现代藏文音节的划分与确定

在藏文信息处理中,实现藏文字符的字典排序是一个比较重要的问题,实现藏文字符排序的关键是准确地判定藏文音节。藏文音节的判定关键是组合字符的判定。判定组合字符的瓶颈是藏文字符编码的占位和不占位的判定。通过应用程序对藏文编码的占位和不占位的有效判定,即可找出组合字符。对找出组合字符的藏文字符可通过藏文正字法的限定对藏文音节进行有效的判定和音节组件的拆分,从而为藏文字符的字典排序奠定基础。     

多字体印刷藏文内容监测系统研究

针对藏文舆情分析中藏文印刷品和藏文图片内容无法自动监测的难题,文章在深入分析印刷藏文字符特征和藏文文本特点的基础上,提出支持多字体印刷藏文内容监测系统的实现方法,重点阐述藏文字符的特征提取、分类算法以及藏文文本内容监测方法。     

引入排序码实现藏文字符的排序

字符排序一般要遵循字典顺序并且需要为每个参与排序的字符赋予特定的排序码。藏文字符有两种编码方式：动态组合方式和静态组合方式,对于动态组合方式编码的藏文组合字符而言,仅仅根据构成它的字母来排序,则排序结果与字典顺序有较大的差异。文中分析了藏文字符的字典顺序,总结了藏文的字典顺序规律,提出了引入藏文字符集扩展集A中的UNICODE编码为排序码对藏文组合字符进行排序的观点,使得藏文组合字符的排序符合藏文字典顺序。     

基于极坐标变换的脱机手写藏文字符特征提取方法

为了使得藏文字符特征向量维数少、存储空间小、运算速度快及区分相似字能力高,基于图像投影法提出一种基于极坐标投影变换的脱机手写藏文字符特征提取方法。将脱机手写藏文字符图像进行预处理后得到大小、位置统一的二值图像,并定位二值图像的极点;求出二值图像中所有值为1的点对应的极坐标后将其进行投影变换得到投影向量,即作为脱机手写藏文字符的特征向量。使用KNN分类器对30 000个脱机手写藏文字进行实验,其中80%的样本作为训练数据,20%的样本作为测试数据,识别率达到了96.32%。结果表明该方法的有效性、计算简单及达到了较好的识别效果。     

藏文识别中相似字丁的区分研究

相似字丁多是藏文识别中的一大难点。本文通过对相似字丁类型的研究,以及印刷体藏文识别结果的统计分析,得到图形结构的分析与识别结果相吻合的结论。说明必须根据藏文字丁的结构特点,在字符归一化、特征选择方面进行特殊的处理,以实现藏文识别中相似字丁的区分。     

引入排序码实现藏文字符的排序

字符排序一般要遵循字典顺序并且需要为每个参与排序的字符赋予特定的排序码.藏文字符有两种编码方式:动态组合方式和静态组合方式,对于动态组合方式编码的藏文组合字符而言,仅仅根据构成它的字母来排序,则排序结果与字典顺序有较大的差异.文中分析了藏文字符的字典顺序,总结了藏文的字典顺序规律,提出了引入藏文字符集扩展集A中的UNICODE编码为排序码对藏文组合字符进行排序的观点,使得藏文组合字符的排序符合藏文字典顺序.     

论藏文的序性及排序方法

为解决藏文排序问题,本文提出藏文的构造序和字符序概念,并在此基础上提出解决藏文词典序的计算机方案。文章对各类藏文构造及字符进行了分析和赋值,给出了藏文计算机排序的技术流程图。     

藏文字符的向量模型及构件特征分析

藏文字属性分析是藏文信息处理的一项基础性工作,对藏文信息处理的研究和藏语文教学具有重要的参考价值及指导意义。藏文字是一种特殊的拼音文字,由1～7个基本构件横向和纵向拼接而成。因而藏文字符的属性包括其组成的构件及其构件的位置特征,以及藏文字的使用频度、结构、字长等属性特征。该文通过分析藏文字的结构,分别建立了藏文字及藏文字符串的向量模型VMTT、VMTS和藏文字符串的稀疏域模型SLM,并在向量模型和稀疏域模型上研究了藏文字符的构件特征。     

基于BP网络的木刻藏文经书文字识别研究

木刻藏文经书文中出现字符间粘连、断裂、遮挡现象严重,为识别带来极大的困难。在字符切分、特征提取等文字识别方法基础上,增加了基于BP网络的训练方法,通过对大量的木刻藏文经书字符的训练,修正了数据,收敛了识别结果。实验结果显示,此方法有助于提高木刻藏文经书的文字识别正确率。     

残缺藏文识别与补全系统

藏文识别补全技术使许多珍贵文献得以保存并传承,故在此采用了基于局部二值模式(LBP)直方图特征和可编程计数器阵列(PCA)方法的特征提取方法提取藏文字符特征,在对藏文字符分类识别后用TensorFlow框架训练识别藏文,同时使用两级生成式对抗网络进行文字补全。用户上传的残缺藏文图片可以通过5G网络连接到云端服务器,经过模型计算后返回最终分析结果。     

文档识别中误切分字符拒识问题的研究

自动文档识别中字切分算法如果仅仅依靠大小位置等度量信息,很容易产生误切分图像块,需要字符分类器给出一定的反馈才能准确切分,为此提出了一个新的拒识算法,目标是尽可能准确地拒识非法字符。该文分析了基于距离的分类器的置信度和广义置信度,在此基础上改进了常用的广义置信度映射函数,并设计了一个基于样本学习的拒识规则,提高了拒识算法的适应性。在中日韩三种文档样本上的实验表明,该文算法明显改善了系统性能,对于较低质量的印刷文本识别具有一定的普遍意义。     

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     

复杂彩色文本图像中字符的提取

从复杂彩色文本图像中提取和识别字符已经成为一个既困难又有趣的问题。本文给出了一个具有创新性和实用性的区域生长算法用于彩色图像的分割:彩色图像游程邻接算法CRAG(color run-length adjacency graph algorithm)。我们将该算法用于彩色文本图像,首先得到图像的彩色连通域,再对这些连通域的平均颜色进行颜色聚类,可得到若干个聚类中心,然后根据不同的颜色中心将图像分为相应的彩色层面,最后通过连通域分析判断所需的文字层。该生长算法修改并扩展了传统的BAG算法,并将其运用于彩色印刷体文本图像中,充分利用了彩色图像的颜色和位置信息。实验结果表明新的方法能很好的从彩色印刷图像中提取多种常见的艺术字,并具有较高的提取速度,同时保留了文字和背景图像的原始色彩,便于将来的图像恢复。     

Text detection and recognition in images and video frames

This paper presents a new method for detecting and recognizing text in complex images and video frames. Text detection is performed in a two-step approach that combines the speed of a text localization step, enabling text size normalization, with the strength of a machine learning text verification step applied on background independent features. Text recognition, applied on the detected text lines, is addressed by a text segmentation step followed by an traditional OCR algorithm within a multi-hypotheses framework relying on multiple segments, language modeling and OCR statistics. Experiments conducted on large databases of real broadcast documents demonstrate the validity of our approach.     

OCR error correction using correction patterns and self-organizing migrating algorithm

Optical character recognition (OCR) systems help to digitize paper-based historical achieves. However, poor quality of scanned documents and limitations of text recognition techniques result in different kinds of errors in OCR outputs. Post-processing is an essential step in improving the output quality of OCR systems by detecting and cleaning the errors. In this paper, we present an automatic model consisting of both error detection and error correction phases for OCR post-processing. We propose a novel approach of OCR post-processing error correction using correction pattern edits and evolutionary algorithm which has been mainly used for solving optimization problems. Our model adopts a variant of the self-organizing migrating algorithm along with a fitness function based on modifications of important linguistic features. We illustrate how to construct the table of correction pattern edits involving all types of edit operations and being directly learned from the training dataset. Through efficient settings of the algorithm parameters, our model can be performed with high-quality candidate generation and error correction. The experimental results show that our proposed approach outperforms various baseline approaches as evaluated on the benchmark dataset of ICDAR 2017 Post-OCR text correction competition.

     

笔迹鉴别的字符予处理与匹配

笔迹鉴别多用匹配方法比较字并的书写风格, 而字符困像的预处理和归一化对匹配是昨常重要的本文介绍笔迹鉴别的字符图像预处理和一种形状匹配方法。预处理主要介绍二值图像的噪声消除和归一化方法。嗓声消除的方法是平滑、轮廓跟踪和填充为保持字符中的书写特征, 点阵的归一化是线性的, 但字符位五和尺度的确定昨常重要。本文给出了三种归一化方法四边定界法、重心对准法和单边定界法, 并在此基拙上用图像匹配方法进行书写人识别的实验。匹配方法是通过距离变换快速实现的。实验结果表明, 重心对·准归一化最适合于笔迹鉴别问题, 距离变换匹配得到的识别率也比较令人满意     

Text detection in chart images

Common OCR (Optical Character Recognition) systems fail to detect and recognize small text strings of few characters, in particular when a text line is not horizontal. Such text regions are typical for chart images. In this paper we present an algorithm that is able to detect small text regions regardless of string orientation and font size or style. We propose to use this algorithm as a preprocessing step for text recognition with a common OCR engine. According to our experimental results, one can get up to 20 times better text recognition rate, and 15 times higher text recognition precision when the proposed algorithm is used to detect text location, size and orientation, before using an OCR system. Experiments have been performed on a benchmark set of 1000 chart images created with the XML/SWF Chart tool, which contain about 14000 text regions in total.     

Text detection on charts and graphs

Current Optical Character Recognition (OCR) systems are not capable of detection and recognition of detached words on an image, especially if the text is not located horizontally. Such text blocks are typical of charts and graphs. In this paper an algorithm of detection of small text blocks with arbitrary orientation, color, style, and font size, which can be used for text localization before application of arbitrary character recognition system, is proposed. According to the experimental results, the use of the proposed algorithm for determination of the location and orientation of text blocks on charts and graphs and the transmission of this information to text recognition system allow increasing the fullness by 20 times and the text recognition precision by 15 times. The experiments were carried out on a test collection of 1000 charts containing about 14 000 text blocks, which was created by means of the XML/SWF Chart tool.     

Font adaptive word indexing of modern printed documents

We propose an approach for the word-level indexing of modern printed documents which are difficult to recognize using current OCR engines. By means of word-level indexing, it is possible to retrieve the position of words in a document, enabling queries involving proximity of terms. Web search engines implement this kind of indexing, allowing users to retrieve Web pages on the basis of their textual content. Nowadays, digital libraries hold collections of digitized documents that can be retrieved either by browsing the document images or relying on appropriate metadata assembled by domain experts. Word indexing tools would therefore increase the access to these collections. The proposed system is designed to index homogeneous document collections by automatically adapting to different languages and font styles without relying on OCR engines for character recognition. The approach is based on three main ideas: the use of self organizing maps (SOM) to perform unsupervised character clustering, the definition of one suitable vector-based word representation whose size depends on the word aspect-ratio, and the run-time alignment of the query word with indexed words to deal with broken and touching characters. The most appropriate applications are for processing modern printed documents (17th to 19th centuries) where current OCR engines are less accurate. Our experimental analysis addresses six data sets containing documents ranging from books of the 17th century to contemporary journals.     

藏文内码扩展体系

针对藏文编码字符集的基本集和辅助集建立在不同平面、编码体系不同所存在的问题,本文提出建立藏文内码扩展体系,给出了藏文合成、生成、分解的规则和方法:通过内码转换表合成藏文藏文内字,实现基本集与辅助集的信息交换;通过构件集,生成规范、标准的藏文外字,满足藏文编码字符集开放性的需要。并且,向上,在字汇一级,兼容UCS ;向下,与GB2312的事实上的内码标准兼容,是一个全藏文编码体系。 作者建议在UCS基本平面的拼音文字区建立内码扩充体系。