A new robust algorithm for video text extraction

We report on the development and implementation of a robust algorithm for extracting text in digitized color video. The algorithm first computes maximum gradient difference to detect potential text line segments from horizontal scan lines of the video. Potential text line segments are then expanded or combined with potential text line segments from adjacent scan lines to form text blocks, which are then subject to filtering and refinement. Color information is then used to more precisely locate text pixels within the detected text blocks. The robustness of the algorithm is demonstrated by using a variety of color images digitized from broadcast television for testing. The algorithm also performs well on images after JPEG compression and decompression, and on images corrupted with different types of noise.     

从高分辨率遥感影像中提取城市道路的新方法

在综合几种现有算法优点的基础上,提出一种新的道路提取策略。首先以角度纹理特性法分割原始影像;接着利用直线匹配原理剔除初始分割结果中的非道路地物,得到更为规则的道路条带;然后通过形态学手段获得道路中心线,并将每条中心线拆分为多段直线;结合上下文知识的马尔可夫模型被用于组织道路段的中心线,从而恢复完整道路网。实验结果表明：新方法具有良好的性能,可以从高分辨IKONOS遥感影像中提取出复杂的城市道路。     

Recognising text in real scenes

We present two different approaches to the location and recovery of text in images of real scenes. The techniques we describe are invariant to the scale and 3D orientation of the text, and allow recovery of text in cluttered scenes. The first approach uses page edges and other rectangular boundaries around text to locate a surface containing text, and to recover a fronto-parallel view. This is performed using line detection, perceptual grouping, and comparison of potential text regions using a confidence measure. The second approach uses low-level texture measures with a neural network classifier to locate regions of text in an image. Then we recover a fronto-parallel view of each located paragraph of text by separating the individual lines of text and determining the vanishing points of the text plane. We illustrate our results using a number of images. Received May 20, 2001 / Accepted June 19, 2001     

Detection of dimension sets in engineering drawings

This correspondence presents a system for detecting dimension sets in engineering drawings that are drawn to ANSI drafting standards. A new rule-based text/graphics separation algorithm and a model-based procedure for detecting arrowheads in any orientation have been developed. Arrowhead tracking and search methods are used to extract leaders, tails, and witness lines from segmented images containing only graphics. Text blocks and feature control frames extracted from the segmented images are than associated with their corresponding leaders to obtain complete dimension sets. Experimental results are presented     

A robust algorithm for text string separation from mixedtext/graphics images

The development and implementation of an algorithm for automated text string separation that is relatively independent of changes in text font style and size and of string orientation are described. It is intended for use in an automated system for document analysis. The principal parts of the algorithm are the generation of connected components and the application of the Hough transform in order to group components into logical character strings that can then be separated from the graphics. The algorithm outputs two images, one containing text strings and the other graphics. These images can then be processed by suitable character recognition and graphics recognition systems. The performance of the algorithm, both in terms of its effectiveness and computational efficiency, was evaluated using several test images and showed superior performance compared to other techniques     

Morphology-based text line extraction

This paper presents a morphology-based text line extraction algorithm for extracting text regions from cluttered images. First of all, the method defines a novel set of morphological operations for extracting important contrast regions as possible text line candidates. The contrast feature is robust to lighting changes and invariant against different image transformations like image scaling, translation, and skewing. In order to detect skewed text lines, a moment-based method is then used for estimating their orientations. According to the orientation, an x-projection technique can be applied to extract various text geometries from the text-analogue segments for text verification. However, due to noise, a text line region is often fragmented to different pieces of segments. Therefore, after the projection, a novel recovery algorithm is then proposed for recovering a complete text line from its pieces of segments. After that, a verification scheme is then proposed for verifying all extracted potential text lines according to their text geometries. Experimental results show that the proposed method improves the state-of-the-art work in terms of effectiveness and robustness for text line detection.     

一种两点间直线搜索的输电线检测方法

在无人机应用于输电线巡检背景下,为了方便后续的输电线故障检测与分析,提出了一种两点间直线搜索的输电线检测方法。首先,通过方向可控滤波器对无人机图像进行分割。然后,通过所提出的基于圆搜索(CBS)直线段检测方法进行直线段检测。最后,通过所提出的连接算法进行直线段连接。通过在人造图像上的直线段检测实验,证明该方法是一种有效的直线检测方法。随后,通过在实际场景的无人机图像上进行实验,证明该方法是一种有效的输电线检测方法。     

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。     

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.     

基于贪婪算法的文档图像中干扰线的去除

各种文档中经常包含有各种特殊作用的横线、手划线等,当这些文档通过扫描等数字化方式存入计算机并需要进一步识别处理成文字编码时,这些线条却成为OCR的干扰因素,降低了文档内容的识别率.为此,本文提出一种新的文档干扰线去除算法,先将文档图像二值化,二值化过程考虑了不均匀光照带来的影响;然后将前景细化为单像素,减少线条粗细造成的影响;接着通过一种改进的贪婪算法计算横、竖两个方向线段的权重,判断权重较高的线段为干扰线;最后通过与干扰线距离的大小判断图像中每个前景像素的归属,从而获得一个完整的文档恢复图.仿真实验表明,本文提出的算法能够有效去除干扰线,特别在干扰线与文字粘连的情况下,去除干扰线的同时较少地影响文档图像的质量,且具有较高的计算速度和较好的去除效果,为图像进一步OCR识别提供了良好的基础.     

彩色图像中汉字的自动定位

检测并提取图像中所含的汉字有许多重要的应用。该文提出了彩色图像中自动定位汉字的一种新算法。该法采用“改进的DCT纹理分析法”把图像中的汉字和背景区域进行了预分类,再根据汉字的结构特征,在利用先验知识的基础上,采用“改进的连接组件分析法”对已预分割出的每个待定汉字区域进行细分。新算法克服了DCT纹理分析法和连接组件法各自的缺点。对包含汉字的照片、书籍封面等图像进行试验的结果表明,这一算法是非常有效的。     

用于文本区域提取的边缘像素聚类方法

根据边缘点的位置和颜色信息采取逐步松弛的聚类方法将图像分割成像素子集,应用文本区域边缘的分布特征提取初始文本区,并进行边界扩展得到完整的文本区域;同时给出了一种文本区域二值化方法,减少了在文本颜色极性未知时的二值图像个数,可提高字符分割等后续处理的计算效率.实验结果表明,该方法对文本区域提取是有效的,提取完整率达99%.     

Efficient detection and tracking of moving objects in geo-coordinates

We present a system to detect and track moving objects from an airborne platform. Given a global map, such as a satellite image, our approach can locate and track the targets in geo-coordinates, namely longitude and latitude obtained from geo-registration. A motion model in geo-coordinates is more physically meaningful than the one in image coordinates. We propose to use a two-step geo-registration approach to stitch images acquired by satellite and UAV cameras. Mutual information is used to find correspondences between these two very different modalities. After motion segmentation and geo-registration, tracking is performed in a hierarchical manner: at the temporally local level, moving image blobs extracted by motion segmentation are associated into tracklets; at the global level, tracklets are linked by their appearance and spatio-temporal consistency on the global map. To achieve efficient time performance, graphics processing unit techniques are applied in the geo-registration and motion detection modules, which are the bottleneck of the whole system. Experiments show that our method can efficiently deal with long term occlusion and segmented tracks even when targets fall out the field of view.     

基于直线段跟踪的手绘图元离线识别方法*

手绘图形是人类思维外化和表达意图的一种有效方式,如何有效地提取手绘在图纸上的图形元素是理解绘图者意图的关键问题。鉴于手绘图形是由基本图元组合构成,采用层次结构逐步实现图元提取的思想,提出了一种手绘基本图元（线段、弧、圆和椭圆）的离线识别方法。在提取图形笔画骨架像素的基础上,跟踪骨架像素得到图形的直线段描述;通过对直线段序列的分析,进行直线段序列的断开和连接处理,形成图元的曲线段描述,通过对图元曲线段描述的分析得出图元的几何参数。实验表明,该方法能够以高精确度快速识别出图像中包含的手绘图元,具有良好的稳定性     

聚类+连体段判别的维吾尔文档图像单词切分

目前针对印刷体维吾尔文档图像的切分研究主要集中在字母切分上，单词切分的文献较少，且存在着标点符号难处理，未合并被拆分书写的单词等问题，同时单词切分准确率有待进一步提高。在对文档图像进行投影处理的基础上，通过[K]均值聚类算法[（K]-means）对文本行中所有连体段之间的间隙进行聚类分析得出最佳的间隙判别阈值，然后对所有连体段进行筛选和粗略识别，并结合对间隙的阈值判别结果来确定单词的精确切分点和获取被拆分书写单词的位置信息。在选取的100张文档图像中测试时，结果表明该方法能有效去除标点符号对切分结果的影响，准确合并被拆分书写的单词，并且平均单词切分准确率保持在99%以上。     

Document image binarization using background estimation and stroke edges

Document images often suffer from different types of degradation that renders the document image binarization a challenging task. This paper presents a document image binarization technique that segments the text from badly degraded document images accurately. The proposed technique is based on the observations that the text documents usually have a document background of the uniform color and texture and the document text within it has a different intensity level compared with the surrounding document background. Given a document image, the proposed technique first estimates a document background surface through an iterative polynomial smoothing procedure. Different types of document degradation are then compensated by using the estimated document background surface. The text stroke edge is further detected from the compensated document image by using L1-norm image gradient. Finally, the document text is segmented by a local threshold that is estimated based on the detected text stroke edges. The proposed technique was submitted to the recent document image binarization contest (DIBCO) held under the framework of ICDAR 2009 and has achieved the top performance among 43 algorithms that are submitted from 35 international research groups.     

基于DCT压缩域的图象字符定位

为了能够利用图象中所含的文字信息来进行图象的快速高效浏览检查，其中，快速字符定位是很重要的工作，为此设计了一种直接在图象压缩域中进行字符定位的方法，该方法主要是利用图象中字符纹理所具有的方向性特点，首先直接在DCT域中提取字符的横向、竖向、斜向纹理的方向信息，然后根据各自的阈值把字符区域从图象背景中分割出来，在处理过程中，用形态滤波的方法可有效地消除噪音点，该算法可直接处理JPEG、MPEG等以DCT为编码基础的压缩数据，仅需少量的解码过程（Huffman解码）即可完成字符定位，因此要处理的数据量较少，用该算法既提高了处理速度，又减少了对计算机资源的需求，试验结果表明，此方法具有较高的准确率。     

Cytoplasm and nucleus segmentation in cervical smear images using Radiating GVF Snake

A Radiating Gradient Vector Flow (RGVF) Snake aiming at accurate extraction of both the nucleus and cytoplasm from a single-cell cervical smear image is proposed. After preprocessing, the areas in the image are roughly clustered into nucleus, cytoplasm and the background by a spatial K-means clustering algorithm. After initial contours are extracted, the image is segmented using RGVF. RGVF involves a new edge map computation method and a stack-based refinement, and is thus robust to contaminations and can effectively locate the obscure boundaries. The boundaries can also be correctly traced even if there are interferences near the cytoplasm and nucleus regions. Experiments performed on the Herlev dataset, which contains 917 images show the effectiveness of the proposed algorithm.     

A robust approach to text line grouping in online handwritten Japanese documents

In this paper, we present an effective approach for grouping text lines in online handwritten Japanese documents by combining temporal and spatial information. With decision functions optimized by supervised learning, the approach has few artificial parameters and utilizes little prior knowledge. First, the strokes in the document are grouped into text line strings according to off-stroke distances. Each text line string, which may contain multiple lines, is segmented by optimizing a cost function trained by the minimum classification error (MCE) method. At the temporal merge stage, over-segmented text lines (caused by stroke classification errors) are merged with a support vector machine (SVM) classifier for making merge/non-merge decisions. Last, a spatial merge module corrects the segmentation errors caused by delayed strokes. Misclassified text/non-text strokes (stroke type classification precedes text line grouping) can be corrected at the temporal merge stage. To evaluate the performance of text line grouping, we provide a set of performance metrics for evaluating from multiple aspects. In experiments on a large number of free form documents in the Tokyo University of Agriculture and Technology (TUAT) Kondate database, the proposed approach achieves the entity detection metric (EDM) rate of 0.8992 and the edit-distance rate (EDR) of 0.1114. For grouping of pure text strokes, the performance reaches EDM of 0.9591 and EDR of 0.0669.