Character generation under grid constraints
SIGGRAPH '87 Proceedings of the 14th annual conference on Computer graphics and interactive techniques
Digital typography: an introduction to type and composition for computer system design
Digital typography: an introduction to type and composition for computer system design
Computer Modern Typefaces
Example-based hinting of true type fonts
Proceedings of the 27th annual conference on Computer graphics and interactive techniques
Perceptually Tuned Generation of Grayscale Fonts
IEEE Computer Graphics and Applications
Constraint-based approach for automatic hinting of digital typefaces
ACM Transactions on Graphics (TOG)
Scalable realtime glyph rendering on consumer level graphics hardware: a hybrid approach
VIIP '07 The Seventh IASTED International Conference on Visualization, Imaging and Image Processing
NPAR '12 Proceedings of the Symposium on Non-Photorealistic Animation and Rendering
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In today's digital computers, phototypesetters and printers, typographic fonts are mainly given by their outline descriptions. Outline descriptions alone do not provide any information about character parts like stems serifs, shoulders, and bowls. But, in order to produce the best looking characters at a given size on a specific printer, non-linear operations must be applied to parts of the character shape. At low-resolution, grid-fitting of character outlines is required for generating nice and regular raster characters. For this reason, grid-fitting rules called hints are added to the character description. Grid-fitting rules require as parameters certain characteristic points within the shape outlines. In order to be able to detect these characteristic points in any given input font, a topological model representing the essence of the shapes found in typographic latin typefaces is proposed. This model includes sufficient information for matching existing non-fancy outline fonts to the model description. For automatic hint generation, a table of applicable hints is added into the topological model description. After matching a given input shape to the model, hints which can be applied to the shape of the given font are taken and added to its outline description. Furthermore, a structural description of individual letter shape parts using characteristic model points can be added to the model. Such a description provides knowledge about typographic structure elements like stems, serifs and bowls.