数学形态学和投影方差在文档图像倾斜校正中的应用

文档图像处理技术已取得很多进展,但是,对于倾斜图像的理解还存在困难。本文对文档图像的倾斜校正进行了研究,根据文档图像的结构特征,给出了基于数学形态学和投影方差相结合的算法,先使用数学形态学进行形态学运算,强化文档图像地结构特征,再使用细化算法,保证投影方差的准确性,最后使用投影方差,进行文档图像的倾斜校正。实验表明,本文提出的算法可以有效的应用于文档图像的倾斜校正。     

最大投影栅缝法检测文档图像倾斜角

针对由照相机扫描仪等文档获取设备拍摄的文档图像可能存在倾斜,进而导致光学字符识别（OCR）软件不能正确识别的情况,采用了一种以文档图像投影栅缝宽为目标函数的优化方法,栅缝宽最大值对应的投影角度的相反数即为文档图像的倾斜角。利用栅线宽函数扩大了检测范围,并提高了检测速度;利用反投影法和均布列预投影等方法,减少了计算量;利用二分法提高了算法的检测精度。通过一些包含少量插图的文档图像的倾斜角检测实验,验证了该方法的有效性。     

一种基于梯度差的文档图像文本行检测算法

在分析文本行特点的基础上,提出了一种利用水平梯度差进行文档图像的文本行检测算法。该算法首先对输入的文档图像进行水平梯度差计算,然后在局部窗口中求解最大梯度差并进行文本行区域的合并,通过非文本区域过滤来消除字符阶跃的跳变,最后将文档图像以行块的形式进行显示。实验结果表明,与投影算法进行相比,该算法对于行间距较小的文档图像的检测效果较好,时间复杂度较低并且检测的正确率较高,具有一定的鲁棒性和较好的适应性。     

一种基于投影算子的二值图像处理算法

二值图像的处理是图像内容分析领域的一个关键环节,处理效果严重影响图像分析的结果.以历史文档为处理对象,提出了基于投影的二值图像处理算法,通过对投影数组的处理实现灰度图像二值化阈值的合理选取、文档外边框的自动检测和图像污点的自动检测.实践证明该算法处理效果好,时间复杂度和空间复杂度低,是一种切实可行的好方法.     

基于投影直方图的文档图像快速匹配研究

如何实现文档图像间的快速匹配已成了人们日益关注的课题.针对文档图像的现有方法进行了研究,提出一种文档图像匹配的新方法.为进一步提高文档图像的匹配性能,结合用行列方向投影特征进行了文档图像的特征分析和提取工作.从而建立了特征相似匹配模t,在进行文档图像匹配时主要采用了平方差进行相似度度量和折半查找进行快速匹配的策略.实验表明随着二分法次数的增加,文档图像的相似度比较效率一定程度上得到很大的提高,匹配算法具有很好的杭倾斜和抗压缩效果.     

浅谈InDesign中可变数据的排版

在InDesign中处理可变数据时,主要处针对一些版式固定不变而内容可变的数据信息.创建模板文档后,将数据源文件导入模板文档中,使数据源和数据项产生链接.最后批量生成版式固定而内容不同合并文档,大大减少了在印刷中处理大量可变信息的工作量.     

基于对数极坐标变换的灰度投影稳像算法

常规的灰度投影稳像算法仅能对存在平移运动的图像进行稳像,而对具有旋转和尺度变化的图像无能为力。为了克服灰度投影算法这一局限性,提出了基于图像对数极坐标变换(LPM)的灰度投影稳像算法,把具有尺度和旋转变化的图像进行对数极坐标变换,这样笛卡儿坐标系下图像的旋转和尺度变化在对数极坐标系下表现为平移变化,从而能够用灰度投影算法处理旋转和尺度变化问题。     

基于神经网络的印刷体数学公式抽取方法*

在分析中文印刷文档版式及字符特征的基础上,提出了一种将决策树与BP神经网络相结合的数学公式抽取方法。采用决策树方法将孤立公式从文档中抽取出来,采用BP神经网络方法定位内嵌公式。实验表明,该抽取方法对中文文档的公式抽取具有较高的正确率、容错率和速率。     

基于投影的文档图像倾斜校正方法

针对文档图像的倾斜校正问题,提出了一种新的基于投影的文档图像倾斜角检测方法。首先采用一种高效的像素遍历算法对文档图像从不同角度进行投影,然后对投影数据进行累加求和,通过比较不同角度下的累加和来确定倾斜角度。该方法在投影过程中只需对文档图像进行极少部分投影,因而大大减少了运算量。基于该方法的特点,提出了由“粗”到“精”的投影策略,在确保检测精度的同时大幅提高了检测速度。实验结果表明,方法非常有效,可以获得很高的检测精度。     

基于纹理梯度的文档图像的倾斜校正方法

文档图像的倾斜校正在光学字符识别以及文档理解系统研究中有着重要的意义,国内外学者提出了很多实现方法,但各种方法都存在一定的局限性.通过对基于Hough变换和投影的倾斜校正方法的分析,提出了一种基于文档图像纹理方向的倾斜校正方法:文档图像中的文本纹理整体表现出一定的方向性,使文本图像能保持水平,通过纹理方向性分析,找出纹理的主要方向,进而求得文档的倾斜角度.通过一个复杂版面的二值文档图像的检测校正实验表明,方法提高了倾斜校正的校正范围,而且具有较好的有效性和鲁棒性.     

基于视窗的OCR页面图像倾斜检测方法

文档在扫描输入过程中，所生成的页面图像一般都存在一定的角度倾斜，当页面图像倾斜角度过大时，将对进一步的版面分析以及字符识别产生不良影响。为了快速准确地检测页面图像倾斜角度和降低计算量，提出了一种基于视窗变换的页面图像倾斜检测方法，该算法首先对视窗中的文字及图片的细节部分进行模糊，然后对其边沿进行直线拟合，以便快速检测页面图像倾斜角度。实验结果表明，该方法能快速准确地检测出各类页面图像的倾斜角度，并具有良好的适应性。     

Simple algorithm page layout analysis

An algorithm for page layout analysis (segmentation) is suggested in the paper. It allows whitespace between text blocks to be detected on a document page. The algorithm could be used in document analysis and recognition problems. In particular, it can be used for column recognition in multicolumn text and tables. The suggested algorithm is quite simple for implementation.     

Beyond document object detection: instance-level segmentation of complex layouts

Information extraction is a fundamental task of many business intelligence services that entail massive document processing. Understanding a document page structure in terms of its layout provides contextual support which is helpful in the semantic interpretation of the document terms. In this paper, inspired by the progress of deep learning methodologies applied to the task of object recognition, we transfer these models to the specific case of document object detection, reformulating the traditional problem of document layout analysis. Moreover, we importantly contribute to prior arts by defining the task of instance segmentation on the document image domain. An instance segmentation paradigm is especially important in complex layouts whose contents should interact for the proper rendering of the page, i.e., the proper text wrapping around an image. Finally, we provide an extensive evaluation, both qualitative and quantitative, that demonstrates the superior performance of the proposed methodology over the current state of the art.

     

多篇章非Manhattan版面层次布局模型和阅读顺序确定

针对报纸中多篇章非Manhattan版面引入PMRegion对象,并建立了满足约束的层次布局模型和层次空间关系,给出了版面逐层快速分解和阅读顺序无二义性确定算法．该算法已成功应用于专业排版系统,取得了满意的效果,对更深入研究文档图像理解具有十分重要的理论和实践意义．     

Transforming paper documents into XML format with WISDOM++

The transformation of scanned paper documents to a form suitable for an Internet browser is a complex process that requires solutions to several problems. The application of an OCR to some parts of the document image is only one of the problems. In fact, the generation of documents in HTML format is easier when the layout structure of a page has been extracted by means of a document analysis process. The adoption of an XML format is even better, since it can facilitate the retrieval of documents in the Web. Nevertheless, an effective transformation of paper documents into this format requires further processing steps, namely document image classification and understanding. WISDOM++ is a document processing system that operates in five steps: document analysis, document classification, document understanding, text recognition with an OCR, and transformation into HTML/XML format. The innovative aspects described in the paper are: the preprocessing algorithm, the adaptive page segmentation, the acquisition of block classification rules using techniques from machine learning, the layout analysis based on general layout principles, and a method that uses document layout information for conversion to HTML/XML formats. A benchmarking of the system components implementing these innovative aspects is reported. Received June 15, 2000 / Revised November 7, 2000     

图文互斥版面中文字阅读顺序的确定

图文互斥版面中确定文字的阅读顺序是排版及版面理解过程中的一个难点。尤其是中文等东方文字特有的分栏串文互斥,其空间关系的复杂性使得阅读顺序存在歧义。针对此问题,建立新的版面布局模型,并引入新的版面对象PMRegion。给出了版面逐层快速分解构造版面对象和基于有序树的阅读顺序确定算法。已成功运用于专业中日文排版系统,取得了满意的效果,并对更深入研究文档图像理解具有十分重要的理论和实践意义。     

新的文本图像倾斜检测及校正算法

在文档扫描过程中,文档可能会发生倾斜,而很多字符识别和布局分析算法都对倾斜十分敏感,文本图像的倾斜检测及校正就成为文档分析不可缺少的环节.提出了一种新的倾斜文本图像的校正方法,该方法首先获取文档图像的bounding box,以bounding box面积最小作为倾斜校正的最终目标,并使用遗传算法搜索该最小值.实验结果表明,该算法对倾斜角的检测具有较高的精确度.     

Document representation and its application to page decomposition

Transforming a paper document to its electronic version in a form suitable for efficient storage, retrieval, and interpretation continues to be a challenging problem. An efficient representation scheme for document images is necessary to solve this problem. Document representation involves techniques of thresholding, skew detection, geometric layout analysis, and logical layout analysis. The derived representation can then be used in document storage and retrieval. Page segmentation is an important stage in representing document images obtained by scanning journal pages. The performance of a document understanding system greatly depends on the correctness of page segmentation and labeling of different regions such as text, tables, images, drawings, and rulers. We use the traditional bottom-up approach based on the connected component extraction to efficiently implement page segmentation and region identification. A new document model which preserves top-down generation information is proposed based on which a document is logically represented for interactive editing, storage, retrieval, transfer, and logical analysis. Our algorithm has a high accuracy and takes approximately 1.4 seconds on a SGI Indy workstation for model creation, including orientation estimation, segmentation, and labeling (text, table, image, drawing, and ruler) for a 2550×3300 image of a typical journal page scanned at 300 dpi. This method is applicable to documents from various technical journals and can accommodate moderate amounts of skew and noise     

Document cleanup using page frame detection

When a page of a book is scanned or photocopied, textual noise (extraneous symbols from the neighboring page) and/or non-textual noise (black borders, speckles, ...) appear along the border of the document. Existing document analysis methods can handle non-textual noise reasonably well, whereas textual noise still presents a major issue for document analysis systems. Textual noise may result in undesired text in optical character recognition (OCR) output that needs to be removed afterwards. Existing document cleanup methods try to explicitly detect and remove marginal noise. This paper presents a new perspective for document image cleanup by detecting the page frame of the document. The goal of page frame detection is to find the actual page contents area, ignoring marginal noise along the page border. We use a geometric matching algorithm to find the optimal page frame of structured documents (journal articles, books, magazines) by exploiting their text alignment property. We evaluate the algorithm on the UW-III database. The results show that the error rates are below 4% each of the performance measures used. Further tests were run on a dataset of magazine pages and on a set of camera captured document images. To demonstrate the benefits of using page frame detection in practical applications, we choose OCR and layout-based document image retrieval as sample applications. Experiments using a commercial OCR system show that by removing characters outside the computed page frame, the OCR error rate is reduced from 4.3 to 1.7% on the UW-III dataset. The use of page frame detection in layout-based document image retrieval application decreases the retrieval error rates by 30%.     

基于最小二乘法的文档图像倾斜检测方法

在文档扫描过程中,输入的文档图像不可避免地会发生倾斜现象,而布局分析及字符识别算法对页面倾斜都十分敏感,因此倾斜检测和校正是文档分析预处理的重要环节。本文提出了一个基于最小二乘法的倾斜检测方法。它将字符连通区包围盒底边中心点作为特征点,利用文本行中特征点与基线的关系,将特征点用最小二乘法拟事出基线的方向,即为页面倾斜方向。同时,本文介绍了一种基于直线拟合的快速倾斜校正算法。实验证明,该算法速度快,准确度高。