The course description provided by instructors is an important piece of information as it defines what is expected from the instructor and what he she is going to deliver during a particular course. One of the key components of a course description is the Learning Outcomes section. The contents of this section are used by program managers who are tasked to compare and match two different courses during the development of Transfer Agreements between different institutions. This research introduces the development of visual tools for understanding the two different courses and making comparisons. We designed methods to extract the text from a course description document, developed an algorithm to perform semantic analysis, and displayed the results in a web interface. We are able to achieve the intermediate results of the research which includes extracting, analyzing and visualizing the data.
The extraction process focuses on segmentation process which involves dividing the document into blocks that are the smallest logical entity and then proceed with extraction in the later stages @cite_8 . The segmentation process is divided into sub parts, which include generating neighbourhood graph, creating page divisions and generating whitespace density graph. For our research, we need to detect headings to make sure the algorithm knows from which part of the document to extract the text. To make sure that the extracted text does not have document header footer or text from another columns(if document has multiple columns), detection of header footer text and multiple columns is also required. So, the most efficient way for our application is to detect the relevant headings and then analyze the area of the document which contains that heading before extracting the text. The authors have not seen such work before.
@cite_8
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The goal of information extraction is to extract database records from text or semi-structured sources. Traditionally, information extraction proceeds by first segmenting each candidate record separately, and then merging records that refer to the same entities. While computationally efficient, this approach is suboptimal, because it ignores the fact that segmenting one candidate record can help to segment similar ones. For example, resolving a well-segmented field with a less-clear one can disambiguate the latter's boundaries. In this paper we propose a joint approach to information extraction, where segmentation of all records and entity resolution are performed together in a single integrated inference process. While a number of previous authors have taken steps in this direction (eg., (2003), (2004)), to our knowledge this is the first fully joint approach. In experiments on the CiteSeer and Cora citation matching datasets, joint inference improved accuracy, and our approach outperformed previous ones. Further, by using Markov logic and the existing algorithms for it, our solution consisted mainly of writing the appropriate logical formulas, and required much less engineering than previous ones. This paper presents a scene text extraction technique that automatically detects and segments texts from scene images. Three text-specific features are designed over image edges with which a set of candidate text boundaries is first detected. For each detected candidate text boundary, one or more candidate characters are then extracted by using a local threshold that is estimated based on the surrounding image pixels. The real characters and words are finally identified by a support vector regression model that is trained using bags-of-words representation. The proposed technique has been evaluated over the latest ICDAR-2013 Robust Reading Competition dataset. Experiments show that it obtains superior F-measures of 78.19 and 75.24 (on atom level), respectively, for the scene text detection and segmentation tasks. Scene text extraction, i.e., segmenting text pixels from background, is an important step before the text can be recognized. It is a challenging problem due to the cluttered background and the variation of lighting. In this paper, we propose a seed-based segmentation method that can automatically judge the text polarity, extract seed points of text and background, and segment texts by semi-supervised learning (SSL). First, we estimate the text polarity and the stroke width using gradient local correlation. Then, all the points in the middle of stroke edge pairs satisfying the width and polarity are taken as foreground seeds, and the points in the middle of the edge pairs with opposite polarity are taken as background seeds. The whole image is then segmented into text and background using an SSL algorithm. Owing to the accurate estimate of text polarity and extraction of seed points, the proposed method yields good segmentation performance. Experimental results on the KAIST dataset demonstrate the superiority of the method. This paper is concerned with keyword extraction. By keyword extraction, we mean extracting a subset of words phrases from a document that can describe the ‘meaning' of the document. Keywords are of benefit to many text mining applications. However, a large number of documents do not have keywords and thus it is necessary to assign keywords before enjoying the benefit from it. Several research efforts have been done on keyword extraction. These methods make use of the ‘global context information', which makes the performance of extraction restricted. A thorough and systematic investigation on the issue is thus needed. In this paper, we propose to make use of not only ‘global context information', but also ‘local context information' for extracting keywords from documents. As far as we know, utilizing both ‘global context information' and ‘local context information' in keyword extraction has not been sufficiently investigated previously. Methods for performing the tasks on the basis of Support Vector Machines have also been proposed in this paper. Features in the model have been defined. Experimental results indicate that the proposed SVM based method can significantly outperform the baseline methods for keyword extraction. The proposed method has been applied to document classification, a typical text mining processing. Experimental results show that the accuracy of document classification can be significantly improved by using the keyword extraction method. We propose an approach for information extraction for multi-page printed document understanding. The approach is designed for scenarios in which the set of possible document classes, i.e., documents sharing similar content and layout, is large and may evolve over time. Describing a new class is a very simple task: the operator merely provides a few samples and then, by means of a GUI, clicks on the OCR-generated blocks of a document containing the information to be extracted. Our approach is based on probability: we derived a general form for the probability that a sequence of blocks contains the searched information. We estimate the parameters for a new class by applying the maximum likelihood method to the samples of the class. All these parameters depend only on block properties that can be extracted automatically from the operator actions on the GUI. Processing a document of a given class consists in finding the sequence of blocks, which maximizes the corresponding probability for that class. We evaluated experimentally our proposal using 807 multi-page printed documents of different domains (invoices, patents, data-sheets), obtaining very good results—e.g., a success rate often greater than 90 even for classes with just two samples. We present a novel method for key term extraction from text documents. In our method, document is modeled as a graph of semantic relationships between terms of that document. We exploit the following remarkable feature of the graph: the terms related to the main topics of the document tend to bunch up into densely interconnected subgraphs or communities, while non-important terms fall into weakly interconnected communities, or even become isolated vertices. We apply graph community detection techniques to partition the graph into thematically cohesive groups of terms. We introduce a criterion function to select groups that contain key terms discarding groups with unimportant terms. To weight terms and determine semantic relatedness between them we exploit information extracted from Wikipedia. Using such an approach gives us the following two advantages. First, it allows effectively processing multi-theme documents. Second, it is good at filtering out noise information in the document, such as, for example, navigational bars or headers in web pages. Evaluations of the method show that it outperforms existing methods producing key terms with higher precision and recall. Additional experiments on web pages prove that our method appears to be substantially more effective on noisy and multi-theme documents than existing methods. We present an end-to-end, multimodal, fully convolutional network for extracting semantic structures from document images. We consider document semantic structure extraction as a pixel-wise segmentation task, and propose a unified model that classifies pixels based not only on their visual appearance, as in the traditional page segmentation task, but also on the content of underlying text. Moreover, we propose an efficient synthetic document generation process that we use to generate pretraining data for our network. Once the network is trained on a large set of synthetic documents, we fine-tune the network on unlabeled real documents using a semi-supervised approach. We systematically study the optimum network architecture and show that both our multimodal approach and the synthetic data pretraining significantly boost the performance. Page segmentation and table detection play an important role in understanding the structure of documents. We present a page segmentation algorithm that incorporates state-of-the-art deep learning methods for segmenting three types of document elements: text blocks, tables, and figures. We propose a multi-scale, multi-task fully convolutional neural network (FCN) for the tasks of semantic page segmentation and element contour detection. The semantic segmentation network accurately predicts the probability at each pixel of the three element classes. The contour detection network accurately predicts instance level "edges" around each element occurrence. We propose a conditional random field (CRF) that uses features output from the semantic segmentation and contour networks to improve upon the semantic segmentation network output. Given the semantic segmentation output, we also extract individual table instances from the page using some heuristic rules and a verification network to remove false positives. We show that although we only consider a page image as input, we produce comparable results with other methods that relies on PDF file information and heuristics and hand crafted features tailored to specific types of documents. Our approach learns the representative features for page segmentation from real and synthetic training data. , and produces good results on real documents. The learning-based property makes it a more general method than existing methods in terms of document types and element appearances. For example, our method reliably detects sparsely lined tables which are hard for rule-based or heuristic methods. Text line extraction is vital pre-requisite for various document processing tasks. This paper presents a novel approach for text line extraction which is based on Gaussian scale space and dedicated binarization that utilize the inherent structure of smoothed text document images. It enhances the text lines in the image using multi-scale anisotropic second derivative of Gaussian filter bank at the average height of the text line. It then applies a binarization, which is based on component-tree and is tailored towards line extraction. The final stage of the algorithm is based on an energy minimization framework for removing spurious text line and assigning connected components to lines. We have tested our approach on various datasets written in different languages at range of image quality and received high detection rates, which outperform state-of-the-art algorithms. Our MATLAB code is publicly available. (http: www.cs.bgu.ac.il rafico LineExtraction.zip) In this paper, we present a segmentation methodology of handwritten documents in their distinct entities, namely, text lines and words. Text line segmentation is achieved by applying Hough transform on a subset of the document image connected components. A post-processing step includes the correction of possible false alarms, the detection of text lines that Hough transform failed to create and finally the efficient separation of vertically connected characters using a novel method based on skeletonization. Word segmentation is addressed as a two class problem. The distances between adjacent overlapped components in a text line are calculated using the combination of two distance metrics and each of them is categorized either as an inter- or an intra-word distance in a Gaussian mixture modeling framework. The performance of the proposed methodology is based on a consistent and concrete evaluation methodology that uses suitable performance measures in order to compare the text line segmentation and word segmentation results against the corresponding ground truth annotation. The efficiency of the proposed methodology is demonstrated by experimentation conducted on two different datasets: (a) on the test set of the ICDAR2007 handwriting segmentation competition and (b) on a set of historical handwritten documents. Proposing a candidate retrieval model for cross-lingual plagiarism detectionThe method relies on using two levels of proximity informationProposing a topic-based text segmentation methodComparing the method with other cross-lingual plagiarism detection approachesShowing improvements using text segmentation and positional language models The rapid growth of documents in different languages, the increased accessibility of electronic documents, and the availability of translation tools have caused cross-lingual plagiarism detection research area to receive increasing attention in recent years. The task of cross-language plagiarism detection entails two main steps: candidate retrieval and assessing pairwise document similarity. In this paper we examine candidate retrieval, where the goal is to find potential source documents of a suspicious text. Our proposed method for cross-language plagiarism detection is a keyword-focused approach. Since plagiarism usually happens in parts of the text, there is a requirement to segment the texts into fragments to detect local similarity. Therefore we propose a topic-based segmentation algorithm to convert the suspicious document to a set of related passages. After that, we use a proximity-based model to retrieve documents with the best matching passages. Experiments show promising results for this important phase of cross-language plagiarism detection. This paper describes new methods of automatically extracting documents for screening purposes, i.e. the computer selection of sentences having the greatest potential for conveying to the reader the substance of the document. While previous work has focused on one component of sentence significance, namely, the presence of high-frequency content words (key words), the methods described here also treat three additional components: pragmatic words (cue words); title and heading words; and structural indicators (sentence location). The research has resulted in an operating system and a research methodology. The extracting system is parameterized to control and vary the influence of the above four components. The research methodology includes procedures for the compilation of the required dictionaries, the setting of the control parameters, and the comparative evaluation of the automatic extracts with manually produced extracts. The results indicate that the three newly proposed components dominate the frequency component in the production of better extracts. A major problem among the elderly involves falling. The recognition of falls from video first requires the segmentation of the individual from the background. To ensure privacy, segmentation should result in a silhouette that is a binary map indicating only the body position of the individual in an image. We have previously demonstrated a segmentation method based on color that can recognize the silhouette and detect and remove shadows. After the silhouettes are obtained, we extract features and train hidden Markov models to recognize future performances of these known activities. In this paper, we present preliminary results that demonstrate the usefulness of this approach for distinguishing between a few common activities, specifically with fall detection in mind. and dynamic environments. High-level knowledge was fused with low-level feature-based classification to handle a time-varying background, and a decision process based on a fuzzy logic inference system was used to detach the moving objects from the human silhouette. One central task in silhouette extraction is background modeling. Once a background model is established, those image regions with significantly different characteristics from the background are considered foreground objects. In (5), a Least Median of Squares method was used to construct a background model, and in (6), a differencing function was proposed to extract moving human silhouettes. (7) demonstrated a non-parametric kernel density estimation method to model and subtract the human from a background. The adaptive background updating handles small motion in the background scene, such as moving tree branches. A slightly different approach outlined in (8) is based on processing in the YUV color space, and is capable of detecting shadows and extracting 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. extraction is an efficient approach to managing an explosion of online text on the Web. Traditionally, an abstraction of the online text is constructed though keywords, which are extracted according to a certain importance measure. One such measure is their occurrence frequency. However, previous work has not considered another important factor: the diversity of the keywords. Therefore, the extracted keywords tend to crowd on one hot topic in the corpora while failing to cover other important topics. In this paper, we propose new algorithms to alleviate the disadvantages of these traditional methods for keyword extraction. Firstly, we propose to extract key phrases instead of keywords because phrases can effectively reduce the ambiguity of single words. Secondly, by leveraging latent semantic analysis, we can learn the related topics for each phrase as well as the distance among the phrases, so that the extracted phrases are able to cover more topics. To demonstrate the performance of our method, we conducted experiments on two open datasets: 20 Newsgroup and Reuters-21578.We design three novel evaluation metrics, based on which both qualitative and quantitative analyses shows that our proposed algorithm can be used to improve the key phrase extraction performance significantly. Information extraction is a form of shallow text processing that locates a specified set of relevant items in a natural-language document. Systems for this task require significant domain-specific knowledge and are time-consuming and difficult to build by hand, making them a good application for machine learning. We present a system, RAPIER, that uses pairs of sample documents and filled templates to induce pattern-match rules that directly extract fillers for the slots in the template. RAPIER employs a bottom-up learning algorithm which incorporates techniques from several inductive logic programming systems and acquires unbounded patterns that include constraints on the words, part-of-speech tags, and semantic classes present in the filler and the surrounding text. We present encouraging experimental results on two domains. In this paper, we present a new text line extraction method for handwritten Arabic documents. The proposed technique is based on a generalized adaptive local connectivity map (ALCM) using a steerable directional filter. The algorithm is designed to solve the particularly complex problems seen in handwritten documents such as fluctuating, touching or crossing text lines. The proposed algorithm consists of three steps. Firstly, a steerable filter is used to probe and determine foreground intensity along multiple directions at each pixel while generating the ALCM. The ALCM is then binarized using an adaptive thresholding algorithm to get a rough estimate of the location of the text lines. In the second step, connected component analysis is used to classify text and non text patterns in the generated ALCM to refine the location of the text lines. Finally, the text lines are separated by superimposing the text line patterns in the ALCM on the original document image and extracting the connected components covered by the pattern mask. Analysis of experimental results on the DARPA MADCAT Arabic handwritten document data indicate that the method is robust and is capable of correctly isolating handwritten text lines even on challenging document images. In the context of historical collection conservation and worldwide diffusion, this paper presents an automatic approach of historical book page layout segmentation. In this article, we propose to search the homogeneous regions from the content of historical digitized books with little a priori knowledge by extracting and analyzing texture features. The novelty of this work lies in the unsupervised clustering of the extracted texture descriptors to find homogeneous regions, i.e. graphic and textual regions, by performing the clustering approach on an entire book instead of processing each page individually. We propose firstly to characterize the content of an entire book by extracting the texture information of each page, as our goal is to compare and index the content of digitized books. The extraction of texture features, computed without any hypothesis on the document structure, is based on two non-parametric tools: the autocorrelation function and multiresolution analysis. Secondly, we perform an unsupervised clustering approach on the extracted features in order to classify automatically the homogeneous regions of book pages. The clustering results are assessed by internal and external accuracy measures. The overall results are quite satisfying. Such analysis would help to construct a computer-aided categorization tool of pages. Scene parsing, or semantic segmentation, consists in labeling each pixel in an image with the category of the object it belongs to. It is a challenging task that involves the simultaneous detection, segmentation and recognition of all the objects in the image. The scene parsing method proposed here starts by computing a tree of segments from a graph of pixel dissimilarities. Simultaneously, a set of dense feature vectors is computed which encodes regions of multiple sizes centered on each pixel. The feature extractor is a multiscale convolutional network trained from raw pixels. The feature vectors associated with the segments covered by each node in the tree are aggregated and fed to a classifier which produces an estimate of the distribution of object categories contained in the segment. A subset of tree nodes that cover the image are then selected so as to maximize the average "purity" of the class distributions, hence maximizing the overall likelihood that each segment will contain a single object. The convolutional network feature extractor is trained end-to-end from raw pixels, alleviating the need for engineered features. After training, the system is parameter free. The system yields record accuracies on the Stanford Background Dataset (8 classes), the Sift Flow Dataset (33 classes) and the Barcelona Dataset (170 classes) while being an order of magnitude faster than competing approaches, producing a 320 240 image labeling in less than 1 second. This dissertation investigates the role of contextual information in the automated retrieval and display of full-text documents, using robust natural language processing algorithms to automatically detect structure in and assign topic labels to texts. Many long texts are comprised of complex topic and subtopic structure, a fact ignored by existing information access methods. I present two algorithms which detect such structure, and two visual display paradigms which use the results of these algorithms to show the interactions of multiple main topics, multiple subtopics, and the relations between main topics and subtopics. The first algorithm, called TextTiling , recognizes the subtopic structure of texts as dictated by their content. It uses domain-independent lexical frequency and distribution information to partition texts into multi-paragraph passages. The results are found to correspond well to reader judgments of major subtopic boundaries. The second algorithm assigns multiple main topic labels to each text, where the labels are chosen from pre-defined, intuitive category sets; the algorithm is trained on unlabeled text. A new iconic representation, called TileBars uses TextTiles to simultaneously and compactly display query term frequency, query term distribution and relative document length. This representation provides an informative alternative to ranking long texts according to their overall similarity to a query. For example, a user can choose to view those documents that have an extended discussion of one set of terms and a brief but overlapping discussion of a second set of terms. This representation also allows for relevance feedback on patterns of term distribution. TileBars display documents only in terms of words supplied in the user query. For a given retrieved text, if the query words do not correspond to its main topics, the user cannot discern in what context the query terms were used. For example, a query on contaminants may retrieve documents whose main topics relate to nuclear power, food, or oil spills. To address this issue, I describe a graphical interface, called Cougar , that displays retrieved documents in terms of interactions among their automatically-assigned main topics, thus allowing users to familiarize themselves with the topics and terminology of a text collection.