Text line extraction in graphical documents using background and foreground information

In graphical documents (e.g., maps, engineering drawings), artistic documents etc., the text lines are annotated in multiple orientations or curvilinear way to illustrate different locations or symbols. For the optical character recognition of such documents, individual text lines from the documents need to be extracted. In this paper, we propose a novel method to segment such text lines and the method is based on the foreground and background information of the text components. To effectively utilize the background information, a water reservoir concept is used here. In the proposed scheme, at first, individual components are detected and grouped into character clusters in a hierarchical way using size and positional information. Next, the clusters are extended in two extreme sides to determine potential candidate regions. Finally, with the help of these candidate regions, individual lines are extracted. The experimental results are presented on different datasets of graphical documents, camera-based warped documents, noisy images containing seals, etc. The results demonstrate that our approach is robust and invariant to size and orientation of the text lines present in the document.     

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.     

古代汉字文献切分研究

针对古代汉字文档的特点,提出了适合于古文档的列切分方法和字切分方法。提出的列切分方法直接对文档的笔画投影进行分析,采用一种基于分层投影过滤和变长间隙阈值的递归切分算法。该算法在列间隔较小、列与格线存在粘连、文档具有一定程度的倾斜的情况下,也能准确地抽取出列,尤其对短列的切分达到了较好的效果。提出的字切分方法分为两步,进行粗切分确定大致的切分位置,采用基于连通域分析与粘连点判断的方法做进一步的细切分。该算法对具有较多粘连和重叠汉字的列,也能较好地切分出完整的单字。实验结果表明,提出的方法用于古代汉字文档切分能够获得较好的效果。     

基于汉字连通分量的印刷图像版面分割方法

针对彩色印刷图像背景色彩丰富和汉字存在多个连通分量,连通域文字分割算法不能精确提取文字,提出基于汉字连通分量的彩色印刷图像版面分割方法。利用金字塔变换逆半调算法对图像进行预处理,通过颜色采样和均值偏移分割图像颜色,标记文字连通分量,根据汉字结构和连通分量特性重建汉字连通分量,分析文字连通分量连接关系确定文字排列方向实现文字分割。实验结果表明,该方法能够有效地重建汉字连通分量,在彩色印刷图像中实现对不同字体、字号、颜色的文字分割。     

Multi-oriented touching text character segmentation in graphical documents using dynamic programming

The touching character segmentation problem becomes complex when touching strings are multi-oriented. Moreover in graphical documents sometimes characters in a single-touching string have different orientations. Segmentation of such complex touching is more challenging. In this paper, we present a scheme towards the segmentation of English multi-oriented touching strings into individual characters. When two or more characters touch, they generate a big cavity region in the background portion. Based on the convex hull information, at first, we use this background information to find some initial points for segmentation of a touching string into possible primitives (a primitive consists of a single character or part of a character). Next, the primitives are merged to get optimum segmentation. A dynamic programming algorithm is applied for this purpose using the total likelihood of characters as the objective function. A SVM classifier is used to find the likelihood of a character. To consider multi-oriented touching strings the features used in the SVM are invariant to character orientation. Experiments were performed in different databases of real and synthetic touching characters and the results show that the method is efficient in segmenting touching characters of arbitrary orientations and sizes.     

基于Parzen窗的印刷文档数学公式抽取的研究

数学公式抽取是公式识别的首要步骤,目前相关的研究还很欠缺。针对印刷文档中数学公式的抽取展开了研究,提出了一种Parzen窗和启发式规则相结合的公式抽取方法。对于孤立式公式采用Parzen窗方法将其从文档中抽取出来,对于嵌入式公式采用启发式规则将其从文本行中抽取出来。实验表明,这两种抽取方法的结合取得了较好的效果。     

Word searching in unconstrained layout using character pair coding

Word searching in non-structural layout such as graphical documents is a difficult task due to arbitrary orientations of text words and the presence of graphical symbols. This paper presents an efficient approach for word searching in documents of non-structural layout using an efficient indexing and retrieval approach. The proposed indexing scheme stores spatial information of text characters of a document using a character spatial feature table (CSFT). The spatial feature of text component is derived from the neighbor component information. The character labeling of a multi-scaled and multi-oriented component is performed using support vector machines. For searching purpose, the positional information of characters is obtained from the query string by splitting it into possible combinations of character pairs. Each of these character pairs searches the position of corresponding text in document with the help of CSFT. Next, the searched text components are joined and formed into sequence by spatial information matching. String matching algorithm is performed to match the query word with the character pair sequence in documents. The experimental results are presented on two different datasets of graphical documents: maps dataset and seal/logo image dataset. The results show that the method is efficient to search query word from unconstrained document layouts of arbitrary orientation.     

Adaptive document block segmentation and classification

This paper presents an adaptive block segmentation and classification technique for daily-received office documents having complex layout structures such as multiple columns and mixed-mode contents of text, graphics, and pictures. First, an improved two-step block segmentation algorithm is performed based on run-length smoothing for decomposing any document into single-mode blocks. Then, a rule-based block classification is used for classifying each block into the text, horizontal/vertical line, graphics, or-picture type. The document features and rules used are independent of character font and size and the scanning resolution. Experimental results show that our algorithms are capable of correctly segmenting and classifying different types of mixed-mode printed documents.     

基于统计特征的数学公式抽取方法的研究

在分析公式特征的基础上，提出了一种将Parezen窗和Bayes分类规则相结合的公式抽取方法。对于孤立式公式采用改进后的Parzen窗方法将其从文档中抽取出来，对于内嵌公式通过Bayes分类规则将其从文本行中抽取出来。实验表明，这种抽取方法对中文文档具有较好的适应性和较高的成功率。     

Open curve segmentation via a two-phase scheme

A two-phase scheme is proposed for the segmentation of open curves. In the first phase, the curvature of each point of the curve is calculated based on the coordinates of five skipped points and the assumption that these five points form a B-spline curve. The points with significant curvatures are collected as candidate corners. During the second phase, the candidate corners are verified for deviations in the global trend of the curve. Those with small deviations are considered excessive and are removed. Application of the proposed method in the segmentation of a typical PCB (printed circuit board) curve, an aerial image of Medioni and Yasumoto (Comput. Vision Graphics Image Process. 39, 267–278 (1987)), and a multi-scale four-semicircle curve of Teh and Chin (IEEE Trans. Pattern Analysis Mach. Intell. PAMI-11, 859–872 (1989)) show that it is both minimal and accurate.     

基于神经网络的印刷体数学公式抽取方法*

在分析中文印刷文档版式及字符特征的基础上,提出了一种将决策树与BP神经网络相结合的数学公式抽取方法。采用决策树方法将孤立公式从文档中抽取出来,采用BP神经网络方法定位内嵌公式。实验表明,该抽取方法对中文文档的公式抽取具有较高的正确率、容错率和速率。     

A word extraction algorithm for machine-printed documents using a 3D neighborhood graph model

Automatic character recognition and image understanding of a given paper document are the main objectives of the computer vision field. For these problems, a basic step is to isolate characters and group words from these isolated characters. In this paper, we propose a new method for extracting characters from a mixed text/graphic machine-printed document and an algorithm for distinguishing words from the isolated characters. For extracting characters, we exploit several features (size, elongation, and density) of characters and propose a characteristic value for classification using the run-length frequency of the image component. In the context of word grouping, previous works have largely been concerned with words which are placed on a horizontal or vertical line. Our word grouping algorithm can group words which are on inclined lines, intersecting lines, and even curved lines. To do this, we introduce the 3D neighborhood graph model which is very useful and efficient for character classification and word grouping. In the 3D neighborhood graph model, each connected component of a text image segment is mapped onto 3D space according to the area of the bounding box and positional information from the document. We conducted tests with more than 20 English documents and more than ten oriental documents scanned from books, brochures, and magazines. Experimental results show that more than 95% of words are successfully extracted from general documents, even in very complicated oriental documents. Received August 3, 2001 / Accepted August 8, 2001     

Finding words in alphabet soup: Inference on freeform character recognition for historical scripts

This paper develops word recognition methods for historical handwritten cursive and printed documents. It employs a powerful segmentation-free letter detection method based upon joint boosting with histograms of gradients as features. Efficient inference on an ensemble of hidden Markov models can select the most probable sequence of candidate character detections to recognize complete words in ambiguous handwritten text, drawing on character n-gram and physical separation models. Experiments with two corpora of handwritten historic documents show that this approach recognizes known words more accurately than previous efforts, and can also recognize out-of-vocabulary words.     

一种现代藏文笔段提取算法

针对藏文字符笔段的几何特征和拓扑结构,本文提出了一种基于字符轮廓信息的藏文笔段提取算法:通过链码跟踪的方法得到笔段轮廓的点列,然后从点列中提取特征点并利用特征点切分出笔段,最后用笔段的轮廓线代替骨架线来表征藏文的笔段。本算法用于印刷体藏文笔段提取,取得了良好的效果,避免了传统细化算法所造成的畸变,提高了笔段提取的抗干扰能力,并减小了计算量,加快了特征提取的速度。