5 research outputs found
Towards End-to-end Car License Plate Location and Recognition in Unconstrained Scenarios
Benefiting from the rapid development of convolutional neural networks, the
performance of car license plate detection and recognition has been largely
improved. Nonetheless, challenges still exist especially for real-world
applications. In this paper, we present an efficient and accurate framework to
solve the license plate detection and recognition tasks simultaneously. It is a
lightweight and unified deep neural network, that can be optimized end-to-end
and work in real-time. Specifically, for unconstrained scenarios, an
anchor-free method is adopted to efficiently detect the bounding box and four
corners of a license plate, which are used to extract and rectify the target
region features. Then, a novel convolutional neural network branch is designed
to further extract features of characters without segmentation. Finally,
recognition task is treated as sequence labelling problems, which are solved by
Connectionist Temporal Classification (CTC) directly. Several public datasets
including images collected from different scenarios under various conditions
are chosen for evaluation. A large number of experiments indicate that the
proposed method significantly outperforms the previous state-of-the-art methods
in both speed and precision
Developing Learning-Based Preprocessing Methods for Detecting Complicated Vehicle Licence Plates
A licence plate detection (LPD) system is an important tool in several roadway traffic applications. This study aims to develop an advanced detection system that works well in complicated scenarios. It proposes a robust preprocessing enhancement method for accurately detecting the licence plates from difficult vehicle images. The proposed method includes the combination of a Gaussian filter, an enhancement cumulative histogram equalization method, and a contrast-limited adaptive histogram equalization technique. The local binary pattern and median filter with histogram of oriented gradient descriptors are used as powerful tools to extract key features from three types of licence plate resolutions. The extracted features are used as input to support vector machine classifier. Processing methods, such as a position-based method are used with the detector to reduce unwanted bounding boxes, as well as false positive values. Four databases consisting of 2050 vehicle images under different conditions are used. Various detection metrics, object localization, and the receiver operating characteristic (ROC) curve are used to evaluate the performance of the proposed method. The experimental results on vehicles databases in several languages, including English, Chinese, and Arabic number plates, show that the proposed method has achieved significant performance improvements. It outperforms the state-of-the-art approaches in terms of both the detection rate and the processing time. The detection rate when trained with 1520 LP images is 99.62% with a false positive rate of 1.675% for complicated images. The average detection time per vehicle image is 0.2408 milliseconds
An Efficient and Layout-Independent Automatic License Plate Recognition System Based on the YOLO detector
This paper presents an efficient and layout-independent Automatic License
Plate Recognition (ALPR) system based on the state-of-the-art YOLO object
detector that contains a unified approach for license plate (LP) detection and
layout classification to improve the recognition results using post-processing
rules. The system is conceived by evaluating and optimizing different models,
aiming at achieving the best speed/accuracy trade-off at each stage. The
networks are trained using images from several datasets, with the addition of
various data augmentation techniques, so that they are robust under different
conditions. The proposed system achieved an average end-to-end recognition rate
of 96.9% across eight public datasets (from five different regions) used in the
experiments, outperforming both previous works and commercial systems in the
ChineseLP, OpenALPR-EU, SSIG-SegPlate and UFPR-ALPR datasets. In the other
datasets, the proposed approach achieved competitive results to those attained
by the baselines. Our system also achieved impressive frames per second (FPS)
rates on a high-end GPU, being able to perform in real time even when there are
four vehicles in the scene. An additional contribution is that we manually
labeled 38,351 bounding boxes on 6,239 images from public datasets and made the
annotations publicly available to the research community