# Benchmark for License Plate Character Segmentation

arXiv: https://arxiv.org/abs/1607.02937 · HTML: https://mlsanity.com/paper/1607.02937/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Jul 2016.

## Built on by (most cited first, 22 of 22)

- [On the Cross-dataset Generalization in License Plate Recognition](https://mlsanity.com/paper/2201.00267/) (2022)
- [A First Look at Dataset Bias in License Plate Recognition](https://mlsanity.com/paper/2208.10657/) (2022)
- [Deep Learning Serves Traffic Safety Analysis: A Forward-looking Review](https://mlsanity.com/paper/2203.10939/) (2022)
- [Unified Chinese License Plate Detection and Recognition with High Efficiency](https://mlsanity.com/paper/2205.03582/) (2022)
- [Leveraging Model Fusion for Improved License Plate Recognition](https://mlsanity.com/paper/2309.04331/) (2023)
- [Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation](https://mlsanity.com/paper/2601.07671/) (2026)
- [Toward Enhancing Vehicle Color Recognition in Adverse Conditions: A Dataset and Benchmark](https://mlsanity.com/paper/2408.11589/) (2024)
- [LPLCv2: An Expanded Dataset for Fine-Grained License Plate Legibility Classification](https://mlsanity.com/paper/2604.08741/) (2026)
- [A Training-Free Framework for Video License Plate Tracking and Recognition with Only One-Shot](https://mlsanity.com/paper/2408.05729/) (2024)
- [License Plate Images Generation with Diffusion Models](https://mlsanity.com/paper/2501.03374/) (2025)
- [MF-LPR2: Multi-Frame License Plate Image Restoration and Recognition using Optical Flow](https://mlsanity.com/paper/2508.14797/) (2025)
- [Image-free multi-character recognition](https://mlsanity.com/paper/2112.10587/) (2021)
- [FANVID: A Benchmark for Face and License Plate Recognition in Low-Resolution Videos](https://mlsanity.com/paper/2506.07304/) (2025)
- [End-to-End High Accuracy License Plate Recognition Based on Depthwise Separable Convolution Networks](https://mlsanity.com/paper/2202.10277/) (2022)
- [Synthesizing Annotated Image and Video Data Using a Rendering-Based Pipeline for Improved License Plate Recognition](https://mlsanity.com/paper/2209.14448/) (2022)
- [Tensor Slicing and Optimization for Multicore NPUs](https://mlsanity.com/paper/2304.03013/) (2023)
- [Iranian License Plate Recognition Using a Reliable Deep Learning Approach](https://mlsanity.com/paper/2305.02292/) (2023)
- [Multi-Feature Aggregation in Diffusion Models for Enhanced Face Super-Resolution](https://mlsanity.com/paper/2408.15386/) (2024)
- [PatrolVision: Automated License Plate Recognition in the wild](https://mlsanity.com/paper/2504.10810/) (2025)
- [A New Hybrid Model of Generative Adversarial Network and You Only Look Once Algorithm for Automatic License-Plate Recognition](https://mlsanity.com/paper/2509.06868/) (2025)
- [BLPR: Robust License Plate Recognition under Viewpoint and Illumination Variations via Confidence-Driven VLM Fallback](https://mlsanity.com/paper/2604.09927/) (2026)
- [A Benchmark for Vehicle Attribute Classification in Cross-Domain Surveillance Scenarios](https://mlsanity.com/paper/2609.01584/) (2026)

## Abstract (arXiv)

Automatic License Plate Recognition (ALPR) has been the focus of many researches in the past years. In general, ALPR is divided into the following problems: detection of on-track vehicles, license plates detection, segmention of license plate characters and optical character recognition (OCR). Even though commercial solutions are available for controlled acquisition conditions, e.g., the entrance of a parking lot, ALPR is still an open problem when dealing with data acquired from uncontrolled environments, such as roads and highways when relying only on imaging sensors. Due to the multiple orientations and scales of the license plates captured by the camera, a very challenging task of the ALPR is the License Plate Character Segmentation (LPCS) step, which effectiveness is required to be (near) optimal to achieve a high recognition rate by the OCR. To tackle the LPCS problem, this work proposes a novel benchmark composed of a dataset designed to focus specifically on the character segmentation step of the ALPR within an evaluation protocol. Furthermore, we propose the Jaccard-Centroid coefficient, a new evaluation measure more suitable than the Jaccard coefficient regarding the location of the bounding box within the ground-truth annotation. The dataset is composed of 2,000 Brazilian license plates consisting of 14,000 alphanumeric symbols and their corresponding bounding box annotations. We also present a new straightforward approach to perform LPCS efficiently. Finally, we provide an experimental evaluation for the dataset based on four LPCS approaches and demonstrate the importance of character segmentation for achieving an accurate OCR.