基于复杂背景的彩色图像中维吾尔文字切分

沿着基线并具有大量附加部分书写是维吾尔文一大特点,这些特点使复杂背景的彩色图像中维吾尔文字行与字的切分和识别成为一个既困难又有趣的问题。本文首先对复杂彩色图像进行灰度化,其次将彩色图像转换为灰度化的边缘图像,再对图像进行局域二值化,然后进行区域检测和边缘调整,初步实现了图像中维吾尔文字行的定位,紧接着根据定位结果从图像中切分出文字行,统计切分后的文字行在水平和垂直方向上的像素累计情况,查找最佳切分点,分离出文字行中的字母独立形式或几个字母连成的连体字母段。实验结果表明,文字行的切分准确率达到96%,字切分准确率达到98%以上。     

改进SURF算法在图像汉字识别中的应用

针对复杂背景下汉字匹配准确率较低的问题,提出一种改进的SURF算法。该算法利用灰度分级的字符分割方法,先进行灰度分割增强图像的对比度,采用灰度分级树将图像中的所有像素处理为树的模式进行计算,根据灰度分级确定主节点,根据主节点的级别所对应的灰度值对图像进行分割。同时,根据汉字结构的特殊性,取消了SURF算法的旋转不变性。实验结果表明,与未使用改进的SURF算法相比,对图像质量较差的文本图像,改进的SURF算法能有效地提高其匹配的准确率。     

文本图像信息的提取与识别

文本是计算机视觉的许多应用中的一项重要特征,图像中的文本往往包含着比较丰富的信息,将文本图像信息里的文字进行提取和识别,对于图像内容的分析、理解、信息检索等方面具有重要的意义。文本图像的识别分为预处理,文字的切分,细化,特征选择与提取,最后对候选文字进行识别。在文字的切分方面提出了一种改进的投影算法,该算法能在很大程度上提高文字切分的准确度,采用基于数学形态学算法对文字进行细化处理,并在特征选择方面引用了多级分类的算法。     

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。     

基于连通域的汉字切分技术研究

字符切分技术已经成为汉字识别系统设计中的关键问题,对于质量较差的文本图像,用灰度图像取代传统的二值化黑白图像能够取得更好的切分效果,基于连通域的切分算法能够对灰度图像进行较好的切分,基于连通域的汉字切分算法能有效地对文本图像中汉字字符部件进行合并及对粘连字符进行分割。     

朝汉混排古籍的文字切分方法

为解决朝鲜语古籍数字化中朝汉文种混排字符切分困难的问题,提出一种朝鲜语古籍图像的文字切分算法。针对古籍列与列之间存在不连续间隔线、倾斜或者粘连等问题,提出一种基于连通域投影的列切分方法。利用连通域的删除、合并、拆分等操作对文字进行切分。使用一种多步切分法完成了具有文字大小不一,横向、纵向混合排版特点图像的字符切分工作。对于粘连字,采用改进的滴水算法进行有效切分。实验结果表明所提出的算法能够很好地完成朝、汉文种混排,文字大小不一,排版情况复杂的朝鲜语古籍图像的文字切分工作。该算法的列切分准确率为97.69%,字切分准确率为87.79%。     

基于贝叶斯分类器的混排文字切分与分类

该文针对实际的混排文档图像,提出一种基于贝叶斯分类器的统计学习方法切分文字,并实现文字类别判断。该方法结构简单、计算量少、易于扩展功能,而且试验结果表明该方法切分效果好、文字类别判断准确。     

基于连通域的扭曲中文文本图像快速校正方法

针对扭曲中文文本图像文字识别率不理想这一问题,提出一种基于连通域的文本图像快速扭曲校正方法。根据汉字结构特征合并连通域,实现切分文字;利用就近聚合文字的方法定位文本行,按行垂直校正每个文字位置,获得被校正的图像。实验结果表明,该方法校正速度快,对严重扭曲的中文文本图像能取得较好的校正效果,校正后图像的OCR识别率明显提高。     

一种光照不均文本图像的校正算法

针对光照不均的文本图像在二值化后文字识别率底下的问题,文章提出了一种针对光照不均图像进行二值化和图像增强的算法。针对图像进行分块处理,根据每块自身的平均灰度值和均方差以及全图的平均灰度值来进行算法的自适应变换,已达到对不同光照的区块进行不同处理的目的。实验结果表明本算法能够比较有效的完成对光照不均的文本图像的处理,比单纯的二值化处理方法有一定的提高。     

基于视频图像的人眼动态追踪

论文基于视频图像对人眼的瞳孔中心进行实时定位,根据该点的位置变化从而判断人眼的注视方向即人眼的动态追踪.首先对视频图像进行灰度化处理,将视频图像从RGB色彩空间转换到灰度空间.然后对灰度图像进行高斯滤波处理,去除图像的噪点.接着对图像进行二值化处理找到人眼区域并检测出瞳孔轮廓,最后进行瞳孔中心定位实现人眼动态追踪.实验结果表明,该方法是有效的,能够实时追踪眼动方向.     

一种优化的文档图像分割方法

文档图像在数字图书馆、电子商务以及电子政务等工程中已获得广泛应用。如何对文档图像进行有效的转换、存储和传输，成为人们研究的焦点。将文档图像分割成不同的区域，根据不同区域的特点分别进行处理，成为一种有效的解决方案。本文在传统的块分割和图层分割方法的基础上，提出了一种优化的文档图像分割思路，对这两种方法进行了合理的综合处理，能够取得更好的效果。     

Efficient character segmentation approach for machine-typed documents

In this paper an efficient approach for segmentation of the individual characters from scanned documents typed on old typewriters is proposed. The approach proposed in this paper is primarily intended for processing of machine-typed documents, but can be used for machine-printed documents as well. The proposed character segmentation approach uses the modified projection profiles technique which is based on using the sliding window for obtaining the information about the document image structure. This is followed by histogram processing in order to determine the spaces between lines, words and characters in the document image. The decision-making logic used in the process of character segmentation is describes and represents the most an integral aspect of the proposed technique. Beside the character segmentation approach, the ultra-fast architecture for geometrical image transformations, which is used for image rotation in the process of skew correction, is presented, and its fast implementation using pointer arithmetic and a highly optimized low-level machine routine is provided. The proposed character segmentation approach is semi-automatic and uses threshold values to control the segmentation process. Provided results for segmentation accuracy show that the proposed approach outperforms the state-of-the-art approaches in most cases. Also, the results from the aspect of the time complexity show that the new technique performs faster than state-of-the-art approaches and can process even very large document images in less than one second, which makes this approach suitable for real-time tasks. Finally, visual demonstration of the proposed approach performances is achieved using original documents authored by Nikola Tesla.     

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.     

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.     

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]     

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.     

基于字符图像分割的打印文件识别方法

针对目前的打印文件识别方法受限于样本中必须有相同字符的问题,提出一种基于字符图像分割的打印文件识别方法。通过k-means算法对字符图像进行分割,分别对不同区域提取局部二值模式纹理特征,从而消除字符结构对识别结果的影响。研究了单一区域的特征集和组合特征集的分类识别效果,实验结果表明,该方法在样本中无相同字符的情况下,能够得到较高的识别准确率。     

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。