Real-time optical character recognition on field programmable gate array for automatic number plate recognition system

作     者：Zhai, Xiaojun Bensaali, Faycal Sotudeh, Reza 

作者机构：Univ Hertfordshire Sch Engn & Technol Hatfield AL10 9AB Herts England Qatar Univ Coll Engn Dept Elect Engn Doha Qatar Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England 

出 版 物：《IET CIRCUITS DEVICES & SYSTEMS》 (IET Circuits Devices Syst.)

年 卷 期：2013年第7卷第6期

页      面：337-344页

核心收录：

学科分类：0808[工学-电气工程] 08[工学] 

主　　题：field programmable gate arrays neural nets optical character recognition time 0 7 ms UK binary character images 4M Gates Xilinx Virtex-4 LX40 Mentor Graphics RC240 FPGA development board ANPR application artificial neural network-based OCR algorithm number plate image number plate characters automatic number plate recognition system field programmable gate array real-time optical character recognition 

摘      要：The last main stage in an automatic number plate recognition system (ANPRs) is optical character recognition (OCR), where the number plate characters on the number plate image are converted into encoded texts. In this study, an artificial neural network-based OCR algorithm for ANPR application and its efficient architecture are presented. The proposed architecture has been successfully implemented and tested using the Mentor Graphics RC240 field programmable gate arrays (FPGA) development board equipped with a 4M Gates Xilinx Virtex-4 LX40. A database of 3570 UK binary character images have been used for testing the performance of the proposed architecture. Results achieved have shown that the proposed architecture can meet the real-time requirement of an ANPR system and can process a character image in 0.7 ms with 97.3% successful character recognition rate and consumes only 23% of the available area in the used FPGA.