  Scene text recognition (STR) has attracted much attention due to its broad
applications. The previous works pay more attention to dealing with the
recognition of Latin text images with complex backgrounds by introducing
language models or other auxiliary networks. Different from Latin texts, many
vertical Chinese texts exist in natural scenes, which brings difficulties to
current state-of-the-art STR methods. In this paper, we take the first attempt
to extract orientation-independent visual features by disentangling content and
orientation information of text images, thus recognizing both horizontal and
vertical texts robustly in natural scenes. Specifically, we introduce a
Character Image Reconstruction Network (CIRN) to recover corresponding printed
character images with disentangled content and orientation information. We
conduct experiments on a scene dataset for benchmarking Chinese text
recognition, and the results demonstrate that the proposed method can indeed
improve performance through disentangling content and orientation information.
To further validate the effectiveness of our method, we additionally collect a
Vertical Chinese Text Recognition (VCTR) dataset. The experimental results show
that the proposed method achieves 45.63% improvement on VCTR when introducing
CIRN to the baseline model.
