Extracting curved text lines using local linearity of the text line

In order to enhance the ability of document analysis systems, we need a text line extraction method which can handle not only straight text lines but also text lines in various shapes. This paper proposes a new method called Extended Linear Segment Linking (ELSL for short), which is able to extract text lines in arbitrary orientations and curved text lines. We also consider the existence of both horizontally and vertically printed text lines on the same page. The new method can produce text line candidates for multiple orientations. We verify the ability of the method by some experiments as well. Received December 21, 1998 / Revised version September 2, 1999     

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.     

A robust algorithm for separation of Chinese characters from line drawings

Separating characters from graphics is an important step towards automatic document understanding. In this paper, we propose a robust algorithm to separate Chinese characters from graphics. Our approach is based on clustering the feature points in an image. Two remedy procedures are also proposed to solve the problems caused by the thinning process. This will obtain a better localization of feature points and improve the performance of the separation process. Using our algorithm, all Chinese characters can be separated from graphics without regard to the font style or orientation of the character. Furthermore, our algorithm can also handle the serious case where characters touch/cross lines. The proposed algorithm has been successfully tested on several kinds of line drawings, such as land register maps and form documents.     

Efficient and flexible text extraction from document pages

This paper describes a novel method for extracting text from document pages of mixed content. The method works by detecting pieces of text lines in small overlapping columns of width , shifted with respect to each other by image elements (good default values are: of the image width, ) and by merging these pieces in a bottom-up fashion to form complete text lines and blocks of text lines. The algorithm requires about 1.3 s for a 300 dpi image on a PC with a Pentium II CPU, 300 MHz, MotherBoard Intel440LX. The algorithm is largely independent of the layout of the document, the shape of the text regions, and the font size and style. The main assumptions are that the background be uniform and that the text sit approximately horizontally. For a skew of up to about 10 degrees no skew correction mechanism is necessary. The algorithm has been tested on the UW English Document Database I of the University of Washington and its performance has been evaluated by a suitable measure of segmentation accuracy. Also, a detailed analysis of the segmentation accuracy achieved by the algorithm as a function of noise and skew has been carried out. Received April 4, 1999 / Revised June 1, 1999     

领域相关的数学文本语义抽取

针对科技领域文档语义信息获取不充分的问题,提出一套基于规则的数学领域相关文本的语义抽取方法。首先从文本中提取领域概念并实现数学实体与领域概念之间的语义映射;然后对数学符号的上下文进行分析,获取数学符号的实体指代或文字描述,进而抽取其语义;最后基于已抽取的数学符号语义实现表达式的语义分析。以线性代数文本为研究实例,构建了一个语义标注数据集并进行实验,实验结果表明所提方法对标识符、线性代数实体以及表达式的语义抽取具有93%以上的精确率和91%以上的召回率。     

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 line extraction from multi-skewed handwritten documents

A novel text line extraction technique is presented for multi-skewed document images of handwritten English or Bengali text. It assumes that hypothetical water flows, from both left and right sides of the image frame, face obstruction from characters of text lines. The stripes of areas left unwetted on the image frame are finally labelled for extraction of text lines. The success rate of the technique, as observed experimentally, are 90.34% and 91.44% for handwritten Bengali and English document images, respectively. The work may contribute significantly for the development of applications related to optical character recognition of Bengali/English text.     

Text line detection in handwritten documents

In this paper, we present a new text line detection method for handwritten documents. The proposed technique is based on a strategy that consists of three distinct steps. The first step includes image binarization and enhancement, connected component extraction, partitioning of the connected component domain into three spatial sub-domains and average character height estimation. In the second step, a block-based Hough transform is used for the detection of potential text lines while a third step is used to correct possible splitting, to detect text lines that the previous step did not reveal and, finally, to separate vertically connected characters and assign them to text lines. The performance evaluation of the proposed approach is based on a consistent and concrete evaluation methodology.     

Extraction and recognition of artificial text in multimedia documents

Abstract The systems currently available for contentbased image and video retrieval work without semantic knowledge, i. e. they use image processing methods to extract low level features of the data. The similarity obtained by these approaches does not always correspond to the similarity a human user would expect. A way to include more semantic knowledge into the indexing process is to use the text included in the images and video sequences. It is rich in information but easy to use, e. g. by key word based queries. In this paper we present an algorithm to localise artificial text in images and videos using a measure of accumulated gradients and morphological processing. The quality of the localised text is improved by robust multiple frame integration. A new technique for the binarisation of the text boxes based on a criterion maximizing local contrast is proposed. Finally, detection and OCR results for a commercial OCR are presented, justifying the choice of the binarisation technique.An erratum to this article can be found at     

线特征提取的多尺度分析

线特征提取是计算机视觉中重要的低级处理过程,而多尺度分析是采用微分几何方法进行线特征提取时一个重要内容。研究了在对不同宽度线特征进行检测时,尺度因子的选择问题,分析了变化的线宽与特定尺度因子间的关系,得到新的尺度因子确定方法。实验表明该方法简单、省时、有效。     

Layout extraction of mixed mode documents

Proper processing and efficient representation of the digitized images of printed documents require the separation of the various information types: text, graphics, and image elements. For most applications it is sufficient to separate text and nontext, because text contains the most information. This paper describes the implementation and performance of a robust algorithm for text extraction and segmentation that is completely independent of text orientation and can deal with text in various font styles and sizes. Text objects can be nested in nontext areas, and inverse printing can also be analyzed. It should be mentioned that the classification is based only on rough image features, and individual characters are not recognized. The three main processing steps of the system are the generation of connected components, neighborhood analysis, and generation of text lines and blocks. As output, connected components are classified as text or nontext. Text components are grouped as characters, words, lines, and blocks. Nontext objects are accumulated as a separate nontext block.     

Video text detection and segmentation for optical character recognition

In this paper, we present approaches to detecting and segmenting text in videos. The proposed video-text-detection technique is capable of adaptively applying appropriate operators for video frames of different modalities by classifying the background complexities. Effective operators such as the repeated shifting operations are applied for the noise removal of images with high edge density. Meanwhile, a text-enhancement technique is used to highlight the text regions of low-contrast images. A coarse-to-fine projection technique is then employed to extract text lines from video frames. Experimental results indicate that the proposed text-detection approach is superior to the machine-learning-based (such as SVM and neural network), multiresolution-based, and DCT-based approaches in terms of detection and false-alarm rates. Besides text detection, a technique for text segmentation is also proposed based on adaptive thresholding. A commercial OCR package is then used to recognize the segmented foreground text. A satisfactory character-recognition rate is reported in our experiments.Published online: 14 December 2004     

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.     

Multi-documents Automatic Abstracting based on text clustering and semantic analysis

A method of realization of multi-documents Automatic Abstracting based on text clustering and semantic analysis is brought forward, aimed at overcoming shortages of some current methods about multi-documents. The method makes use of semantic analysis and can realize Automatic Abstracting of multi-documents. The algorithm of twice word segmentation based on the title and first-sentences in paragraphs is brought forward. Its precision and recall is above 95%. For a specific domain on plastics, an Automatic Abstracting system named TCAAS is implemented. The precision and recall of multi-document’s Automatic Abstracting is above 75%. And experiments do prove that it is feasible to use the method to develop a domain Automatic Abstracting system, which is valuable for further study in more depth.     

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.     

基于结构相似网页聚类的正文提取算法研究

针对当前互联网网页越来越多样化、复杂化的特点,提出一种基于结构相似网页聚类的网页正文提取算法,首先,根据组成网页前端模板各“块”对模板的贡献赋以不同的权重,其次计算两个网页中对应块的相似度,将各块的相似度与权重乘积的总和作为两个网页的相似度。该算法充分考虑结构差别较大的网页对网页正文提取的影响,通过计算网页间相似度将网页聚类,使得同一簇中的网页正文提取结果更加准确。实验结果表明,该方法具有更高的准确率,各项评价指标均有所提高。     

Automatic text segmentation and text recognition for video indexing

Efficient indexing and retrieval of digital video is an important function of video databases. One powerful index for retrieval is the text appearing in them. It enables content-based browsing. We present our new methods for automatic segmentation of text in digital videos. The algorithms we propose make use of typical characteristics of text in videos in order to enable and enhance segmentation performance. The unique features of our approach are the tracking of characters and words over their complete duration of occurrence in a video and the integration of the multiple bitmaps of a character over time into a single bitmap. The output of the text segmentation step is then directly passed to a standard OCR software package in order to translate the segmented text into ASCII. Also, a straightforward indexing and retrieval scheme is introduced. It is used in the experiments to demonstrate that the proposed text segmentation algorithms together with existing text recognition algorithms are suitable for indexing and retrieval of relevant video sequences in and from a video database. Our experimental results are very encouraging and suggest that these algorithms can be used in video retrieval applications as well as to recognize higher level semantics in videos.