Restoring warped document images through 3D shape modeling

Scanning a document page from a thick bound volume often results in two kinds of distortions in the scanned image, i.e., shade along the "spine" of the book and warping in the shade area. In this paper, we propose an efficient restoration method based on the discovery of the 3D shape of a book surface from the shading information in a scanned document image. From a technical point of view, this shape from shading (SFS) problem in real-world environments is characterized by 1) a proximal and moving light source, 2) Lambertian reflection, 3) nonuniform albedo distribution, and 4) document skew. Taking all these factors into account, we first build practical models (consisting of a 3D geometric model and a 3D optical model) for the practical scanning conditions to reconstruct the 3D shape of the book surface. We next restore the scanned document image using this shape based on deshading and dewarping models. Finally, we evaluate the restoration results by comparing our estimated surface shape with the real shape as well as the OCR performance on original and restored document images. The results show that the geometric and photometric distortions are mostly removed and the OCR results are improved markedly.     

Answering content and structure-based queries on XML documents using relevance propagation

As XML documents contain both content and structure information, taking advantage of the document structure in the retrieval process can lead to better identify relevant information units. In this paper, we describe an information retrieval (IR) approach dealing with queries composed of content and structure conditions. The XFIRM model we propose is designed to be as flexible as possible to process such queries. It is based on a complete query language, derived from XPath and on a relevance values propagation method. This paper aims at evaluating functions used in the propagation process, and particularly the use of distance between nodes as a parameter. The proposed method is evaluated, thanks to the INEX evaluation initiative. Results show a relative high precision of our proposal.     

A TOPSIS-based QoE model for adapted content selection of slide documents

Multimedia Tools and Applications - In certain platforms, such as Google Docs, documents are adapted for specific mobile device types, and the installation of their applications is required....     

Relevance feedback revisited: dealing with content and structure in XML documents

Relevance feedback (RF) is a technique that allows to enrich an initial query according to the user feedback. The goal is to express more precisely the user’s needs. Some open issues arise when considering semi-structured documents like XML documents. They are mainly related to the form of XML documents which mix content and structure information and to the new granularity of information. Indeed, the main objective of XML retrieval is to select relevant elements in XML documents instead of whole documents. Most of the RF approaches proposed in XML retrieval are simple adaptation of traditional RF to the new granularity of information. They usually enrich queries by adding terms extracted from relevant elements instead of terms extracted from whole documents. In this article, we describe a new approach of RF that takes advantage of two sources of evidence: the content and the structure. We propose to use the query term proximity to select terms to be added to the initial query and to use generic structures to express structural constraints. Both sources of evidence are used in different combined forms. Experiments were carried out within the INEX evaluation campaign and results show the effectiveness of our approaches.     

二维概念格图形的三维重构研究

介绍了二维概念格图形向三维空间转化和延伸的必要性和现状。通过对传统概念格图形分层定位布局方法的研究与分析,提出并实现了一种新的以具有大量的平行四边形和有向线段为基本特征的概念格在三维空间的自动布局算法,描述了一种基于该算法的二维概念格图形的三维重构机制,有效地解决了节点横向过度扩张的问题并减少了线段交叉,较好地实现了复杂概念格图形的三维可视化,为知识发现和知识处理提供了良好的基础。     

二维概念格图形的三维重构研究

介绍了二维概念格图形向三维空问转化和延伸的必要性和现状.通过对传统概念格图形分层定位布局方法的研究与分析,提出并实现了一种新的以具有大量的平行四边形和有向线段为基本特征的概念格在三维空间的自动布局算法,描述了一种基于该算法的二维概念格图形的三维重构机制,有效地解决了节点横向过度扩张的问题并减少了线段交叉,较好地实现了复杂概念格图形的三维可视化,为知识发现和知识处理提供了良好的基础.     

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.     

Learning from similarity and information extraction from structured documents

International Journal on Document Analysis and Recognition (IJDAR) - The automation of document processing has recently gained attention owing to its great potential to reduce manual work. Any...     

Automatic ontology-based knowledge extraction from Web documents

To bring the Semantic Web to life and provide advanced knowledge services, we need efficient ways to access and extract knowledge from Web documents. Although Web page annotations could facilitate such knowledge gathering, annotations are rare and will probably never be rich or detailed enough to cover all the knowledge these documents contain. Manual annotation is impractical and unscalable, and automatic annotation tools remain largely undeveloped. Specialized knowledge services therefore require tools that can search and extract specific knowledge directly from unstructured text on the Web, guided by an ontology that details what type of knowledge to harvest. An ontology uses concepts and relations to classify domain knowledge. Other researchers have used ontologies to support knowledge extraction, but few have explored their full potential in this domain. The paper considers the Artequakt project which links a knowledge extraction tool with an ontology to achieve continuous knowledge support and guide information extraction. The extraction tool searches online documents and extracts knowledge that matches the given classification structure. It provides this knowledge in a machine-readable format that will be automatically maintained in a knowledge base (KB). Knowledge extraction is further enhanced using a lexicon-based term expansion mechanism that provides extended ontology terminology.     

Transformations between Pictures from 2D to 3D

This paper describes programs for 3-dimensional engraving. The programs use raster or vector images to create a 3D model and, subsequently, convert this model into a sequence of control commands for 3D engraving machines. Three programs have been developed. A program for engraving general 3D surfaces from grey-scale images, a program for preparing these grey-scale images from patterns and vector images, and a program for fast 2D engraving. A simple and fast preparation of the 3D model, a user-friendly environment, and small hardware requirements were the principal goals.     

3D foot shape generation from 2D information

Two methods to generate an individual 3D foot shape from 2D information are proposed. A standard foot shape was first generated and then scaled based on known 2D information. In the first method, the foot outline and the foot height were used, and in the second, the foot outline and the foot profile were used. The models were developed using 40 participants and then validated using a different set of 40 participants. Results show that each individual foot shape can be predicted within a mean absolute error of 1.36 mm for the left foot and 1.37 mm for the right foot using the first method, and within a mean absolute error of 1.02 mm for the left foot and 1.02 mm for the right foot using the second method. The second method shows somewhat improved accuracy even though it requires two images. Both the methods are relatively cheaper than using a scanner to determine the 3D foot shape for custom footwear design.     

Extending distortion viewing from 2D to 3D

Addresses the visual exploration of 3D information layouts. Several visual exploration techniques have been proposed for 2D information layouts. Many of these try to take advantage of humans' natural visual pattern-recognition abilities to understand global relationships while simultaneously integrating this knowledge with local details. This desire for detail-in-context views (also called fisheye, multiscale and distortion views) has fueled considerable research in the development of distortion viewing tools. Generally, these tools provide space for magnification of local detail by compressing the rest of the image. In considering a possible detail-in-context view for 3D layouts, we first examine 2D distortion techniques, bearing in mind the particular 3D problem of occlusion. Comparing 2D and 3D information layout adjustment tools leads directly to a 3D visual access tool that clears a line of sight to any region of interest. While our technique can extend to any type of 3D information display, we focus on graphs     

True stereoscopic 3D cannot be simulated by shifting 2D content off the screen plane

Generating stereoscopic 3D (S3D) content is expensive, so industry producers sometimes attempt to save money by including brief sections of 2D content displayed with a uniform disparity, i.e. the 2D image is geometrically shifted behind the screen plane. This manipulation is believed to produce an illusion of depth which, while not as powerful as true S3D, is nevertheless more compelling than simple 2D. Our study examined whether this belief is correct. 30 s clips from a nature documentary were shown in the original S3D, in ordinary 2D and in shifted versions of S3D and 2D. Participants were asked to determine the impression of depth on a 7 point Likert scale. There was a clear and highly significant difference between the S3D depth perception (mean 6.03) and the shifted 2D depth perception (mean 4.13) (P = 0.002, ANOVA). There was no difference between ordinary 2D presented on the screen plane, and the shifted 2D. We conclude that the shifted 2D method not only fails to mimic the depth effect of true S3D, it in fact has no benefit over ordinary 2D in terms of the depth illusion created. This could impact viewing habits of people who notice the difference in depth quality.     

S2S: structural-to-syntactic matching similar documents

Management of large collection of replicated data in centralized or distributed environments is important for many systems that provide data mining, mirroring, storage, and content distribution. In its simplest form, the documents are generated, duplicated and updated by emails and web pages. Although redundancy may increase the reliability at a level, uncontrolled redundancy aggravates the retrieval performance and might be useless if the returned documents are obsolete. Document similarity matching algorithms do not provide the information on the differences of documents, and file synchronization algorithms are usually inefficient and ignore the structural and syntactic organization of documents. In this paper, we propose the S2S matching approach. The S2S matching is composed of structural and syntactic phases to compare documents. Firstly, in the structural phase, documents are decomposed into components by its syntax and compared at the coarse level. The structural mapping processes the decomposed documents based on its syntax without actually mapping at the word level. The structural mapping can be applied in a hierarchical way based on the structural organization of a document. Secondly, the syntactic matching algorithm uses a heuristic look-ahead algorithm for matching consecutive tokens with a verification patch. Our two-phase S2S matching approach provides faster results than currently available string matching algorithms.

Generating compact redundancy-free XML documents from conceptual-model hypergraphs

As XML data becomes more and more prevalent and as larger quantities of data find their way into XML documents, the need for quality XML data organization only increase. One standard way of structuring data well is to reduce and, if possible, eliminate redundancy, while at the same time making the storage structures as compact as possible. In this paper, we present a methodology to generate XML storage structures where conforming XML documents are redundancy-free, and for most practical cases, are also fully compact. Our methodology assumes the input is a conceptual-model hypergraph. For the special case that every edge in the hypergraph is binary, we present a simple algorithm, guaranteed to always generate redundancy-free storage structures. We show, however, that generating a minimum number of redundancy-free storage structures is NP-hard. We therefore provide heuristics to guide the process and observe that these heuristics result in satisfactory solutions, which are often optimal. We then present a general algorithm for n-ary edges and show that it generates redundancy-free storage structures. The general algorithm must overcome several problems that do not arise in the special case.