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.

利用字符图像重建网络（CIRN）对纵向中文文本进行识别，通过分离内容和方向信息，提高自然场景中横向和纵向文本的识别性能。

场景图像中的无方向中文文本识别

Orientation-Independent Chinese Text Recognition in Scene Images

Recently, object detection in aerial images has gained much attention in
computer vision. Different from objects in natural images, aerial objects are
often distributed with arbitrary orientation. Therefore, the detector requires
more parameters to encode the orientation information, which are often highly
redundant and inefficient. Moreover, as ordinary CNNs do not explicitly model
the orientation variation, large amounts of rotation augmented data is needed
to train an accurate object detector. In this paper, we propose a
Rotation-equivariant Detector (ReDet) to address these issues, which explicitly
encodes rotation equivariance and rotation invariance. More precisely, we
incorporate rotation-equivariant networks into the detector to extract
rotation-equivariant features, which can accurately predict the orientation and
lead to a huge reduction of model size. Based on the rotation-equivariant
features, we also present Rotation-invariant RoI Align (RiRoI Align), which
adaptively extracts rotation-invariant features from equivariant features
according to the orientation of RoI. Extensive experiments on several
challenging aerial image datasets DOTA-v1.0, DOTA-v1.5 and HRSC2016, show that
our method can achieve state-of-the-art performance on the task of aerial
object detection. Compared with previous best results, our ReDet gains 1.2, 3.5
and 2.6 mAP on DOTA-v1.0, DOTA-v1.5 and HRSC2016 respectively while reducing
the number of parameters by 60\% (313 Mb vs. 121 Mb). The code is available at:
https://github.com/csuhan/ReDet.

本文提出了一种旋转等变探测器（ReDet），它可以准确地预测方向并且具有巨大的模型尺寸减小，同时在 RoI 对齐上具有旋转不变性。在多个具有挑战性的航空影像数据集上，与以前的最佳结果相比，ReDet 的实验结果更好，同时可以降低 60％的参数（313 Mb 与 121 Mb）。