License Plate Detection and Recognition in Unconstrained Scenarios

Sergio Montazzolli Silva (Home | LinkedIn | Lattes)
Claudio Rosito Jung (Home | Lattes)
Frame by frame system output.

Paper Information


Despite the large number of both commercial and academic methods for Automatic License Plate Recognition (ALPR), most existing approaches are focused on a specific license plate (LP) region (e.g. European, US, Brazilian, Taiwanese, etc.), and frequently explore datasets containing approximately frontal images. This work proposes a complete ALPR system focusing on unconstrained capture scenarios, where the LP might be considerably distorted due to oblique views. Our main contribution is the introduction of a novel Convolutional Neural Network (CNN) capable of detecting and rectifying multiple distorted license plates in a single image, which are fed to an Optical Character Recognition (OCR) method to obtain the final result. As an additional contribution, we also present manual annotations for a challenging set of LP images from different regions and acquisition conditions. Our experimental results indicate that the proposed method, without any parameter adaptation or fine tuning for a specific scenario, performs similarly to state-of-the-art commercial systems in traditional scenarios, and outperforms both academic and commercial approaches in challenging ones.

Links and BibTex


Training and evaluation datasets

The data available for download in this webpage consists only of annotations. The images where those annotations come from are part of freely available datasets not owned by us. So please refer to the following links for instructions on how to obtain each dataset.

Training annotations: download the zip file and take a look at the README.txt for more instructions about how the data is organized.

Test dataset (CD-HARD): CSV containg an image filename (1st column) and the license plates on it (2nd column and so on) for each row. The images used are solely from Cars dataset.

Implementation and Trained Networks

We created a GitHub repository containing the necessary code to reproduce our results. You can checkout, compile and test using the following commands:

git clone

cd alpr-unconstrained/

cd darknet/ && make && cd ..

bash samples/ /tmp/output_dir /tmp/output_dir/results.csv

More details can be found in the project description.