Topic
Em
About: Em is a research topic. Over the lifetime, 2 publications have been published within this topic receiving 34 citations.
Papers
TL;DR: The results indicate the existence of interactions between all factors considered in this study, and the best overall legibility was achieved by the Times New Roman and Arial typefaces.
Abstract: The effects of typeface, point size, screen resolution and monitor size on legibility were studied in a task-setting similar to skimming headlines in an electronic newspaper. Times New Roman, Book Antiqua, Century Gothic, and Arial were the four typefaces used in the study. The point sizes used to present the headlines were 14 point, 20 point, and 24 point sizes. Two resolutions, 640× 480 pixels and 1024 × 768 pixels, were used in the study on two different monitor sizes—14″ (0.28 mm dot-pitch) and 19″ (0.31 mm dot-pitch). All the headlines used in the study averaged 6 words in length with an average pixel width of 410 pixels when displayed at a logical dots-per-inch of 96 dpi under Microsoft Windows. The headlines were shown to 28 subjects using a brief-exposure method. The results indicate the existence of interactions between all factors considered in this study. The results of further investigation into the simple interaction effects of typeface × point size as well as the simple, simple main effects of typeface are presented for the 14″ monitor. The best overall legibility was achieved by the Times New Roman and Arial typefaces. These two typefaces represent a serif and a sans-serif typeface tuned specifically for the display of text on a computer screen.
36 citations
1 Mar 1998
TL;DR: A methodology for identifying typefaces of printed Chinese characters in documents using three kinds of features: Black, Li, Kai-Round, or Ming-Song, and a character recognition system using two statistical features: crossing counts and contour directional counts.
Abstract: In this paper, we propose a methodology for identifying typefaces of printed Chinese characters in documents. Three kinds of features, stroke width means, stroke width variations, and aspect ratio, are first used to classify character typefaces as: Black, Li, Kai-Round, or Ming-Song. Each of the last two groups contains two typefaces. Vertical/horizontal stroke width ratios are used to distinguish between the Ming and Song typefaces and accumulative pixel ratio to distinguish between the Kai and Round typefaces. Six different typeface feature distributions measured from 5401 printed Chinese characters are considered, and a trapezoid-shaped membership function is constructed for each distribution. Based on these membership functions, we determine what typeface each input character belongs to using a two-level decision tree. To increase the identification rate, the typeface of a certain character is adjusted according to the typeface identification results of the front and the next characters. In the character recognition system, we use two statistical features: crossing counts and contour directional counts. We achieved an 89.87% typeface identification rate in our experiments, and a 95.60% character recognition rate.
3 citations
Related Topics (5)
Performance Metrics
| Year | Papers |
|---|---|
| 1998 | 1 |
| 1997 | 1 |