University of Hertfordshire

From the same journal

  • Xiaojun Zhai
  • Faycal Bensaali
  • Reza Sotudeh
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Original languageEnglish
Pages (from-to)337-344
Number of pages8
JournalIET Circuits, Devices & Systems
Early online date27 Aug 2013
Publication statusPublished - Nov 2013


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

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