Some examples include detecting errors in text that has been recognized using automated text recognition technology. For instance, errors in the recognized text may be detected based on glyph image similarity and the use of a language model, dictionary information, or the like. Some implementations may group together glyphs based on association of the glyphs with the same glyph identifier and a similarity of the appearance of the glyphs. Furthermore, the words associated with each glyph may be checked against a language model, such as to check a spelling or other validity of the words, and a score may be assigned to each group of glyphs based on the validity of the words corresponding to the glyphs in that group. Groups that have a score that fails to meet a threshold may be reviewed by a person or may undergo automated correction techniques.