Deep Image Orientation Angle Detection

TL;DR

This paper introduces a novel deep learning approach combining CNNs with a custom loss function to accurately estimate and rectify the orientation angle of images across the full 0 to 360-degree range, achieving state-of-the-art results.

Contribution

It presents a new method that integrates CNNs with a specially designed loss function for precise angle detection in images, surpassing previous techniques.

Findings

Achieved state-of-the-art accuracy in image orientation detection

Effective estimation of angles across the full 0-360 degree range

Demonstrated robustness on diverse image datasets

Abstract

Estimating and rectifying the orientation angle of any image is a pretty challenging task. Initial work used the hand engineering features for this purpose, where after the invention of deep learning using convolution-based neural network showed significant improvement in this problem. However, this paper shows that the combination of CNN and a custom loss function specially designed for angles lead to a state-of-the-art results. This includes the estimation of the orientation angle of any image or document at any degree (0 to 360 degree),