A novel stroke-based feature extraction for handwritten chinese character recognition

Hung Pin Chiu, Din Chang Tseng

Research output: Contribution to journalArticlepeer-review

24 Scopus citations

Abstract

A stroke-based approach to extract skeletons and structural features for handwritten Chinese character recognition is proposed. We first determine stroke directions based on the directional run-length information of binary character patterns. According to the stroke directions and their adjacent relationships, we split strokes into stroke and fork segments, and then extract the skeletons of the stroke segments called skeleton segments. After all skeleton segments are extracted, fork segments are processed to find the fork points and fork degrees. Skeleton segments that touch a fork segment are connected at the fork point, and all connected skeleton segments form the character skeleton. According to the extracted skeletons and fork points, we can extract primitive strokes and stroke direction maps for recognition. A simple classifier based on the stroke direction map is presented to recognize regular and rotated characters to verify the ability of the proposed feature extraction for handwritten Chinese character recognition. Several experiments are carried out, and the experimental results show that the proposed approach can easily and effectively extract skeletons and structural features, and works well for handwritten Chinese character recognition.

Original languageEnglish
Pages (from-to)1947-1959
Number of pages13
JournalPattern Recognition
Volume32
Issue number12
DOIs
StatePublished - Dec 1999

Keywords

  • Character stroke
  • Fork point
  • Handwritten chinese character recognition
  • Primitive stroke
  • Stroke direction
  • Thinning

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