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.     

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     

Substituting outline fonts for bitmap fonts in archived PDF files

As collections of archived digital documents continue to grow the maintenance of an archive, and the quality of reproduction from the archived format, become important long‐term considerations. In particular, Adobe's portable document format (PDF) is now an important ‘final form’ standard for archiving and distributing electronic versions of technical documents. It is important that all embedded images in the PDF, and any fonts used for text rendering, should at the very minimum be easily readable on screen. Unfortunately, because PDF is based on PostScript technology, it allows the embedding of bitmap fonts in Adobe Type 3 format as well as higher‐quality outline fonts in TrueType or Adobe Type 1 formats. Bitmap fonts do not generally perform well when they are scaled and rendered on low‐resolution devices such as workstation screens. The work described here investigates how a plug‐in to Adobe Acrobat enables bitmap fonts to be substituted by corresponding outline fonts using a checksum matching technique against a canonical set of bitmap fonts, as originally distributed. The target documents for our initial investigations are those PDF files produced by LATEX systems when set up in a default (bitmap font) configuration. For all bitmap fonts where recognition exceeds a certain confidence threshold replacement fonts in Adobe Type 1 (outline) format can be substituted with consequent improvements in file size, screen display quality and rendering speed. The accuracy of font recognition is discussed together with the prospects of extending these methods to bitmap‐font PDF files from sources other than LATEX. Copyright © 2003 John Wiley & Sons, Ltd.     

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     

文字相关的单字符字体识别方法

汉字具有丰富的字体类型,并且不同的字体在汉字结构上有显著的不同,现在的OCR技术侧重字的识别,而对字体识别的关注较少。提出文字相关的单字符字体识别方法,利用文字相关的先验信息及字体结构特征,对字体的相似性度量采用向量空间模型,并针对常用66款简体字进行实验,得到了较好的平均识别率。     

基于深度学习的手写书法字体识别算法

为解决因手写书法作品种类繁多而识别困难的问题,降低人们观赏书法的门槛,本文提出了基于深度学习的手写书法字体识别算法.识别过程中首先使用投影法等图像处理方法对书法作品图像中的汉字进行定位和分割,然后分别利用GoogLeNet Inception-v3模型和ResNet-50残差网络进行书体风格识别和字形识别.实验结果表明...     

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     

高性能的多体印刷英文识别系统的实现

提高低质量文本图像的识别率是现今文字识别研究的重要方向。文章对倾斜文本行的切分算法,断裂、粘连、交叠字符的切分算法以及后处理作了较为深入的研究,提出一些新的算法。该系统能够识别多达260种字体,包括黑体、斜体等字体,对训练集的识别率达到98.5%,并在实际应用中取得了良好效果。     

鲁棒的多体印刷英文识别系统的实现

文章讨论了设计一个实用的多体英文识别系统中解决的主要问题。该系统能识别多达260种字体,包括斜体和黑体等字体,对训练集的识别率达到99%,对实际文本测试的错误率比TH-OCR2000低56%。文章详细阐述了文本行字切分,特征提取和分类器设计,以及后处理所使用的常用技术,对各种技术的特点进行了分析和比较,并提出了一些新的技术。文章对于OCR系统的设计具有一定的指导意义。     

工程图纸自动输入字符识别的二维隐性马尔可夫模型方法

在分析了语音识别的一维马尔可夫模型(1D-HMM)方法的基础上,采用二维马尔可夫模型(2D-HMM)方法识别工程图纸中的各类印刷体汉字、较规范的手写仿宋体汉字、英文及阿拉伯数字,适用于多字体、倾斜字符识别等情形,抗噪声能力强,取得了较高的识别率.     

基于形态学的新的汉字字形自动生成方法

电子印刷,桌面出版,艺术,广告等领域对不同风格汉字的需求,迫切需求一种自动的汉字字形生成方法。传统方法只适用于两种字形相关不大的字体进行合成,并且需人干预,本文通过对字体的凸剖分,并建立两种不同字体的子凸集映射,提出了一种全新的基于形态变换的汉字形自动生成方法,     

Farsi/Arabic handwritten digit recognition based on ensemble of SVD classifiers and reliable multi-phase PSO combination rule

The problem of handwritten digit recognition has long been an open problem in the field of pattern classification and of great importance in industry. The heart of the problem lies within the ability to design an efficient algorithm that can recognize digits written and submitted by users via a tablet, scanner, and other digital devices. From an engineering point of view, it is desirable to achieve a good performance within limited resources. To this end, we have developed a new approach for handwritten digit recognition that uses a small number of patterns for training phase. To improve the overall performance achieved in classification task, the literature suggests combining the decision of multiple classifiers rather than using the output of the best classifier in the ensemble; so, in this new approach, an ensemble of classifiers is used for the recognition of handwritten digit. The classifiers used in proposed system are based on singular value decomposition (SVD) algorithm. The experimental results and the literature show that the SVD algorithm is suitable for solving sparse matrices such as handwritten digit. The decisions obtained by SVD classifiers are combined by a novel proposed combination rule which we named reliable multi-phase particle swarm optimization. We call the method “Reliable” because we have introduced a novel reliability parameter which is applied to tackle the problem of PSO being trapped in local minima. In comparison with previous methods, one of the significant advantages of the proposed method is that it is not sensitive to the size of training set. Unlike other methods, the proposed method uses just 15 % of the dataset as a training set, while other methods usually use (60–75) % of the whole dataset as the training set. To evaluate the proposed method, we tested our algorithm on Farsi/Arabic handwritten digit dataset. What makes the recognition of the handwritten Farsi/Arabic digits more challenging is that some of the digits can be legally written in different shapes. Therefore, 6000 hard samples (600 samples per class) are chosen by K-nearest neighbor algorithm from the HODA dataset which is a standard Farsi/Arabic digit dataset. Experimental results have shown that the proposed method is fast, accurate, and robust against the local minima of PSO. Finally, the proposed method is compared with state of the art methods and some ensemble classifier based on MLP, RBF, and ANFIS with various combination rules.     

Region growing based segmentation algorithm for typewritten and handwritten text recognition

This paper presents a new technique of high accuracy to recognize both typewritten and handwritten English and Arabic texts without thinning. After segmenting the text into lines (horizontal segmentation) and the lines into words, it separates the word into its letters. Separating a text line (row) into words and a word into letters is performed by using the region growing technique (implicit segmentation) on the basis of three essential lines in a text row. This saves time as there is no need to skeletonize or to physically isolate letters from the tested word whilst the input data involves only the basic information—the scanned text. The baseline is detected, the word contour is defined and the word is implicitly segmented into its letters according to a novel algorithm described in the paper. The extracted letter with its dots is used as one unit in the system of recognition. It is resized into a 9 × 9 matrix following bilinear interpolation after applying a lowpass filter to reduce aliasing. Then the elements are scaled to the interval [0,1]. The resulting array is considered as the input to the designed neural network. For typewritten texts, three types of Arabic letter fonts are used—Arial, Arabic Transparent and Simplified Arabic. The results showed an average recognition success rate of 93% for Arabic typewriting. This segmentation approach has also found its application in handwritten text where words are classified with a relatively high recognition rate for both Arabic and English languages. The experiments were performed in MATLAB and have shown promising results that can be a good base for further analysis and considerations of Arabic and other cursive language text recognition as well as English handwritten texts. For English handwritten classification, a success rate of about 80% in average was achieved while for Arabic handwritten text, the algorithm performance was successful in about 90%. The recent results have shown increasing success for both Arabic and English texts.     

Highly Accurate Recognition of Handwritten Arabic Decimal Numbers Based on a Self-Organizing Maps Approach

Handwritten numeral recognition is one of the most popular fields of research in automation because it is used in many applications. Indeed, automation has continually received substantial attention from researchers. Therefore, great efforts have been made to devise accurate recognition methods with high recognition ratios. In this paper, we propose a method for integrating the correlation coefficient with a Self-Organizing Maps (SOM)-based technique to recognize offline handwritten Arabic decimal digits. The simulation results show very high recognition rates compared with the rates achieved by other existing methods.     

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。     

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。     

数字化藏文古籍中多样性字体的实现方法研究

After the computer became a tool for data sharing and information exchange, the unified computer font has made the text lose the diversity and discreteness of handwriting. Text is the crucial factor for the spread of culture and civilization. Many electronic books have lost the characteristic fonts with cultural background and historical significance in the original ancient books after the digitalization. One example is the sculpted typeface with diversity and discreteness that can be called a Tibetan culture. In order to solve this problem, a research method of digitizing engraving fonts in ancient Tibetan books is proposed. Firstly, the projection method and the connected domain method are used to segment the ancient book image. Secondly, the GIST feature algorithm is used to realize the image text recognition. Thirdly, the SIFT feature algorithm is used to implement the image font style classification, and diffe rent styles of carved fonts in the ancient books are obtained. A font diversity expression algorithm is proposed to realize the diversity and discreteness of carved fonts in ancient books. The purpose of the research is to achieve the inheritance and protection of engraving fonts, which has important cultural research and inheritance significance.