一种利用灰度信息和二值信息的手写字符分割

在字符识别系统中，字符的有效分割是识别的关键。该文描述了一种两阶段的手写字符分割方法。根据字符倾斜角度将原字符串图像划分出若干个形状不规则的区域，每个区域含有一条分割线；针对现有分割方法的某些局限性，提出采用字符图像灰度信息和二值信息相结合的方式来求取分割区域中的分割路径。实验结果表明了该方法对于手写字符分割的有效性。     

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.     

Segmentation of connected handwritten digits using Self-Organizing Maps

Segmentation is an important issue in document image processing systems as it can break a sequence of characters into its components. Its application over digits is common in bank checks, mail and historical document processing, among others. This paper presents an algorithm for segmentation of connected handwritten digits based on the selection of feature points, through a skeletonization process, and the clustering of the touching region via Self-Organizing Maps. The segmentation points are then found, leading to the final segmentation. The method can deal with several types of connection between the digits, having also the ability to map multiple touching. The proposed algorithm achieved encouraging results, both relating to other state-of-the-art algorithms and to possible improvements.     

Automatic accurate broken character restoration for patrimonial documents

In this article, we are interested in the restoration of character shapes in antique document images. This particular class of documents generally present a lot of involuntary historical information that have to be taken into account to get quality digital libraries. Actually, many document processing methods of all sorts have already been proposed to cope with degraded character images, but those techniques often consist in replacing the degraded shapes by a corresponding prototype which is not satisfying for lots of specialists. For that, we decided to develop our own method for accurate character restoration, basing our study on generic image processing tools (namely: Gabor filtering and the active contours model) completed with some specific automatically extracted structural information. The principle of our method is to make an active contour recover the lost information using an external energy term based on the use of an automatically built and selected reference character image. Results are presented for real case examples taken from printed and handwritten documents.     

Accurate segmentation of complex document image using digital shearlet transform with neutrosophic set as uncertainty handling tool

In any image segmentation problem, there exist uncertainties. These uncertainties occur from gray level and spatial ambiguities in an image. As a result, accurate segmentation of text regions from non-text regions (graphics/images) in mixed and complex documents is a fairly difficult problem. In this paper, we propose a novel text region segmentation method based on digital shearlet transform (DST). The method is capable of handling the uncertainties arising in the segmentation process. To capture the anisotropic features of the text regions, the proposed method uses the DST coefficients as input features to a segmentation process block. This block is designed using the neutrosophic set (NS) for management of the uncertainty in the process. The proposed method is experimentally verified extensively and the performance is compared with that of some state-of-the-art techniques both quantitatively and qualitatively using benchmark dataset.     

Radon CLF: A Novel Approach for Skew Detection Using Radon Transform

In the digital world, a wide range of handwritten and printed documents should be converted to digital format using a variety of tools, including mobile phones and scanners. Unfortunately, this is not an optimal procedure, and the entire document image might be degraded. Imperfect conversion effects due to noise, motion blur, and skew distortion can lead to significant impact on the accuracy and effectiveness of document image segmentation and analysis in Optical Character Recognition (OCR) systems. In Document Image Analysis Systems (DIAS), skew estimation of images is a crucial step. In this paper, a novel, fast, and reliable skew detection algorithm based on the Radon Transform and Curve Length Fitness Function (CLF), so-called Radon CLF, was proposed. The Radon CLF model aims to take advantage of the properties of Radon spaces. The Radon CLF explores the dominating angle more effectively for a 1D signal than it does for a 2D input image due to an innovative fitness function formulation for a projected signal of the Radon space. Several significant performance indicators, including Mean Square Error (MSE), Mean Absolute Error (MAE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Measure (SSIM), Accuracy, and run-time, were taken into consideration when assessing the performance of our model. In addition, a new dataset named DSI5000 was constructed to assess the accuracy of the CLF model. Both two- dimensional image signal and the Radon space have been used in our simulations to compare the noise effect. Obtained results show that the proposed method is more effective than other approaches already in use, with an accuracy of roughly 99.87% and a run-time of 0.048 (s). The introduced model is far more accurate and time-efficient than current approaches in detecting image skew.     

脱机手写维吾尔文本图像单词切分

针对脱机手写维吾尔文本行图像中单词切分问题,提出了FCM融合K-means的聚类算法。通过该算法得到单词内距离和单词间距离两种分类。以聚类结果为依据,对文字区域进行合并,得到切分点,再对切分点内的文字进行连通域标注,进行着色处理。以50幅不同的人书写的维吾尔脱机手写文本图像为实验对象,共有536行和4?002个单词,正确切分率达到80.68%。实验结果表明,该方法解决了手写维吾尔文在切分过程中,单词间距离不规律带来的切分困难的问题和一些单词间重叠的问题。同时实现了大篇幅手写文本图像的整体处理。     

回归—聚类联合框架下的手写文本行提取

目的 手写文本行提取是文档图像处理中的重要基础步骤,对于无约束手写文本图像,文本行都会有不同程度的倾斜、弯曲、交叉、粘连等问题。利用传统的几何分割或聚类的方法往往无法保证文本行边缘的精确分割。针对这些问题提出一种基于文本行回归-聚类联合框架的手写文本行提取方法。方法 首先,采用各向异性高斯滤波器组对图像进行多尺度、多方向分析,利用拖尾效应检测脊形结构提取文本行主体区域,并对其骨架化得到文本行回归模型。然后,以连通域为基本图像单元建立超像素表示,为实现超像素的聚类,建立了像素-超像素-文本行关联层级随机场模型,利用能量函数优化的方法实现超像素的聚类与所属文本行标注。在此基础上,检测出所有的行间粘连字符块,采用基于回归线的k-means聚类算法由回归模型引导粘连字符像素聚类,实现粘连字符分割与所属文本行标注。最后,利用文本行标签开关实现了文本行像素的操控显示与定向提取,而不再需要几何分割。结果 在HIT-MW脱机手写中文文档数据集上进行文本行提取测试,检测率DR为99.83%,识别准确率RA为99.92%。结论 实验表明,提出的文本行回归-聚类联合分析框架相比于传统的分段投影分析、最小生成树聚类、Seam Carving等方法提高了文本行边缘的可控性与分割精度。在高效手写文本行提取的同时,最大程度地避免了相邻文本行的干扰,具有较高的准确率和鲁棒性。     

Text segmentation using gabor filters for automatic document processing

There is a considerable interest in designing automatic systems that will scan a given paper document and store it on electronic media for easier storage, manipulation, and access. Most documents contain graphics and images in addition to text. Thus, the document image has to be segmented to identify the text regions, so that OCR techniques may be applied only to those regions. In this paper, we present a simple method for document image segmentation in which text regions in a given document image are automatically identified. The proposed segmentation method for document images is based on a multichannel filtering approach to texture segmentation. The text in the document is considered as a textured region. Nontext contents in the document, such as blank spaces, graphics, and pictures, are considered as regions with different textures. Thus, the problem of segmenting document images into text and nontext regions can be posed as a texture segmentation problem. Two-dimensional Gabor filters are used to extract texture features for each of these regions. These filters have been extensively used earlier for a variety of texture segmentation tasks. Here we apply the same filters to the document image segmentation problem. Our segmentation method does not assume any a priori knowledge about the content or font styles of the document, and is shown to work even for skewed images and handwritten text. Results of the proposed segmentation method are presented for several test images which demonstrate the robustness of this technique. This work was supported by the National Science Foundation under NSF grant CDA-88-06599 and by a grant from E. 1. Du Pont De Nemours & Company.     

Shape based local thresholding for binarization of document images

This paper presents a novel local threshold algorithm for the binarization of document images. Stroke width of handwritten and printed characters in documents is utilized as the shape feature. As a result, in addition to the intensity analysis, the proposed algorithm introduces the stroke width as shape information into local thresholding. Experimental results for both synthetic and practical document images show that the proposed local threshold algorithm is superior in terms of segmentation quality to the threshold approaches that solely use intensity information.     

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 graph-based approach for segmenting touching lines in historical handwritten documents

Text line segmentation in handwritten documents is an important task in the recognition of historical documents. Handwritten document images contain text lines with multiple orientations, touching and overlapping characters between consecutive text lines and different document structures, making line segmentation a difficult task. In this paper, we present a new approach for handwritten text line segmentation solving the problems of touching components, curvilinear text lines and horizontally overlapping components. The proposed algorithm formulates line segmentation as finding the central path in the area between two consecutive lines. This is solved as a graph traversal problem. A graph is constructed using the skeleton of the image. Then, a path-finding algorithm is used to find the optimum path between text lines. The proposed algorithm has been evaluated on a comprehensive dataset consisting of five databases: ICDAR2009, ICDAR2013, UMD, the George Washington and the Barcelona Marriages Database. The proposed method outperforms the state-of-the-art considering the different types and difficulties of the benchmarking data.     

Near-duplicate document image matching: A graphical perspective

A near-duplicate document image matching approach characterized by a graphical perspective is proposed in this paper. Document images are represented by graphs whose nodes correspond to the objects in the images. Consequently, the image matching problem is then converted to graph matching. To deal with the instability of object segmentation, a multi-granularity object tree is constructed for a document image. Each level in the tree corresponds to one possible object segmentation, while different levels are characterized by various object granularities. Some graphs can be generated from the tree and the objects associated with each graph may be of different granularities. Two graphs with the maximum similarity are found from the multi-granularity object trees of the two near-duplicate document images which are to be matched. The encouraging experimental results have demonstrated the effectiveness of the proposed approach.     

Segmentation and recognition system for unknown-length handwritten digit strings

The segmentation of handwritten digit strings into isolated digits remains a challenging task. The difficulty for recognizing handwritten digit strings is related to several factors such as sloping, overlapping, connecting and unknown length of the digit string. Hence, this paper aims to propose a segmentation and recognition system for unknown-length handwritten digit strings by combining several explicit segmentation methods depending on the configuration link between digits. Three segmentation methods are combined based on histogram of the vertical projection, the contour analysis and the sliding window Radon transform. A recognition and verification module based on support vector machine classifiers allows analyzing and deciding the rejection or acceptance each segmented digit image. Moreover, various submodules are included leading to enhance the robustness of the proposed system. Experimental results conducted on the benchmark dataset show that the proposed system is effective for segmenting handwritten digit strings without prior knowledge of their length comparatively to the state of the art.     

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]     

一种快速均值漂移图像分割算法

图像分割是图像分析及图像理解的关键步骤。与其他图像分割算法相比,均值漂移(Mean Shift)算法具有原理简单、无需先验知识、可以处理灰度图像及复杂的自然彩色图像等优点。但该算法需要对图像中每个像素点进行迭代计算,因此分割所需要的时间较长。本文提出了一种快速Mean Shift图像分割算法(Fast mean shift,FMS),将少量像素点作为初始点进行迭代计算,而出现在高维球区域内的其他像素点根据其到已有类中心的距离进行归类,从而减少Mean Shift算法的迭代次数,缩短分割时间。实验结果表明,本文提出的快速Mean Shift图像分割算法可以获得良好的分割结果且具有较高的分割效率。     

An adaptive local binarization method for document images based on a novel thresholding method and dynamic windows

Binary image representation is essential format for document analysis. In general, different available binarization techniques are implemented for different types of binarization problems. The majority of binarization techniques are complex and are compounded from filters and existing operations. However, the few simple thresholding methods available cannot be applied to many binarization problems. In this paper, we propose a local binarization method based on a simple, novel thresholding method with dynamic and flexible windows. The proposed method is tested on selected samples called the DIBCO 2009 benchmark dataset using specialized evaluation techniques for binarization processes. To evaluate the performance of our proposed method, we compared it with the Niblack, Sauvola and NICK methods. The results of the experiments show that the proposed method adapts well to all types of binarization challenges, can deal with higher numbers of binarization problems and boosts the overall performance of the binarization.