ICPR 2026 LRLPR Competition

The Challenge

In surveillance contexts, license plate images are frequently captured at low resolutions or subjected to heavy compression due to storage and bandwidth limitations. Consequently, characters often become distorted, blend into the background, or overlap with neighboring symbols, making automated recognition particularly challenging.

Recognizing these low-resolution license plates remains a highly challenging and underexplored problem with strong forensic and societal relevance. The difficulty is evident in the fact that even state-of-the-art methods currently struggle to surpass 50-60% recognition accuracy. Improving recognition performance under such adverse conditions, therefore, offers a significant opportunity to substantially narrow investigative searches and expedite law enforcement decisions. This competition aims to encourage the development of advanced approaches, such as super-resolution, temporal modeling, and robust Optical Character Recognition (OCR) techniques, capable of operating effectively despite low-quality input conditions.

Dataset

Illustrative example of low resolution and high resolution license plate images — **Figure 1. Examples of tracks from the competition dataset.** Each track in the training set contains five consecutive low-resolution (LR) images and five consecutive high-resolution (HR) images of the same license plate. Each row illustrates a complete track, with LR images displayed on the left and their corresponding HR counterparts on the right. **In the competition, participants must predict the license plate text using only the low-resolution (LR) images**. The HR images are included solely in the training set, enabling participants to explore image enhancement and super-resolution strategies to improve recognition accuracy.

Training Data (LR + HR images)

Per Track: 5 Low-Resolution (LR) images and 5 High-Resolution (HR) images (see Figure 1).
- The dataset creation process is illustrated in Figure 2.
Scenario A (10,000 tracks): obtained from a recently published dataset (Nascimento et al., 2025). These tracks were collected under relatively controlled environmental conditions (e.g., no rain and only during daylight hours).
- Each track includes annotations for the license plate text, layout, and corner positions.
Scenario B (10,000 tracks): newly collected specifically for this competition, using the same camera as Scenario A but oriented in a different direction. These tracks were captured under a wider range of environmental conditions (Figure 3).
- Each track includes annotations for the license plate text and layout. Corner annotations are not provided.
Access to the training data: see Get Started. The dataset will be made available exclusively for non-commercial use and only to institutional email addresses from educational or research organizations.

Test Data (LR Images Only)

Images per Track: 5 Low-Resolution (LR) images.
Note: High-Resolution (HR) images are provided exclusively for training, so participants may explore image enhancement techniques.
Public Test Set (1,000 Tracks): tracks sourced from Scenario B, used for public leaderboard ranking (soon).
Note: This set is a subset of the Blind Test Set.
Blind Test Set (3,000 Tracks): Tracks sourced from Scenario B, used for final competition ranking.
Submission Format: Participants in the competition are required to submit a single predictions.txt file where each line contains the prediction for one track using the following format:
```
track_00001,ABC1234;0.9876
track_00002,DEF5678;0.6789
track_00003,GHI9012;0.4521
...
```
Each line consists of: track_id,plate_text;confidence.
- Participants may use any method to aggregate predictions across the five LR images (e.g., majority voting, confidence-based selection, temporal modeling).

Overview of the dataset creation process for license plate images — **Figure 2. Overview of the dataset creation process.** License plates were first detected with YOLOv11 and then tracked with BoT-SORT. Low-resolution patches correspond to images captured when the vehicle was farther from the camera, while high-resolution patches were obtained when the vehicle was closer. Each license plate text was subsequently labeled semi-automatically using a state-of-the-art recognition model applied to the high-resolution patches.

Examples of capture conditions from Scenario B in the competition dataset — **Figure 3. Examples of capture conditions from which the LP images in the competition dataset were extracted (Scenario B).** These images showcase a variety of vehicle types, captured under different environmental conditions.

Competition Schedule

Dec 15, 2025

Registration Opens

Website goes live

Dec 18, 2025

Training Set Releases

Competition starts

Jan 12, 2026

Public Test Set Releases

30% of the blind test data (used for leaderboard)

Feb 16, 2026

Blind Test Set Releases

Official test data (used for final ranking)

Mar 1, 2026

Submission Deadline

Competition ends

ICPR 2026 Presentation:

The competition summary paper will be presented at ICPR 2026 in August 2026 and will subsequently be published in the conference proceedings.

Get Started

Step by step

Request the training data: download and sign the license agreement, then submit the signed document via the ICPR26-LRLPR Dataset Request Form. A Gmail account is required to submit the agreement through the form; however, you must provide an institutional email address in the designated field. The dataset will be made available exclusively for non-commercial use and only to institutional email addresses associated with educational or research organizations (e.g., .edu, .ac, or similar). Once the request is approved, a download link for the training dataset will be provided;
Develop your method: predictions must be based exclusively on the five low-resolution (LR) images provided per track. High-resolution images are available only in the training set, so participants may explore image enhancement techniques;
Next steps: detailed instructions for the remaining steps will be announced in the coming weeks, before the release of the public test set.

Rules

Team size: each participating team may consist of up to five participants;
Use of external data: external training data is permitted, but its source and usage must be clearly documented by top-ranked teams. Teams that rely on external data will be required to report results obtained using only the provided training dataset;
Code verification: if deemed necessary by the organizers, top-ranked teams may be asked to either submit their code to the organizers or make it publicly available. This may include a complete and reproducible training pipeline, solely to verify that the reported results were obtained algorithmically and without any use of test data during training.

Evaluation Criteria

📊 Primary Metric: Recognition Rate (Exact Match)

The official ranking is based on the Recognition Rate computed over the Blind Test Set.

How Recognition Rate is Computed

Recognition Rate =

Number of Correct Predictions

Total Tracks in the Test Set

Criteria for a Correct Prediction

A prediction is considered correct only when all characters match the ground truth exactly;
If any character differs, the prediction is not counted as correct.

⚖️ Tie-Breaker: Confidence Gap

If two or more teams achieve the same Recognition Rate, the tie will be broken using a metric called Confidence Gap.

How Confidence Gap is Computed

Confidence Gap =

Mean Confidence of Correct Predictions

Mean Confidence of Incorrect Predictions

Ranking Rule

The team with the largest Confidence Gap ranks higher.

A larger gap indicates a better calibrated and more reliable model.

Competition Platform

The competition will be hosted on Codabench (subject to confirmation).

For details on the required submission file, please see Submission Format.

For the public test set, teams are subject to the following submission limits:

1 submission per day;
10 total submissions over the competition period.

Reminder: please review the Rules before participating.

🏆 Recognition & Publication

All teams that submit predictions for the blind test set will receive a certificate of participation from the competition organizers;
The top 3 participating teams will receive certificates of achievement from the ICPR 2026 committee;
The top 5 teams will be invited to contribute to the competition summary paper included in the ICPR 2026 proceedings.

Additional teams may be invited to contribute, contingent upon the final ranking and the overall number of participating teams.

FAQ (Summary)

Q: How large can a team be?

A: Each team may have up to five participants.

Q: We would like to participate. What do we need to do?

A: Complete the ICPR26-LRLPR Dataset Request Form by providing your contact details, institutional affiliation, and team members. A Gmail account is required to submit the agreement through the form; however, you must provide an institutional email address in the designated field. Follow the instructions in the form and submit it to register.

Q: What are the requirements to download the dataset?

A: Dataset access is granted after completing and submitting the license agreement through the ICPR26-LRLPR Dataset Request Form. Registration must use an institutional email address from an educational or research organization, such as domains ending in .edu, .ac or similar.

Q: How will the submissions be evaluated?

A: Submissions will be evaluated on the Blind Test Set using the Recognition Rate, which measures the proportion of tracks whose predicted license plate text exactly matches the ground truth. Additional details, including the tie-breaking procedure based on Confidence Gap, are described in the Evaluation Criteria section.

Q: What are the prizes?

A: The top three teams will receive certificates of achievement from the ICPR 2026 committee. The top five teams will be invited to contribute to the competition summary paper to be published in the ICPR 2026 proceedings (subject to peer review). Depending on the final ranking, additional teams may also be invited to contribute.

Q: Can we use external datasets to train our models?

A: Yes. The use of additional training data is allowed, but its source and usage must be clearly documented. Top-ranked teams that use external data will also be required to submit results obtained using only the provided training dataset.

Q: Can the test sets be used in any way during training?

A: No. Manual annotation or any use of test data for training is strictly prohibited. To verify that results were obtained through a fully algorithmic process and without access to test data, top-ranked teams may be asked, if deemed necessary by the organizers, to submit their code to the organizers or make it publicly available.

Q: Will we need to submit our code?

A: Not necessarily. If deemed necessary by the organizers, top-ranked teams may be asked to either submit their code to the organizers or make it publicly available. This may include a complete and reproducible training pipeline, used solely to confirm that the results were obtained algorithmically and without any use of test data for training.

Q: How can we contact the organizers?

A: For any questions about the competition, please contact us at icpr26lrlpr@gmail.com.

Organizing Committee

Rayson Laroca

Professor, PUCPR & UFPR, Brazil

Professor at the Pontifical Catholic University of Paraná (PUCPR), Brazil. Actively engaged in automatic license plate recognition research since 2018. He has developed widely adopted benchmarks in the field, including UFPR ALPR and RodoSol ALPR, which have become reference datasets for the research community. His conference and journal publications have helped shape the current state of the art in the field.

Valfride Nascimento

PhD Candidate, UFPR, Brazil

PhD candidate at the Federal University of Paraná (UFPR), Brazil, where his research focuses on license plate super resolution. His proposed methods have demonstrated superior performance compared to general-purpose and domain-specific approaches, bridging the gap to real-world forensic scenarios.

David Menotti

Full Professor, UFPR, Brazil

Full Professor at the Federal University of Paraná (UFPR), Brazil. Actively engaged in automatic license plate recognition research since 2010. Extensive experience in computer vision, especially in surveillance applications. Achieved top-ranked results in several international challenges and consistently listed by Elsevier among the world's top two percent most influential researchers.

Selected References

• V. Nascimento, G. E. Lima, R. O. Ribeiro, W. R. Schwartz, R. Laroca, D. Menotti, "Toward Advancing License Plate Super-Resolution in Real-World Scenarios: A Dataset and Benchmark," Journal of the Brazilian Computer Society, vol. 31, no. 1, pp. 435-449, 2025;

• H. Gong, Z. Zhang, Y. Feng, A. Nguyen, H. Liu, "License Plate Recognition under Challenging Conditions Using Multi-Scale Feature Fusion," IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 17831-17840, 2025;

• R. Laroca, V. Estevam, G. J. P. Moreira, R. Minetto, D. Menotti, "Advancing Multinational License Plate Recognition Through Synthetic and Real Data Fusion: A Comprehensive Evaluation," IET Intelligent Transport Systems, vol. 19, no. 1, p. e70086, 2025;

• K. Na, J. Oh, Y. Cho, B. Kim, S. Cho, J. Choi, I. Kim, "MF-LPR2: Multi-frame License Plate Image Restoration and Recognition Using Optical Flow," Computer Vision and Image Understanding, vol. 256, p. 104361, 2025;

• V. Nascimento, R. Laroca, R. O. Ribeiro, W. R. Schwartz, D. Menotti, "Enhancing License Plate Super-Resolution: A Layout-Aware and Character-Driven Approach," in Conference on Graphics, Patterns and Images (SIBGRAPI), pp. 1-6, Sept. 2024;

• R. Laroca, L. A. Zanlorensi, V. Estevam, R. Minetto, D. Menotti, "Leveraging Model Fusion for Improved License Plate Recognition," in Iberoamerican Congress on Pattern Recognition (CIARP), pp. 60-75, Nov. 2023;

• V. Nascimento, R. Laroca, J. A. Lambert, W. R. Schwartz, D. Menotti, "Super-Resolution of License Plate Images Using Attention Modules and Sub-Pixel Convolution Layers," Computers & Graphics, vol. 113, pp. 69-76, 2023.