Deep Image Orientation Angle Detection
Subhadip Maji, Smarajit Bose

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),
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Taxonomy
TopicsAdvanced Neural Network Applications · Advanced Image and Video Retrieval Techniques · Image and Object Detection Techniques
