Handwritten Chinese text line segmentation by clustering with distance metric learning

Separating text lines in unconstrained handwritten documents remains a challenge because the handwritten text lines are often un-uniformly skewed and curved, and the space between lines is not obvious. In this paper, we propose a novel text line segmentation algorithm based on minimal spanning tree (MST) clustering with distance metric learning. Given a distance metric, the connected components (CCs) of document image are grouped into a tree structure, from which text lines are extracted by dynamically cutting the edges using a new hypervolume reduction criterion and a straightness measure. By learning the distance metric in supervised learning on a dataset of pairs of CCs, the proposed algorithm is made robust to handle various documents with multi-skewed and curved text lines. In experiments on a database with 803 unconstrained handwritten Chinese document images containing a total of 8,169 lines, the proposed algorithm achieved a correct rate 98.02% of line detection, and compared favorably to other competitive algorithms.     

A multi-plane approach for text segmentation of complex document images

This study presents a new method, namely the multi-plane segmentation approach, for segmenting and extracting textual objects from various real-life complex document images. The proposed multi-plane segmentation approach first decomposes the document image into distinct object planes to extract and separate homogeneous objects including textual regions of interest, non-text objects such as graphics and pictures, and background textures. This process consists of two stages—localized histogram multilevel thresholding and multi-plane region matching and assembling. Then a text extraction procedure is applied on the resultant planes to detect and extract textual objects with different characteristics in the respective planes. The proposed approach processes document images regionally and adaptively according to their respective local features. Hence detailed characteristics of the extracted textual objects, particularly small characters with thin strokes, as well as gradational illuminations of characters, can be well-preserved. Moreover, this way also allows background objects with uneven, gradational, and sharp variations in contrast, illumination, and texture to be handled easily and well. Experimental results on real-life complex document images demonstrate that the proposed approach is effective in extracting textual objects with various illuminations, sizes, and font styles from various types of complex document images.     

A method for combining complementary techniques for document image segmentation

Image segmentation is a major task of handwritten document image processing. Many of the proposed techniques for image segmentation are complementary in the sense that each of them using a different approach can solve different difficult problems such as overlapping, touching components, influence of author or font style etc. In this paper, a combination method of different segmentation techniques is presented. Our goal is to exploit the segmentation results of complementary techniques and specific features of the initial image so as to generate improved segmentation results. Experimental results on line segmentation methods for handwritten documents demonstrate the effectiveness of the proposed combination method.     

复杂版面的文本图像图文分割算法

为了满足办公自动化的实时性要求,本文提出了一种改进的自顶向下的图文分割算法。该方法利用文本行基线之间的距离自适应的确定结构元素的大小,克服自顶向下算法要求对页面有先验知识的缺点。实验表明,本文提出的算法分割准确,速度快。     

手写体数字识别技术的研究

手写体数字识别特征提取方面,有模板匹配,统计特征和结构特征,在分类器设计上有基于距离的分类器和神经网络分类器等,分析和评价了这些问题后,指出今后的研究方向应在特征综合,分类器集成以及新的分类器的研究上。     

A differential-processing extraction approach to text and image segmentation

To efficiently store the information found in paper documents, text and non-text regions need to be separated. Non-text regions include half-tone photographs and line diagrams. The text regions can be converted (via an optical character reader) to a computer-searchable form, and the non-text regions can be extracted and preserved in compressed form using image-compression algorithms. In this paper, an effective system for automatically segmenting a document image into regions of text and non-text is proposed. The system first performs an adaptive thresholding to obtain a binarized image. Subsequently the binarized image is smeared using a run-length differential algorithm. The smeared image is then subjected to a text characteristic filter to remove error smearing of non-text regions. Next, baseline cumulative blocking is used to rectangularize the smeared region. Finally, a text block growing algorithm is used to block out a text sentence. The recognition of text is carried out on a text sentence basis.     

Low quality document image modeling and enhancement

In order to tackle problems such as shadow- through and bleed-through, a novel defect model is developed which generates physically damaged document images. This model addresses physical degradation, such as aging and ink seepage. Based on the diffusive nature of the physical defects, the model is designed using virtual diffusion processes. Then, based on this degradation model, a restoration method is proposed and used to fix the bleed-through effect in double-sided document images using the reverse diffusion process. Subjective and objective evaluations are performed on both the degradation model and the restoration method. The experiments show promising results on both real and generated data.     

Generalization of Frei and Chen's approach to extract the lines and edges of a digital image

This work proposes the generalization to any k dimension of the approach suggested by Frei and Chen for line and edge detection in a digital image with square masks. With the proposed algorithm we can obtain information about the image lines and edges without modifying the rest of the image. To test these results we have applied the algorithm to biomedical images and geophysical images of archaeological prospections achieving optimal results.     

图像分割技术研究与应用

图像分割是指将一副图像分解为若干互不交叠的有意义且具有相同属性的区域。图像分割是数字图像处理中的一项关键技术,其分割的准确性直接影响后续任务的有效性,因此具有十分重要的意义。现有的分割算法在不同程度上取得了一定的成功,但是图像分割的很多问题还远远没有解决,该方面的研究仍然面临很多挑战。文章分析了现有图像分割的各种算法的特点以及存在的问题,对基于图像分割的经典算法进行改进,实现了一种新的分割方法,并将其应用到机器视觉的相关产品当中,取得了良好的效果。     

基于图层分割的文档图像压缩方法

传统的图像压缩技术,大都基于图像空域和色度空间同质性的假定,在文档图像的压缩中并不能取得最好的压缩效果。针对文档图像的特点,提出了一种基于图层分割的文档图像压缩方法。该方法首先利用多尺度的2色聚类算法进行文档图像的图层分割,然后根据不同图层的特征,分别采用效果最佳的压缩技术,能够获得比传统的方法更好的压缩效果。     

An empirical measure of the performance of a document image segmentation algorithm

Document image segmentation is the first step in document image analysis and understanding. One major problem centres on the performance analysis of the evolving segmentation algorithms. The use of a standard document database maintained at the Universities/Research Laboratories helps to solve the problem of getting authentic data sources and other information, but some methodologies have to be used for performance analysis of the segmentation. We describe a new document model in terms of a bounding box representation of its constituent parts and suggest an empirical measure of performance of a segmentation algorithm based on this new graph-like model of the document. Besides the global error measures, the proposed method also produces segment-wise details of common segmentation problems such as horizontal and vertical split and merge as well as invalid and mismatched regions. Received July 14, 2000 / Revised June 12, 2001[-1mm]     

A modified support vector machine and its application to image segmentation

Recently, researchers are focusing more on the study of support vector machine (SVM) due to its useful applications in a number of areas, such as pattern recognition, multimedia, image processing and bioinformatics. One of the main research issues is how to improve the efficiency of the original SVM model, while preventing any deterioration of the classification performance of the model. In this paper, we propose a modified SVM based on the properties of support vectors and a pruning strategy to preserve support vectors, while eliminating redundant training vectors at the same time. The experiments on real images show that (1) our proposed approach can reduce the number of input training vectors, while preserving the support vectors, which leads to a significant reduction in the computational cost while attaining similar levels of accuracy. (2)The approach also works well when applied to image segmentation.     

Text region extraction in a document image based on the Delaunay tessellation

In this paper, Delaunay triangulation is applied for the extraction of text areas in a document image. By representing the location of connected components in a document image with their centroids, the page structure is described as a set of points in two-dimensional space. When imposing Delaunay triangulation on these points, the text regions in the Delaunay triangulation will have distinguishing triangular features from image and drawing regions. For analysis, the Delaunay triangles are divided into four classes. The study reveals that specific triangles in text areas can be clustered together and identified as text body. Using this method, text regions in a document image containing fragments can also be recognized accurately. Experiments show the method is also very efficient.     

一种结合训练样本筛选的SVM图像分割方法

基于支持向量的图像分割方法一般使用交互方式获取的训练样本,不可避免的在训练样本中引入歧义样本。这些歧义样本严重影响了基于支持向量机图像分割方法的性能。提出一种先对训练样本进行筛选,再进行分类(分割)的支持向量图像分割方法;并给出了一种基于支持向量机的样本筛选方法,可有效地降低歧义样本的影响。实验表明,经样本筛选的SVM分割方法有更好的分割性能。     

Text line and word segmentation of handwritten documents

In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents.     

A new scheme for unconstrained handwritten text-line segmentation

Variations in inter-line gaps and skewed or curled text-lines are some of the challenging issues in segmentation of handwritten text-lines. Moreover, overlapping and touching text-lines that frequently appear in unconstrained handwritten text documents significantly increase segmentation complexities. In this paper, we propose a novel approach for unconstrained handwritten text-line segmentation. A new painting technique is employed to smear the foreground portion of the document image. The painting technique enhances the separability between the foreground and background portions enabling easy detection of text-lines. A dilation operation is employed on the foreground portion of the painted image to obtain a single component for each text-line. Thinning of the background portion of the dilated image and subsequently some trimming operations are performed to obtain a number of separating lines, called candidate line separators. By using the starting and ending points of the candidate line separators and analyzing the distances among them, related candidate line separators are connected to obtain segmented text-lines. Furthermore, the problems of overlapping and touching components are addressed using some novel techniques. We tested the proposed scheme on text-pages of English, French, German, Greek, Persian, Oriya and Bangla and remarkable results were obtained.     

A knowledge-based system for extracting text-lines from mixed and overlapping text/graphics compound document images

This paper presents a new knowledge-based system for extracting and identifying text-lines from various real-life mixed text/graphics compound document images. The proposed system first decomposes the document image into distinct object planes to separate homogeneous objects, including textual regions of interest, non-text objects such as graphics and pictures, and background textures. A knowledge-based text extraction and identification method obtains the text-lines with different characteristics in each plane. The proposed system offers high flexibility and expandability by merely updating new rules to cope with various types of real-life complex document images. Experimental and comparative results prove the effectiveness of the proposed knowledge-based system and its advantages in extracting text-lines with a large variety of illumination levels, sizes, and font styles from various types of mixed and overlapping text/graphics complex compound document images.