  The existing Optical Character Recognition (OCR) systems are capable of
recognizing images with horizontal texts. However, when the rotation of the
texts increases, it becomes harder to recognizing these texts. The performance
of the OCR systems decreases. Thus predicting the rotations of the texts and
correcting the images are important. Previous work mainly uses traditional
Computer Vision methods like Hough Transform and Deep Learning methods like
Convolutional Neural Network. However, all of these methods are prone to
background noises commonly existing in general images with texts. To tackle
this problem, in this work, we introduce a new masked bounding-box selection
method, that incorporating the bounding box information into the system. By
training a ResNet predictor to focus on the bounding box as the region of
interest (ROI), the predictor learns to overlook the background noises.
Evaluations on the text rotation prediction tasks show that our method improves
the performance by a large margin.
