# Deep Image Orientation Angle Detection

arXiv: https://arxiv.org/abs/2007.06709 · HTML: https://mlsanity.com/paper/2007.06709/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2020.

## Built on by (most cited first, 5 of 5)

- [BitMark: Watermarking Bitwise Autoregressive Image Generative Models](https://mlsanity.com/paper/2506.21209/) (2025)
- [A Simple Approach to Image Tilt Correction with Self-Attention MobileNet for Smartphones](https://mlsanity.com/paper/2111.00398/) (2021)
- [Depth-Aware Image and Video Orientation Estimation](https://mlsanity.com/paper/2604.13995/) (2026)
- [Silver medal Solution for Image Matching Challenge 2024](https://mlsanity.com/paper/2411.01851/) (2024)
- [Image Rotation Angle Estimation: Comparing Circular-Aware Methods](https://mlsanity.com/paper/2603.25351/) (2026)

## Abstract (arXiv)

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),