A character recognition method, comprising: training parameter values of each calculation layer of a first classifier by using character picture samples, wherein the first classifier comprises first N calculation layers and a last M calculation layers of calculation layers shared by the first classifier and a second classifier; fixing parameters of a first N calculation layers of the second classifier with parameters of the first N calculation layers of the first classifier, and training parameter values of a last L layers of the second classifier by using non-character picture samples and character picture samples; obtaining picture data; performing calculation on the picture data by using the calculation layers shared by the first classifier and the second classifier, to obtain a first result, wherein the first classifier is a classifier for recognizing a specific character in the picture data, and the second classifier is a classifier for recognizing whether the picture data is a character picture; performing calculation by bringing the first result into remaining calculation layers other than the shared calculation layers in the first classifier, to obtain a first probability corresponding to each character; performing calculation by bringing the first result into remaining calculation layers other than the shared calculation layers in the second classifier, to obtain a second probability; calculating, based on the first probability and the second probability, a confidence value that the picture data is recognized as each character; and outputting a character recognition result based on the confidence value. training parameter values of each calculation layer of a first classifier by using character picture samples, wherein the first classifier comprises first N calculation layers and a last M calculation layers of calculation layers shared by the first classifier and a second classifier; fixing parameters of a first N calculation layers of the second classifier with parameters of the first N calculation layers of the first classifier, and training parameter values of a last L layers of the second classifier by using non-character picture samples and character picture samples; obtaining picture data; performing calculation on the picture data by using the calculation layers shared by the first classifier and the second classifier, to obtain a first result, wherein the first classifier is a classifier for recognizing a specific character in the picture data, and the second classifier is a classifier for recognizing whether the picture data is a character picture; performing calculation by bringing the first result into remaining calculation layers other than the shared calculation layers in the first classifier, to obtain a first probability corresponding to each character; performing calculation by bringing the first result into remaining calculation layers other than the shared calculation layers in the second classifier, to obtain a second probability; calculating, based on the first probability and the second probability, a confidence value that the picture data is recognized as each character; and outputting a character recognition result based on the confidence value. <EOS>