NDLI: Chinese vehicle license plate recognition using kernel-based extreme learning machine with deep convolutional features

Content Provider	IET Digital Library
Author	Yang, Yun Li, Donghai Duan, Zongtao
Abstract	License plate recognition (LPR) is an important component of intelligent transportation systems. Compared with letters and numbers, Chinese characters contain more information, making automatic recognition more difficult. Accurate Chinese LPR (CLPR) is determined by three factors: training dataset, feature extractor, and classifier. Most license plates with benchmark dataset contain only letters and numbers; thus, the authors build a large dataset for CLPR. Convolutional neural networks (CNNs) can be used to extract inherent image features, on all levels of abstraction. CNNs can be used for classification if they have a sufficient number of fully connected layers. This implies that CNNs must be trained using gradient descent-based methods, which often yields sub-optimal results. Extreme learning machines (ELMs) demonstrate impressive performance on classification, with good generalisation. Therefore, the authors propose a novel deep architecture for CLPR which combines a CNN and an ELM. Firstly, a CNN without fully connected layers, working as a feature extractor, learns deep features associated with characters in written Chinese. Then, a kernel-based ELM (KELM) classifier, which accepts CNN features as input, is utilised for classification. Compared with CNNs that use Softmax, support vector machines and ELMs, the CNN that uses KELM yields competitive results in a shorter training time.
Starting Page	213
Ending Page	219
Page Count	7
ISSN	1751956X
Volume Number	12
e-ISSN	17519578
Issue Number	Issue 3, Apr (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-its/12/3
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-its.2017.0136
Journal	IET Intelligent Transport Systems
Publisher Date	2017-11-28
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Chinese Vehicle License Plate Recognition Classification CLPR CNN Computer Vision And Image Processing Technique Convolutional Neural Network Deep Convolutional Features ELM Feature Extraction Feature Extractor Feedforward Neural Network Image Classification Image Recognition KELM Classifier Kernel-based Extreme Learning Machine Knowledge Engineering Technique Learning in AI Neural Computing Technique Object Recognition Softmax Support Vector Machine Traffic Engineering Computing
Content Type	Text
Resource Type	Article
Subject	Law Transportation Environmental Science Mechanical Engineering

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Extreme learning machine with feature mapping of kernel function

Multi-focus image fusion through DCNN and ELM

Efficient and unified license plate recognition via lightweight deep neural network

License plate segmentation and recognition system using deep learning and OpenVINO

Deep-learning-based license plate detection method using vehicle region extraction

Improved softmax loss for deep learning-based face and expression recognition

A Spectral-Spatial Domain-Specific Convolutional Deep Extreme Learning Machine for Supervised Hyperspectral Image Classification

Learning deep features for image emotion classification

Learning pairwise SVM on hierarchical deep features for ear recognition

Chinese vehicle license plate recognition using kernel-based extreme learning machine with deep convolutional features

Similar Documents

Extreme learning machine with feature mapping of kernel function

Multi-focus image fusion through DCNN and ELM

Efficient and unified license plate recognition via lightweight deep neural network

License plate segmentation and recognition system using deep learning and OpenVINO

Deep-learning-based license plate detection method using vehicle region extraction

Improved softmax loss for deep learning-based face and expression recognition

A Spectral-Spatial Domain-Specific Convolutional Deep Extreme Learning Machine for Supervised Hyperspectral Image Classification

Learning deep features for image emotion classification

Learning pairwise SVM on hierarchical deep features for ear recognition

Chinese vehicle license plate recognition using kernel-based extreme learning machine with deep convolutional features