Inspired by Wang et al. (2019), we propose a method that identifies characters likely to be incorrect: Let x1 and x2 denote the logit scores of the top 2 candidates output by the OCR model. Now we set a threshold t for the difference between x1 and x2. Any OCR prediction where x1 − x2 &lt; t is treated as likely to be incorrect and is passed on to the correction step. This step works by having a pre-trained BERT model re-predict the character from the top k OCR candidates. Systematically testing for different combinations of t and k (with t <hi xmlns="http://www.tei-c.org/ns/1.0" rend="math">∈</hi> [0,0.5, . . . ,10] and k <hi xmlns="http://www.tei-c.org/ns/1.0" rend="math">∈</hi> [0,1, . . . ,18]), we settle with t = 2.5 and k = 7, where we attain the following final results: