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     

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.     

基于知识块摘要和词转移距离的高效司法文档分类

随着全国司法机关智能化建设的深入推进,通过信息化建设应用所积累的海量司法文书为开展司法智能服务提供了司法数据分析基础。通过司法文书的相似性分析实现类案推送,可以为司法人员提供智能辅助办案决策支持,从而提高办案的质量和效率。针对面向通用领域的文本分类方法因没有考虑特定司法领域文本的复杂结构和知识语义而导致司法文本分类的效能低问题,提出一种基于司法知识块摘要和词转移距离（WMD）的高效司法文档分类方法。首先为司法文书构建领域本体知识模型,进而基于领域本体,利用信息抽取技术获取司法文档中核心知识块摘要;然后基于司法文本的知识块摘要利用WMD进行司法文档相似度计算;最后利用K最近邻算法进行司法文本分类。以两个典型罪名的案件文档集作为实验数据,与传统的WMD文档相似度计算方法进行对比,实验结果表明,所提方法能明显提高司法文本分类的正确率（分别有5.5和9.9个百分点的提升）,同时也降低了文档分类所需的时间（速度分别提升到原来的52.4和89.1倍）。     

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.     

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.     

Text extraction in complex color documents

Text extraction in mixed-type documents is a pre-processing and necessary stage for many document applications. In mixed-type color documents, text, drawings and graphics appear with millions of different colors. In many cases, text regions are overlaid onto drawings or graphics. In this paper, a new method to automatically detect and extract text in mixed-type color documents is presented. The proposed method is based on a combination of an adaptive color reduction (ACR) technique and a page layout analysis (PLA) approach. The ACR technique is used to obtain the optimal number of colors and to convert the document into the principal of them. Then, using the principal colors, the document image is split into the separable color plains. Thus, binary images are obtained, each one corresponding to a principal color. The PLA technique is applied independently to each of the color plains and identifies the text regions. A merging procedure is applied in the final stage to merge the text regions derived from the color plains and to produce the final document. Several experimental and comparative results, exhibiting the performance of the proposed technique, are also presented.     

Classification of document pages using structure-based features

Searching for documents by their type or genre is a natural way to enhance the effectiveness of document retrieval. The layout of a document contains a significant amount of information that can be used to classify it by type in the absence of domain-specific models. Our approach to classification is based on “visual similarity” of layout structure and is implemented by building a supervised classifier, given examples of each class. We use image features such as percentages of text and non-text (graphics, images, tables, and rulings) content regions, column structures, relative point sizes of fonts, density of content area, and statistics of features of connected components which can be derived without class knowledge. In order to obtain class labels for training samples, we conducted a study where subjects ranked document pages with respect to their resemblance to representative page images. Class labels can also be assigned based on known document types, or can be defined by the user. We implemented our classification scheme using decision tree classifiers and self-organizing maps. Received June 15, 2000 / Revised November 15, 2000     

Document segmentation using textural features summarization and feedforward neural network

Document Segmentation is a process that aims to filter documents while identifying certain regions of interest. Generally, the regions of interest include texts, graphics (image occupied regions) and the background. This paper presents a novel top-bottom approach to perform document segmentation using texture features that are extracted from the specified/selected documents. A mask of suitable size is used to summarize textural features, and statistical parameters are captured as blocks in document images. Four textural features that are extracted from masks using the gray level co-occurrence matrix (glcm) include entropy, contrast, energy and homogeneity. Furthermore, two statistical parameters extracted from corresponding masks are the modal and median pixel values. The extracted attributes allow the classification of each mask or block as text, graphics, and background. A feedforward network is trained on the 6 extracted attributes, using documents obtained from a public database ; an error rate of 15.77 % is achieved. Furthermore, it is shown that this novel approach produces promising performance in segmenting documents and is expected to be significantly efficient for content-based information retrieval systems. Detection of duplicate documents within large databases is another potential area of application.     

Ensemble of keyword extraction methods and classifiers in text classification

Automatic keyword extraction is an important research direction in text mining, natural language processing and information retrieval. Keyword extraction enables us to represent text documents in a condensed way. The compact representation of documents can be helpful in several applications, such as automatic indexing, automatic summarization, automatic classification, clustering and filtering. For instance, text classification is a domain with high dimensional feature space challenge. Hence, extracting the most important/relevant words about the content of the document and using these keywords as the features can be extremely useful. In this regard, this study examines the predictive performance of five statistical keyword extraction methods (most frequent measure based keyword extraction, term frequency-inverse sentence frequency based keyword extraction, co-occurrence statistical information based keyword extraction, eccentricity-based keyword extraction and TextRank algorithm) on classification algorithms and ensemble methods for scientific text document classification (categorization). In the study, a comprehensive study of comparing base learning algorithms (Naïve Bayes, support vector machines, logistic regression and Random Forest) with five widely utilized ensemble methods (AdaBoost, Bagging, Dagging, Random Subspace and Majority Voting) is conducted. To the best of our knowledge, this is the first empirical analysis, which evaluates the effectiveness of statistical keyword extraction methods in conjunction with ensemble learning algorithms. The classification schemes are compared in terms of classification accuracy, F-measure and area under curve values. To validate the empirical analysis, two-way ANOVA test is employed. The experimental analysis indicates that Bagging ensemble of Random Forest with the most-frequent based keyword extraction method yields promising results for text classification. For ACM document collection, the highest average predictive performance (93.80%) is obtained with the utilization of the most frequent based keyword extraction method with Bagging ensemble of Random Forest algorithm. In general, Bagging and Random Subspace ensembles of Random Forest yield promising results. The empirical analysis indicates that the utilization of keyword-based representation of text documents in conjunction with ensemble learning can enhance the predictive performance and scalability of text classification schemes, which is of practical importance in the application fields of text classification.     

Development of a patent document classification and search platform using a back-propagation network

In order to process large numbers of explicit knowledge documents such as patents in an organized manner, automatic document categorization and search are required. In this paper, we develop a document classification and search methodology based on neural network technology that helps companies manage patent documents more effectively. The classification process begins by extracting key phrases from the document set by means of automatic text processing and determining the significance of key phrases according to their frequency in text. In order to maintain a manageable number of independent key phrases, correlation analysis is applied to compute the similarities between key phrases. Phrases with higher correlations are synthesized into a smaller set of phrases. Finally, the back-propagation network model is adopted as a classifier. The target output identifies a patent document’s category based on a hierarchical classification scheme, in this case, the international patent classification (IPC) standard. The methodology is tested using patents related to the design of power hand-tools. Related patents are automatically classified using pre-trained neural network models. In the prototype system, two modules are used for patent document management. The automatic classification module helps the user classify patent documents and the search module helps users find relevant and related patent documents. The result shows an improvement in document classification and identification over previously published methods of patent document management.     

Transforming paper documents into XML format with WISDOM++

The transformation of scanned paper documents to a form suitable for an Internet browser is a complex process that requires solutions to several problems. The application of an OCR to some parts of the document image is only one of the problems. In fact, the generation of documents in HTML format is easier when the layout structure of a page has been extracted by means of a document analysis process. The adoption of an XML format is even better, since it can facilitate the retrieval of documents in the Web. Nevertheless, an effective transformation of paper documents into this format requires further processing steps, namely document image classification and understanding. WISDOM++ is a document processing system that operates in five steps: document analysis, document classification, document understanding, text recognition with an OCR, and transformation into HTML/XML format. The innovative aspects described in the paper are: the preprocessing algorithm, the adaptive page segmentation, the acquisition of block classification rules using techniques from machine learning, the layout analysis based on general layout principles, and a method that uses document layout information for conversion to HTML/XML formats. A benchmarking of the system components implementing these innovative aspects is reported. Received June 15, 2000 / Revised November 7, 2000     

语义驱动的司法文档学习分类方法

基于海量的司法文书进行的高效司法文档分类有助于目前的司法智能化应用，如类案推送、文书检索、判决预测和量刑辅助等。面向通用领域的文本分类方法因没有考虑司法领域文本的复杂结构和知识语义，导致司法文本分类的效能很低。针对该问题提出了一种语义驱动的方法来学习和分类司法文书。首先，提出并构建了面向司法领域的领域知识模型以清晰表达文档级语义；然后，基于该模型对司法文档进行相应的领域知识抽取；最后，利用图长短期记忆模型（Graph LSTM）对司法文书进行训练和分类。实验结果表明该方法在准确率和召回率方面明显优于常用的长短期记忆（LSTM）模型、多类别逻辑回归和支持向量机等方法。     

The Indexing and Retrieval of Document Images: A Survey

The economic feasibility of maintaining large data bases of document images has created a tremendous demand for robust ways to access and manipulate the information these images contain. In an attempt to move toward a paperless office, large quantities of printed documents are often scanned and archived as images, without adequate index information. One way to provide traditional data-base indexing and retrieval capabilities is to fully convert the document to an electronic representation which can be indexed automatically. Unfortunately, there are many factors which prohibit complete conversion including high cost, low document quality, and the fact that many nontext components cannot be adequately represented in a converted form. In such cases, it can be advantageous to maintain a copy of and use the document in image form. In this paper, we provide a survey of methods developed by researchers to access and manipulate document images without the need for complete and accurate conversion. We briefly discuss traditional text indexing techniques on imperfect data and the retrieval of partially converted documents. This is followed by a more comprehensive review of techniques for the direct characterization, manipulation, and retrieval, of images of documents containing text, graphics, and scene images.     

一种面向领域文档的结构化检索模型及其在农技处方检索中的应用

各种专业领域中的文档往往具有显著的结构化特征,即一篇文档往往是由具有不同表达功能的相对固定的多个文本字段构成,同时这些字段蕴含了相关的领域知识。针对专业文档的结构化和领域化特征,设计了一种面向结构化领域文档的信息检索模型。在该模型中,首先对领域文档集进行挖掘以构建能够反映领域知识的结构化模型,之后以此为基础设计了结构化文档检索算法来为用户查询返回相关的领域文档。选择一类典型的领域文档——农技处方开展了应用研究,利用一份现实的农技处方文档数据集将提出的方法与传统的信息检索方法进行了实验对比分析,并开发了农技处方检索原型系统。     

Using text classification and multiple concepts to answer e-mails

In text mining, the applications domain of text classification techniques is very broad to include text filtering, word identification, and web page classification, etc. Through text classification techniques, documents can be placed into previously defined classifications in order to save on time costs especially when manual document search methods are employed. This research uses text classification techniques applied to e-mail reply template suggestions in order to lower the burden of customer service personnel in responding to e-mails. Suggested templates allows customer service personnel, using a pre-determined number of templates, to find the needed reply template, and not waste time in searching for relevant answers from too much information available. Current text classification techniques are still single-concept based. This research hopes to use a multiple concept method to integrate the relationship between concepts and classifications which will thus allow easy text classification. Through integration of different concepts and classifications, a dynamically unified e-mail concept can recommend different appropriate reply templates. In so doing, the differences between e-mails can be definitely determined, effectively improving the accuracy of the suggested template. In addition, for e-mails with two or more questions, this research tries to come up with an appropriate reply template. Based on experimental verification, the method proposed in this research effectively proposes a template for e-mails of multiple questions. Therefore, using multiple concepts to display the document topic is definitely a clearer way of extracting information that a document wants to convey when the vector of similar documents is used.     

An alternative,layout‐driven approach to the clustering of documents

Internet has become a huge repository of information and knowledge, based on the sharing of the electronic documents. Last trends in knowledge management focus on the knowledge representation based on the document content. In fact, most accustomed approaches achieve the document understanding by analyzing the “portions of information'' in the document which describe the content, through techniques of text parsing and extraction. This paper presents an alternative approach that departs from the consolidated techniques of document management and focuses on the logical structure of a PDF document as a discriminating source of document knowledge. The main idea is based on the fact, when the reader looks at a paper, his first perception is related to the layout of the document. The analysis of layout, typesetting, paginating, and graphical arrangement of a document provides interesting information about its content understanding; in general, the documents that are in the same category present similar page layout, fonts, and figures arrangement. In this sense, this work presents an alternative way to deal with documents recognition and understanding, through the analysis of the layout of electronic PDF documents and their classification. © 2008 Wiley Periodicals, Inc.     

An information fusion approach to integrate image annotation and text mining methods for geographic knowledge discovery

Due to the steady increase in the number of heterogeneous types of location information on the internet, it is hard to organize a complete overview of the geospatial information for the tasks of knowledge acquisition related to specific geographic locations. The text- and photo-types of geographical dataset contain numerous location data, such as location-based tourism information, therefore defining high dimensional spaces of attributes that are highly correlated. In this work, we utilized text- and photo-types of location information with a novel approach of information fusion that exploits effective image annotation and location based text-mining approaches to enhance identification of geographic location and spatial cognition. In this paper, we describe our feature extraction methods to annotating images, and utilizing text mining approach to analyze images and texts simultaneously, in order to carry out geospatial text mining and image classification tasks. Subsequently, photo-images and textual documents are projected to a unified feature space, in order to generate a co-constructed semantic space for information fusion. Also, we employed text mining approaches to classify documents into various categories based upon their geospatial features, with the aims to discovering relationships between documents and geographical zones. The experimental results show that the proposed method can effectively enhance the tasks of location based knowledge discovery.