# License Plate Recognition Based On Multi-Angle View Model

arXiv: https://arxiv.org/abs/2309.12972 · HTML: https://mlsanity.com/paper/2309.12972/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 34 references that are in the map.
- First posted on arXiv 22 Sep 2023.

## Builds on (most cited first, 6 of 6)

- [Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression](https://mlsanity.com/paper/1911.08287/) (2019)
- [Prior-aware Neural Network for Partially-Supervised Multi-Organ Segmentation](https://mlsanity.com/paper/1904.06346/) (2019)
- [Symmetry-constrained Rectification Network for Scene Text Recognition](https://mlsanity.com/paper/1908.01957/) (2019)
- [Towards Robust Pattern Recognition: A Review](https://mlsanity.com/paper/2006.06976/) (2020)
- [Mask-RCNN and U-net Ensembled for Nuclei Segmentation](https://mlsanity.com/paper/1901.10170/) (2019)
- [2D Attentional Irregular Scene Text Recognizer](https://mlsanity.com/paper/1906.05708/) (2019)

## Abstract (arXiv)

In the realm of research, the detection/recognition of text within images/videos captured by cameras constitutes a highly challenging problem for researchers. Despite certain advancements achieving high accuracy, current methods still require substantial improvements to be applicable in practical scenarios. Diverging from text detection in images/videos, this paper addresses the issue of text detection within license plates by amalgamating multiple frames of distinct perspectives. For each viewpoint, the proposed method extracts descriptive features characterizing the text components of the license plate, specifically corner points and area. Concretely, we present three viewpoints: view-1, view-2, and view-3, to identify the nearest neighboring components facilitating the restoration of text components from the same license plate line based on estimations of similarity levels and distance metrics. Subsequently, we employ the CnOCR method for text recognition within license plates. Experimental results on the self-collected dataset (PTITPlates), comprising pairs of images in various scenarios, and the publicly available Stanford Cars Dataset, demonstrate the superiority of the proposed method over existing approaches.