Implementation of real-time handwriting recognition system using touch panel based on neural network

Sung Yang, Cheng Fang Huang, Bo Jhih Hu, Teh-Lu Liao, Jun Juh Yan

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

Based on neural network, this study contributes to propose a real-time handwriting recognition system with Arabic numbers and lowercase letters. It includes two parts which are hardware design and software algorithm. In hardware design, after pressing the touch panel surface, analog signals are obtained and transformed into digital ones by A/D converter. In software algorithm, recognition architecture is constructed by three level backpropagation neural network and learning samples of Arabic numbers and lowercase letters are collected from nine schoolmates. Based on the illustration, the proposed handwriting recognition system of this study can achieve about 90% correction rates and satisfy the market standard.

Original languageEnglish
Pages (from-to)148-154
Number of pages7
JournalLife Science Journal
Volume9
Issue number3
Publication statusPublished - 2012 Dec 1

All Science Journal Classification (ASJC) codes

  • Biochemistry, Genetics and Molecular Biology(all)

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