e-PCP: A robust skew detection method for scanned document images

We present here an enhanced algorithm (e-PCP) for skew detection in scanned documents, based on the work on Piecewise Covering by Parallelogram (PCP) for robust determination of skew angles [C.-H. Chou, S.-Y. Chu, F. Chang, Estimation of skew angles for scanned documents based on piecewise covering by parallelograms, Pattern Recognition 40 (2007) 443-455]. Our algorithm achieves even better robustness for detection of skew angle than the original PCP algorithm. We have shown accurate determination of skew angles in document images where the original PCP algorithm fails. Further, the increased robustness of performance is achieved with reduced number of computation compared to the originally proposed PCP algorithm. The e-PCP algorithm also outputs a confidence measure which is important in automated systems to filter cases where the estimated skew angle may not be very accurate and thus can be handled by manual intervention. The proposed algorithm was tested extensively on all categories of real time documents and comparisons with PCP method is also provided. Useful details regarding faster execution of the proposed algorithm is provided in Appendix.     

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。     

基于改进Hough变换的文本图像倾斜校正方法

文本图像在扫描输入时产生的倾斜现象会对后续的页面分割及光学字符识别（OCR）处理产生很大的影响，而传统的标准Hough变换虽然具有对噪声不敏感，不依赖于直线连续性的优点，但由于计算量偏大，速度慢，在实用时有较大的局限性。提出一种基于改进的Hough变换的文本图像倾斜校正方法，通过在变分辨率图像中采用不同的文本方向提取算法，及选择合理投票门限等改进Hough变换的措施，减小了由图像区域及文字笔画粗细所产生的对倾角判定的不利影响，并使用基于偏移值的方法实现页面倾斜的快速校正。实验结果表明，该算法实现了大范围高精度的文本图像倾角的快速检测，具有较强的实用性。     

基于数学形态学的文档图像倾斜校正算法

随着信息采集技术的不断发展,文档图像在信息的数字化管理中越来越重要.对文档图像的倾斜校正进行了研究,给出了基于数学形态学和Hough变换相结合的算法,进行文档图像的倾斜校正,同时将算法应用于印刷体和手写体的文档图像.实验表明该算法可以有效应用于两种文档图像的倾斜校正.     

Document skew estimation without angle range restriction

The existing skew estimation techniques usually assume that the input image is of high resolution and that the detectable angle range is limited. We present a more generic solution for this task that overcomes these restrictions. Our method is based on determination of the first eigenvector of the data covariance matrix. The solution comprises image resolution reduction, connected component analysis, component classification using a fuzzy approach, and skew estimation. Experiments on a large set of various document images and performance comparison with two Hough transform-based methods show a good accuracy and robustness for our method. Received October 10, 1998 / Revised version September 9, 1999     

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.     

OMR图像倾斜矫正与分割

提出一种采用Hough变换进行OMR图像倾斜矫正的方法,该方法不必识别定位标记位置,具有很好的抗噪能力。为克服Hough变换计算量大的缺点,采用图像子抽样生成低辨率图像进行Hough变换,提高了算法效率。同时,提出一种快速游程段中心迭代算法分割图像,结合Hough变换,可快速准确地实现OMR图像的倾斜矫正与分割。     

Convex hull based skew estimation

Skew estimation and page segmentation are the two closely related processing stages for document image analysis. Skew estimation needs proper page segmentation, especially for document images with multiple skews that are common in scanned images from thick bound publications in 2-up style or postal envelopes with various printed labels. Even if only a single skew is concerned for a document image, the presence of minority regions of different skews or undefined skew such as noise may severely affect the estimation for the dominant skew. Page segmentation, on the other hand, may need to know the exact skew angle of a page in order to work properly. This paper presents a skew estimation method with built-in skew-independent segmentation functionality that is capable of handling document images with multiple regions of different skews. It is based on the convex hulls of the individual components (i.e. the smallest convex polygon that fully contains a component) and that of the component groups (i.e. the smallest convex polygon that fully contain all the components in a group) in a document image. The proposed method first extracts the convex hulls of the components, segments an image into groups of components according to both the spatial distances and size similarities among the convex hulls of the components. This process not only extracts the hints of the alignments of the text groups, but also separate noise or graphical components from that of the textual ones. To verify the proposed algorithms, the full sets of the real and the synthetic samples of the University of Washington English Document Image Database I (UW-I) are used. Quantitative and qualitative comparisons with some existing methods are also provided.     

Digit recognition in a natural scene with skew and slant normalization

This paper proposes a method to recognize digits in a natural scene, such as telephone numbers on a signboard. Candidate regions of digits are extracted from an image through contrast enhancement, edge extraction, and labeling. Since the target text patterns are in a 3D space, unlike traditional character recognition problems, we have to deal with the image transformation effect due to the orientation in the 3D space and projection. We have to cancel the effect as much as possible before digit recognition. In our method, the image transformation effect is modeled as skew and slant. In the proposed method, simplified Hough transform is used for the skew normalization. After the skew normalization, the remaining effect of image transformation is corrected by circumscribing digit patterns with tilted rectangles and affine transformation. In experiments, we tested a total of 1,332 images of signboards with 11,939 digits. We obtained a digit extraction rate of 99.2% and a correct digit recognition rate of 98.8%.Received: 15 December 2003, Accepted: 21 October 2004, Published online: 2 February 2005     

基于直线拟合的文本倾斜检测算法

在文本扫描输入的过程中,文本图像不可避免地会发生倾斜,而布局分析及字符识别算法对页面倾斜十分敏感,因此倾斜检测和校正是文档分析预处理中的重要环节。提出了一个基于直线拟合的倾斜检测方法,它对文本图像二值化、分块,进行Fourier变换获得Fourier光谱,提取Fourier光谱中反映倾斜角的特征点,然后对特征点进行拟合处理,最后获得页面倾斜角。实验结果表明,该方法能够精确检测文本的倾斜角度,并且不受文本布局、行间距以及字体的影响。     

票据图像预处理方法的研究

在文档影像的自动处理中,去黑边和倾斜校正是影像预处理的首要环节.该文提出了变黑边模板的概念和基于区域填充的黑边去除算法.对于图像的倾斜校正,我们提出了基于方向投影的表格线检测方法,并由此实现图像的自动分类;对不含表格线的图像,文中将字符包围盒中心作为特征点,采用Hough变换的算法进行倾斜检测.另外,倾斜检测时还采用金字塔模型降低图像分辨率,进一步提高了算法速度.实验表明,该文的方法能够有效地去除图像黑边,快速准确地检测出图像的倾斜角,并具有很强的抗干扰性和应用适应性.     

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.     

Hough变换在中文名片图像倾斜校正中的应用

近来,文档图像的计算机自动理解已取得很多进展。但是,对于具有倾斜的图像的理解仍然存在许多困难。这种困难在中文名片图像自动识别与理解系统中尤为突出。必须在系统的输入端对图像作有效的倾斜校正以保证系统的性能。由于中文名片版面复杂,名片中文字行以及每行字符较少,使得现有的倾斜校正算法在处理名片图像时效果很不理想。Hough变换可用于一般文档图像的倾斜校正。但是,Hough变换在名片图像中的应用还有待研究。本文提出一种二级Hough变换算法,并应用于名片图像理解系统,利用名片图像自身的特点提高Hough变换的精确度和速度。这一方法的效果已被实验结果所证实。     

一种基于Hough变换的文档图像倾斜纠正方法

在对文本扫描输入的过程中,文本图像不可避免地会发生倾斜,倾斜校正将为图文分割、文字识别等后续处理工作创造良好的条件。提出了一种基于Hough变换的检测图像倾斜度的方法,为了克服Hough变换计算量大的缺点,该方法首先选取局部代表性子区域并提取其图像水平边缘,然后对提取的水平边缘进行两级Hough变换,从而实现了准确性与快速性的很好结合。     

Fiducial line based skew estimation

Skew estimation for textual document images is a well-researched topic and numerals of methods have been reported in the literature. One of the major challenges is the presence of interfering non-textual objects of various types and quantities in the document images. Many existing methods require proper separation of the textual objects which are well aligned from the non-textual objects which are mostly nonaligned. Some comparative evaluation work on the existing methods chooses only the text zones of the test image database. Therefore, the object filtering or zoning stage is crucial to the skew detection stage. However, it is difficult if not impossible to design general-purpose filters that are able to discriminate noises from textual components. This paper presents a robust, general-purpose skew estimation method that does not need any filtering or zoning preprocessing. In fact, this method does apply filtering, but not on the input components at the beginning of the detection process, rather on the output spectrum at the end of the detection process. Therefore, the problem of finding a textual component filter has been transformed into finding a convolution filter on the output accumulator array. This method consists of three steps: (1) the calculation of the slopes of the virtual lines that pass through the centroids of all the unique pairs of the connected components in an image, and quantizes the arctangents of the slopes into a 1-D accumulator array that covers the range from -90 to +90; (2) a special convolution on the resultant histogram, after which there remain only the prominent peaks that possibly correspond to the skew angles of the image; (3) the verification of the detection result. Its computational complexity and detection precision are uncoupled, unlike those projection-profile-based or Hough-transform-based methods whose speeds drop when higher precision is in demand. Speedup measures on the baseline implementation are also presented. The University of Washington English Document Image Database I (UWDB-I) contains a large number of scanned document images with significant amount of non-textual objects. Therefore, it is a good image database for evaluating the proposed method.     

织物图像的倾斜检测与纬纱密度识别

根据织物表面图像来自动识别组织结构参数是纹织CAD的一个重要研究内容。为解决在扫描过程中织物图像不可避免的倾斜现象,提出了一种快速的基于Hough变换的织物图像倾斜检测算法。为减少运算量,此算法首先提取图像梯度作为纬纱走向信息;然后运用层次Hough变换来检测倾斜角度,并获得了满意的检测精度;最后根据倾斜检测结果,采用一种新的与倾斜无关的纬密识别算法,通过提取倾斜角处的投影轮廓线来得到纬密排列规律,并计算出纬纱密度。实验结果表明,该算法用于结构图像倾斜检测和纬纱密度识别,可获得大于88%的检测准确率,纬度识别倾斜误差可控制在2°以内,可见具有较高的准确率和较好的实用性。     

Correcting for variable skew in document images

The proliferation of inexpensive sheet-feed scanners, particularly in fax machines, has led to a need to correct for the uneven paper feed rates during digitization if the images produced by these scanners are to be further analyzed. We develop a technique for detecting and compensating for this type of image distortion. This technique relies on the detection of multiple prominent skew angles in the document image along with their vertical position on the page, rotating the image by each of those angles and sampling the rotated images to allow reconstruction of the entire page image.Received: 28 November 2002, Accepted: 16 April 2003, Published online: 12 September 2003Correspondence to: A. Lawrence Spitz