Model-based matching and hinting of fonts

In 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, nonlinear 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 bowl

Learning a Manifold of Fonts

The design and manipulation of typefaces and fonts is an area requiring substantial expertise; it can take many years of study to become a proficient typographer. At the same time, the use of typefaces is ubiquitous; there are many users who, while not experts, would like to be more involved in tweaking or changing existing fonts without suffering the learning curve of professional typography packages. Given the wealth of fonts that are available today, we would like to exploit the expertise used to produce these fonts, and to enable everyday users to create, explore, and edit fonts. To this end, we build a generative manifold of standard fonts. Every location on the manifold corresponds to a unique and novel typeface, and is obtained by learning a non-linear mapping that intelligently interpolates and extrapolates existing fonts. Using the manifold, we can smoothly interpolate and move between existing fonts. We can also use the manifold as a constraint that makes a variety of new applications possible. For instance, when editing a single character, we can update all the other glyphs in a font simultaneously to keep them compatible with our changes

Legibility in typeface design for screen interfaces

This thesis explores the considerations related to the design of a typeface specifically for the use in interface typography. The genre of interface typefaces is outlined and essential attributes and requirements of this category of typefaces are inspected from the viewpoints of legibility, readability and type design practices. The research is based on the analysis of interface typeface samples, interviews with type designers as well as empirical findings documented by designers. These trade practices and design artefacts are contrasted with findings from cognitive psychology and legibility research. Furthermore the author’s design of the «Silta» typeface and its creation process are used to scrutinize and validate these observations. Amongst the crucial factors in the design of interface typefaces the legibility of confusable characters is extensively analysed. Furthermore, the rasterized on-screen rendering of outline based fonts is identified as a major contributing factor requiring special attention in the design, technical production and testing phases of modern fonts. Additionally, the context and use of interface typography and how users interact with interfaces are identified as the cornerstones influencing the design decisions of a typeface for this use. Finally, the aesthetics of interface typography and the motivations for developing specific interface typefaces are touched upon. As evident from the reviewed material, branding and visual identity often appear to be a driving force in the creation of new interface typefaces. However, the necessity for technological innovation and its demonstration equally inspire new design solutions. While technological limitations stemming from digital display media are increasingly becoming of less importance, the changes in reading behaviour and adaptive typography drive current development

Technical challenges in multiple master font design with extreme form change

The hypothesis was that it is possible to create a unique PostScript Multiple Master font which makes a relatively smooth transition between serifed roman and italic letterforms. In the process of creating such a font, the author discovered new principles of Multiple Master font construction and new methodologies for ensuring smooth transitions. The methodology was to first construct a prototype, in order to explore the complications of such a project; the prototype metamorphosed between the roman and italic forms of the Adobe typeface Minion. For the actual project, roman and italic extremes were newly designed, loosely based on historical models dating from around 1540. The initial models for these fonts were found in a 16th century French edition of Livy\u27s His tory of Rome, published by the Giunti family, from the Melbert B. Cary, Jr. Graphic Arts Collection. The models were scanned, and the digital bitmaps used as the starting point for the digital masters. Creation of the font involved a variety of software tools, on both Macintosh and Windows, notably Macromedia Fontographer. The written thesis project explores the technical and design problems and solutions uncovered in creation of both the prototype and the thesis project, and explaining how these add to the theory and principles of Multiple Master font construction. The thesis also: reviews the relationship between italic and roman typefaces; analyzes the success of the intermediate fonts between italic and roman; and displays printed samples of various steps in the design process, as well as the two final masters and three intermediate fonts at various levels of italicization

A window-based method for automatic typographic parameter extraction

The synthesis of existing fonts with characters represented by parameterized structure elements requires determination of a set of font-specific global and local parameters. Parameters comprise, for example, the widths of vertical stems, horizontal bars and curved elements, the spacing between vertical stems, the relative position of the junction between arches and vertical bars and serif measures. These parameters need to be extracted from existing outline fonts. The paper presents a window based method for locating within existing outline characters the position of character features from which parameters can be measured. The method is based on the match between outline characters and their corresponding virtual skeleton

Rasterization techniques for Chinese outline fonts.

Kwong-ho Wu.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references (leaves 72-75).Chapter 1 --- Introduction --- p.1Chapter 1.1 --- Outline Fonts --- p.2Chapter 1.1.1 --- Advantages and Disadvantages --- p.4Chapter 1.1.2 --- Representations --- p.4Chapter 1.1.3 --- Rasterization --- p.5Chapter 1.2 --- Introduction to This Thesis --- p.6Chapter 1.2.2 --- Organization --- p.7Chapter 1.2.1 --- Objectives --- p.7Chapter 2 --- Chinese Characters Fonts --- p.8Chapter 2.1 --- Large Character Set --- p.8Chapter 2.2 --- Font Styles --- p.8Chapter 2.3 --- Storage Problems --- p.9Chapter 2.4 --- Hierarchical Structure --- p.10Chapter 2.5 --- High Stroke Count --- p.11Chapter 3 --- Rasterization --- p.13Chapter 3.1 --- The Basic Rasterization --- p.13Chapter 3.1.1 --- Scan Conversion --- p.14Chapter 3.1.2 --- Filling Outline --- p.16Chapter 3.2 --- Font Rasterization --- p.17Chapter 3.2.1 --- Outline Scaling --- p.17Chapter 3.2.2 --- Hintings --- p.17Chapter 3.2.3 --- Basic Rasterization Approach for Chinese Fonts --- p.18Chapter 3.3 --- Hintings --- p.20Chapter 3.3.1 --- Phase Control --- p.20Chapter 3.3.2 --- Auto-Hints --- p.21Chapter 3.3.3 --- Storage of Hintings Information in TrueType Font and Postscript Font --- p.22Chapter 4 --- An Improved Chinese Font Rasterizer --- p.24Chapter 4.1 --- Floating Point Avoidance --- p.24Chapter 4.2 --- Filling --- p.25Chapter 4.2.1 --- Filling with Horizontal Scan Line --- p.25Chapter 4.2.2 --- Filling with Vertical Scan Line --- p.27Chapter 4.3 --- Hintings --- p.30Chapter 4.3.1 --- Assumptions --- p.30Chapter 4.3.2 --- Maintaining Regular Strokes Width --- p.30Chapter 4.3.3 --- Maintaining Regular Spacing Among Strokes --- p.34Chapter 4.3.4 --- Hintings of Single Stroke Contour --- p.42Chapter 4.3.5 --- Storing the Hinting Information in Font File --- p.49Chapter 4.4 --- A Rasterization Algorithm for Printing --- p.51Chapter 4.4.1 --- A Simple Algorithm for Generating Smooth Characters --- p.52Chapter 4.4.2 --- Algorithm --- p.54Chapter 4.4.3 --- Results --- p.54Chapter 5 --- Experiments --- p.56Chapter 5.1 --- Apparatus --- p.56Chapter 5.2 --- Experiments for Investigating Rasterization Speed --- p.56Chapter 5.2.1 --- Investigation into the Effects of Features of Chinese Fonts on Rasterization Time --- p.56Chapter 5.2.2 --- Improvement of Fast Rasterizer --- p.57Chapter 5.2.3 --- Details of Experiments --- p.57Chapter 5.3 --- Experiments for Rasterization Speed of Font File with Hints --- p.57Chapter 6 --- Results and Conclusions --- p.58Chapter 6.1 --- Observations --- p.58Chapter 6.1.1 --- Relationship Between Time for Rasterization and Stroke Count --- p.58Chapter 6.1.2 --- Effects of Style --- p.61Chapter 6.1.3 --- Investigation into the Observed Relationship --- p.62Chapter 6.2 --- Improvement of the Improved Rasterizer --- p.64Chapter 6.3 --- Gain and Cost of Inserting Hints into Font File --- p.68Chapter 6.3.1 --- Cost --- p.68Chapter 6.3.2 --- Gain --- p.68Chapter 6.4 --- Conclusions --- p.69Chapter 6.5 --- Future Work --- p.69Appendi

Oriental fonts auto boldness.

by Lo I Fan.Thesis (M.Phil.)--Chinese University of Hong Kong, 1994.Includes bibliographical references.Chapter Chapter 1: --- IntroductionChapter 1.1 --- The Evolution of Fonts --- p.1Chapter 1.2 --- Bitmap Fonts --- p.2Chapter 1.3 --- Outline FontsChapter 1.3.1 --- Arc and Vector Form --- p.4Chapter 1.3.2 --- Spline Form --- p.4Chapter 1.3.3 --- Pros and Cons of Outline Fonts --- p.8Chapter 1.4 --- Examples of Outline FontsChapter 1.4.1 --- Adobe's PostScript --- p.9Chapter 1.4.2 --- Apple's and Microsoft TrueTypeChapter 1.4.2.1 --- Outline Representation --- p.10Chapter 1.4.2.2 --- Rasterisation --- p.12Chapter 1.4.2.3 --- Hinting --- p.13Chapter 1.5 --- Bold FontsChapter 1.5.1 --- Definition of Bold --- p.15Chapter 1.5.2 --- Definition of Auto B oldness --- p.16Chapter 1.5.3 --- Auto Boldness by Double Printing --- p.17Chapter 1.5.4 --- Auto Boldness by Multi-Master Technique --- p.18Chapter 1.6 --- Chinese FontsChapter 1.6.1 --- Chinese Character Sets --- p.19Chapter 1.6.2 --- The Subtleties of Chinese Fonts Auto Boldness --- p.21Chapter 1.7 --- Project Objective --- p.23Chapter 1.8 --- Goals --- p.23Chapter Chapter 2: --- Main Ideas of Chinese Font Auto BoldnessChapter 2.1 --- Prototype of Auto Boldness Driver --- p.24Chapter 2.2 --- Design Features of the Prototype Auto Boldness Driver --- p.25Chapter 2.3 --- Data Structure and Algorithm of Auto BoldnessChapter 2.3.1 --- Data Structure of TrueType Character Outline --- p.27Chapter 2.3.2 --- Algorithm of Auto Boldness --- p.28Chapter 2.3.3 --- Algorithm Description --- p.29Chapter 2.4 --- Component Font Auto Boldness --- p.35Chapter Chapter 3: --- Language of Auto BoldnessChapter 3.1 --- Enhancements of TrueType Engine to support Auto Boldness --- p.36Chapter 3.2 --- Symmetric Bold Instruction --- p.38Chapter 3.3 --- Rotate Bold Instruction --- p.47Chapter 3.4 --- Asymmetric B old Instruction --- p.50Chapter 3.5 --- Comparison of Bold Instructions --- p.54Chapter 3.6 --- Serif Accommodation Instruction --- p.55Chapter Chapter 4: --- Shape Parsing and Auto Bold Code GenerationChapter 4.1 --- Compilation Process and Auto Boldness --- p.62Chapter 4.2 --- Shape Lexical Analyzer --- p.64Chapter 4.3 --- Shape Token Attributes EvaluationChapter 4.3.1 --- line Token --- p.66Chapter 4.3.2 --- bezier2 Token --- p.67Chapter 4.3.3 --- sharp Token --- p.70Chapter 4.3.4 --- concave Token --- p.75Chapter 4.3.5 --- convex Token --- p.75Chapter 4.4 --- Scope of Shape Parsing --- p.76Chapter 4.5 --- Shape Parsing Mechanism --- p.77Chapter 4.6 --- Model Grammar RulesChapter 4.6.1 --- Grammar Rule Format --- p.81Chapter 4.6.2 --- Grammar Rule Item --- p.82Chapter 4.6.3 --- Grammar Rule Assignment --- p.83Chapter 4.6.4 --- Grammar Rule Condition --- p.83Chapter 4.7 --- Auto Boldness Code Generation --- p.84Chapter 4.8 --- Program Methodology of Prototype Auto Boldness Driver --- p.86Chapter Chapter 5: --- ConclusionsChapter 5.1 --- Work Achieved --- p.87Chapter 5.2 --- The Pros and Cons of Auto Boldness Algorithm --- p.88Chapter 5.3 --- Bold Quality Assessments --- p.91Chapter 5.3 --- Future Directions --- p.93ReferencesAppendix OneAppendix Tw