  In this paper, we introduce HoughToRadon Transform layer, a novel layer
designed to improve the speed of neural networks incorporated with Hough
Transform to solve semantic image segmentation problems. By placing it after a
Hough Transform layer, "inner" convolutions receive modified feature maps with
new beneficial properties, such as a smaller area of processed images and
parameter space linearity by angle and shift. These properties were not
presented in Hough Transform alone. Furthermore, HoughToRadon Transform layer
allows us to adjust the size of intermediate feature maps using two new
parameters, thus allowing us to balance the speed and quality of the resulting
neural network. Our experiments on the open MIDV-500 dataset show that this new
approach leads to time savings in document segmentation tasks and achieves
state-of-the-art 97.7% accuracy, outperforming HoughEncoder with larger
computational complexity.
