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Scene text recognition (STR) has attracted much attention due to its broadapplications. The previous works pay more attention to dealing with therecognition of Latin text images with complex backgrounds by introducinglanguage models or other auxiliary networks. Different from Latin texts, manyvertical Chinese texts exist in natural scenes, which brings difficulties tocurrent state-of-the-art STR methods. In this paper, we take the first attemptto extract orientation-independent visual features by disentangling content andorientation information of text images, thus recognizing both horizontal andvertical texts robustly in natural scenes. Specifically, we introduce aCharacter Image Reconstruction Network (CIRN) to recover corresponding printedcharacter images with disentangled content and orientation information. Weconduct experiments on a scene dataset for benchmarking Chinese textrecognition, and the results demonstrate that the proposed method can indeedimprove performance through disentangling content and orientation information.To further validate the effectiveness of our method, we additionally collect aVertical Chinese Text Recognition (VCTR) dataset. The experimental results showthat the proposed method achieves 45.63% improvement on VCTR when introducingCIRN to the baseline model.

Orientation-Independent Chinese Text Recognition in Scene Images