Siyu Zhu

Abstract

An end-to-end license plate recognition (LPR) system is proposed. It is composed of pre-processing, detection, segmentation and character recognition to find and recognize plates from camera based still images. The system utilizes connected component (CC) properties to quickly extract the license plate region. A novel two-stage CC filtering is utilized to address both shape and spatial relationship information to produce high precision and recall values for detection. Floating peak and valleys (FPV) of projection profiles are used to cut the license plates into individual characters. A turning function based method is proposed to recognize each character quickly and accurately. It is further accelerated using curvature histogram based support vector machine (SVM). The INFTY dataset is used to train the recognition system. And MediaLab license plate dataset is used for testing. The proposed system achieved 89.45% F-measure for detection and 87.33% accuracy for overall recognition rate which is comparable to current state-of-the-art systems.

Library of Congress Subject Headings

Automobile license plates--Data processing; Optical pattern recognition; Image analysis

Publication Date

3-2015

Document Type

Thesis

Student Type

Graduate

Degree Name

Electrical Engineering (MS)

Department, Program, or Center

Electrical Engineering (KGCOE)

Advisor

Sohail Dianat

Advisor/Committee Member

Lalit K. Mestha

Advisor/Committee Member

Eli Saber

Comments

Physical copy available from RIT's Wallace Library at HE5620.L5 Z48 2015

Campus

RIT – Main Campus

Plan Codes

EEEE-MS

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