Knowledge Model Based Approach in Recognition of On Line Chinese Characters

Kuo Sen Chou, Kuo Chin Fan, Tzu I. Fan, Chang Keng Lin, Bor Shenn Jeng

Research output: Contribution to journalArticlepeer-review

15 Scopus citations

Abstract

A knowledge modelbased OCR system is presented in this study for the recognition of On Line connected stroke Chinese characters. In our approach, segment attributes are first extracted to characterize the segment sequence of an unknown character. Next, radical recognition based on model matching is adopted as the coarse classification to reduce the number of candidate characters before detailed matching. Finally, a deviation modeling method is proposed to recognize not only regular writing characters but also characters with stroke-order and stroke-number deviations. The effectiveness of the approach is verified by experiments on the recognition of On Line Chinese characters.

Original languageEnglish
Pages (from-to)1566-1575
Number of pages10
JournalIEEE Journal on Selected Areas in Communications
Volume12
Issue number9
DOIs
StatePublished - Dec 1994

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