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I'm trying to create a noising model that accurately reflects how people would noise name data. I was thinking of randomly switching out characters and creating a probability over which character gets switched in based on keyboard closeness and how similar anatomically another character looks to it. For example, "l" has a higher prob of being switched in with "|" and "k" cause "k" is close by on the keyboard and "|" looks like "l", but that requires a lot of hard coding and reward for that seemed low because that's not the only 2 ways people can noise things. I also had the same idea above except use template matching of every character to every other character but itself and that would give it a similarity score then divide that by the sum over all chars to get the probs. Any other suggestions? My goal is the maximize closeness to actual human noising.

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