Quality enhancements of digital outline fonts

Digital fonts are easily available today and their use has become widespread. Nevertheless, the visual quality of printed and displayed text and the technical quality of digital typefaces are often unsatisfactory. The foremost reason for this situation is that font manufacturing systems manipulate font representations through low-level operations. In this paper we examine several aspects of font quality, and present a method for enhancing the quality of a typeface given in today's standard representation, the outline (boundary) representation. The method is based on the usage of high-level typographic features, and includes enhancements to both individual glyphs and complete fonts. Glyph enhancements include elimination of redundant points, insertion of essential points, corrections to contour orientations and inter-penetrations, and vertical and horizontal coherence. Font enhancements include regularization of font weights and sizes, serif unification, and feature extraction and discrimination. Our method is comprised of several algorithms that have been implemented in a commercial font manufacturing system, achieving enhanced quality of the fonts produced.     

VxWorks5.5 平台下矢量字体显示的实现

VxWorks5.5采用点阵字库实现字体显示,这种字库设计简洁,应用广泛,但一个字库只能对应一种字体的一种大小,在不确定使用何种字体的情况下,这种传统的字体显示方式便不能够满足需求。通过使用TrueType字库和FreeType字体引擎相结合的方式,能实现多种字体、任意大小的显示功能。主要介绍了TrueType、FreeType技术的基本原理,以及在VxWorks5.5下如何将WindML、FreeType和TrueType三者相结合实现矢量字体显示的方法。     

Optical font recognition using typographical features

A new statistical approach based on global typographical features is proposed to the widely neglected problem of font recognition. It aims at the identification of the typeface, weight, slope and size of the text from an image block without any knowledge of the content of that text. The recognition is based on a multivariate Bayesian classifier and operates on a given set of known fonts. The effectiveness of the adopted approach has been experimented on a set of 280 fonts. Font recognition accuracies of about 97 percent were reached on high-quality images. In addition, rates higher than 99.9 percent were obtained for weight and slope detection. Experiments have also shown the system robustness to document language and text content and its sensitivity to text length     

KAFD Arabic font database

Font recognition is useful for improving optical text recognition systems’ accuracy and time, and to restore the documents’ original formats. This paper addresses a need for Arabic font recognition research by introducing an Arabic font recognition database consisting of 40 fonts, 10 sizes (ranging from 8 to 24 points) and 4 styles (viz. normal, bold, italic, and bold–italic). The database is split into three sets (viz. training, validation, and testing). The database is freely available to researchers.¹ Moreover, we introduce a baseline font recognition system for benchmarking purposes, and report identification rates on our KAFD database and the Arabic Printed Text Image (APTI) database with 20 and 10 fonts, respectively. The best recognition rates are achieved using log-Gabor filters.     

基于汉字字体结构认知计算的多意象预测模型

为了揭示汉字字体与受众的情感意象之间的内在关系，从认知计算的角度出发， 探索构建一种“设计特征-结构指标-意象”的灰箱关联模型，以其预测汉字字体的多个意象。首 先依据认知计算的原理将字体结构规则抽象为知识，运用产生式规则将字体结构知识进行定量 描述，提出字重、重心、字面、字怀 4 个字体结构指标的认知计算公式，将无序的形态信息转 化为结构化的有序信息。然后基于汉字字体意象认知系统的非线性耦合的特点，发展出一种运 用多输出最小二乘支持向量回归机(MLS-SVR)进行汉字字体多意象预测的方法。将该方法对汉 字字体的 3 个意象进行预测，实验结果表明其具有良好的预测效果和精度。该模型可作为字体 智能设计系统的适应度函数，为发展字体智能设计提供有益的参考。     

High accuracy optical character recognition using neural networkswith centroid dithering

Optical character recognition (OCR) refers to a process whereby printed documents are transformed into ASCII files for the purpose of compact storage, editing, fast retrieval, and other file manipulations through the use of a computer. The recognition stage of an OCR process is made difficult by added noise, image distortion, and the various character typefaces, sizes, and fonts that a document may have. In this study a neural network approach is introduced to perform high accuracy recognition on multi-size and multi-font characters; a novel centroid-dithering training process with a low noise-sensitivity normalization procedure is used to achieve high accuracy results. The study consists of two parts. The first part focuses on single size and single font characters, and a two-layered neural network is trained to recognize the full set of 94 ASCII character images in 12-pt Courier font. The second part trades accuracy for additional font and size capability, and a larger two-layered neural network is trained to recognize the full set of 94 ASCII character images for all point sizes from 8 to 32 and for 12 commonly used fonts. The performance of these two networks is evaluated based on a database of more than one million character images from the testing data set     

基于经验模式分解的汉字字体识别方法

提出了一种基于经验模式分解(empirical mode decomposition,简称EMD)的汉字字体识别方法.通过对大量汉字字体的研究比较,选取了能反映汉字字体基本特征的8种基本笔画.以这8种汉字笔画为模板,在汉字文档图像块中随机地抽取笔画信息,形成笔画特征序列.通过对笔画特征序列作EMD分解,提取每个笔画特征序列的高频能量,并结合汉字文档图像块的平均灰度,形成字体识别的一个9维特征.     

Font recognition based on global texture analysis

We describe a novel texture analysis-based approach toward font recognition. Existing methods are typically based on local typographical features that often require connected components analysis. In our method, we take the document as an image containing some specific textures and regard font recognition as texture identification. The method is content-independent and involves no detailed local feature analysis. Experiments are carried out by using 14000 samples of 24 frequently used Chinese fonts (six typefaces combined with four styles), as well as 32 frequently used English fonts (eight typefaces combined with four styles). An average recognition rate of 99.1 percent is achieved. Experimental results are also included on the robustness of the method against image degradation (e.g., pepper and salt noise) and on the comparison with existing methods     

Handling fonts in electronic publishing systems

An electronic publishing system (EPS) uses a variety of different typefaces. Implementations of typefaces are commonly referred to as fonts. Typically, sets of fonts used in an EPS come from different sources, i.e. vendors, and they use different encoding mappings, e.g. ASCII, XC1, EBCIDIC, etc. Furthermore, a logical font which the user uses or sees may be based on a different number of machine (printer or typesetter) fonts. In an EPS system fonts must be handled in such a way to shield the user from such idiosyncrasies and also to make tasks of document composition and typesetting as efficient as possible. This paper treats such problems and proposes a solution for handling of fonts for the XPS-700 Xerox Publishing System.     

曲线轮廓汉字修饰晕线的自动生成

汉字修饰晕线的应用十分广泛，但制作难度很大，本文叙述了面向多种轮廓汉字字体的两种修饰晕线的自动生成方法，讨论了该自动生成方法的优点，还介绍了该方法在一个计算机辅助言行设计系统中的应用。     

Analysis of the representative shape classification features of Hangul font services

There are various design guidelines for choosing Roman font that can strengthen the effectiveness and significance of visual delivery by using changes in font shape. However, it is not easy to apply Roman font guide directly to Hangul. The structural characteristics of Hangul fonts may be the cause. It means that Hangul fonts are composed of vowels and consonants with a structure that consists of an initial, middle, and final sound; there are shape properties that differ from existing Roman font design properties. Therefore, the representative shape features must also be defined differently. Moreover, although there are Korea standards related to shape features, it is impossible to systematize them using a classification standard with a visual or cognitive basis. This study identified 10 representative shape features based on a statistical analysis of 100 Hangul fonts and 75 shape features presented by previous studies. The validity of utilizing Hangul fonts with respect to the newly presented representative shape features was verified in this investigation. The shape features presented in this study will be proposed as a shape feature standard for Hangul fonts and may be used in the recommendation system.     

毛笔书法临帖的计算机评价

摘 要：针对书法初学者学习的特点,基于计算机辅助技术,根据适用于初学者的书法水平评价标准,提出相似度的算法。将初学者的临帖作品通过扫描或拍照后输入计算机,利用计算机图像处理技术进行临帖作品预处理。通过图像骨架、不变矩、灰度投影等方法,对临帖字体的结体特征、点画形态和章法布局的特征进行描述。对临帖字体与原碑帖字体进行相似度计算,实现对书法初学者临帖作品综合评价的辅学系统。 关 键 词：计算机应用;书法教育;毛笔书法;临帖;计算机评价     

嵌入式系统中的TrueType字体驱动

Freetype库应用于嵌入式高清电影播放机时,存在srt字幕显示滞后的问题。针对该缺陷,给出一种实现TrueType字体驱动的方法,建立字体本地索引,将TrueType字体文件中必要的数据导入内存,建立字体glyf内存数据库。测试表明,该驱动稳定可靠,适用于资源受限的嵌入式平台。     

基于Authorware平台课件中特殊字体应用的研究

当前,Authorware是一个广泛使用的多媒体课件著作工具。在多媒体CAI课件开发时，有时会用到特殊字体，由于它是非系统自带的字体．在使用方法上就有所不同，否则就达不到想要的字体效果。本文从多媒体文本素材作用介绍入手，利用Authorware著作平台探讨了特殊字体在课件中应用的两种方法。     

Locating text in a bounded connected region

The paper considers the problem of fitting the image of a text with letters of different sizes and different fonts in a given bounded connected region of arbitrary configuration on a plane.Translated from Kibernetika i Sistemnyi Analiz, No. 4, pp. 130–139, July–August, 1991.     

基于Authorware平台课件中特殊字体应用的研究

当前,Authorware是一个广泛使用的多媒体课件著作工具。在多媒体CAI课件开发时,有时会用到特殊字体,由于它是非系统自带的字体,在使用方法上就有所不同,否则就达不到想要的字体效果。本文从多媒体文本素材作用介绍入手,利用Authorware著作平台探讨了特殊字体在课件中应用的两种方法。     

A Web based system to capture outlines of Arabic fonts

This paper presents a Web based system for the automatic capture of Arabic fonts. The system is based on an algorithm, which incorporates various features including the detection of boundary, discovering corner and break points, and fitting the curve. The work done, in this paper, fully automate the process and produces best optimal results. Moreover the automated system developed is deployed over the Web using client/server model and state of the art technology.     

基于Adaboost算法的场景中文文本定位

提出了一种基于Adaboost算法的场景中文文本定位的新方法。首先利用边缘特征进行文本区域的检测,即对数字图像进行边缘提取、二值化处理,然后通过连通域分析去除明显的非字符连通域,并获得候选的文本区域。对场景中文文本区域进行分析,提取了场景中文文本的4类特征,并利用这4类特征经过分类与回归决策树构造了Adaboost强分类器。将候选文本区域送入强分类器,得到正确的文本区域。实验结果表明方法不仅对场景文本图像中字体、大小和颜色多变的文本具有很好的定位效果,而且具有很高的召回率和准确率。     

利用VC实现汉字字模提取及串行通信

目前,单片机广泛地应用于许多实时工业控制和数据采集系统,极大地提高了工业设备的智能化。在单片机加显示器件构成的小系统中,需要经常用到汉字显示技术。如何得到汉字字模是汉字显示技术中首先必须解决的问题。并且,由于单片机的硬件资源很有限,寻址和计算能力都远低于PC机,显示汉字受到限制。因此将这个工作交给PC机,让PC机访问汉字库并提取汉字字模,再通过串口将汉字字模发送给单片机。通过该方法不仅极大地提高了汉字显示的效率还保证了可靠性!