation, so naturally translation methods based on and targeting N-gram similarity (e.g. pre NN Google translate) did well, and looked much better than they actually were.
Evaluation methods, too, are bad because they don't think critically about what the downstream task is. Word Error Rate and Character Error Rate are terrible metrics for most historical HTR, yet they're what people use because of habit.
It's a bit like how for a long time BLEU was the metric for translation quality. BLEU is based on N-gram similarity to a reference translation, so naturally translation methods based on and targeting N-gram similarity (e.g. pre NN Google translate) did well, and looked much better than they actually were.