Comparing the minimum spatial-frequency content for recognizing Chinese and alphabet characters

Hui Wang, Gordon E. Legge

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Visual blur is a common problem that causes difficulty in pattern recognition for normally sighted people under degraded viewing conditions (e.g., near the acuity limit, when defocused, or in fog) and also for people with impaired vision. For reliable identification, the spatial frequency content of an object needs to extend up to or exceed a minimum value in units of cycles per object, referred to as the critical spatial frequency. In this study, we investigated the critical spatial frequency for alphabet and Chinese characters, and examined the effect of pattern complexity. The stimuli were divided into seven categories based on their perimetric complexity, including the lowercase and uppercase alphabet letters, and five groups of Chinese characters. We found that the critical spatial frequency significantly increased with complexity, from 1.01 cycles per character for the simplest group to 2.00 cycles per character for the most complex group of Chinese characters. A second goal of the study was to test a space-bandwidth invariance hypothesis that would represent a tradeoff between the critical spatial frequency and the number of adjacent patterns that can be recognized at one time. We tested this hypothesis by comparing the critical spatial frequencies in cycles per character from the current study and visual-span sizes in number of characters (measured by Wang, He, & Legge, 2014) for sets of characters with different complexities. For the character size (1.28) we used in the study, we found an invariant product of approximately 10 cycles, which may represent a capacity limitation on visual pattern recognition.

Original languageEnglish (US)
Article number1
JournalJournal of vision
Volume18
Issue number1
DOIs
StatePublished - Jan 1 2018

Keywords

  • Chinese character
  • Letter recognition
  • Minimal spatial frequency
  • Pattern complexity
  • Pattern recognition
  • Reading
  • Visual blur

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