18279251
Detection and analysis of table of contents based on content association
AbstractAs a special type of table understanding, the detection and analysis of tables of contents (TOCs) play an important role in the digitization of multi-page documents. Most previous TOC analysis methods only concentrate on the TOC itself without taking into account the other pages in the same document. Besides, they often require manual coding or at least machine learning of document-specific models. This paper introduces a new method to detect and analyze TOCs based on content association. It fully leverages the text information throughout the whole multi-page document and can be directly applied to a wide range of documents without the need to build or learn the models for individual documents. In addition, the associations of general text and page numbers are combined to make the TOC analysis more accurate. Natural language processing and layout analysis are integrated to improve the TOC functional tagging. The applications of the proposed method in a large-scale digital library project are also 
discussed.
26474180
Adaptive confidence transform based classifier combination for Chinese character recognition
Abstract Classifier combination is an effective way to improve recognition performance. However, in Chinese character recognition the extremely large number of categories results in several difficulties for the combination. In order to overcome these difficulties a novel combination method is presented in this paper. It consists of three main components: adaptive confidence transform (ACT), consensus theoretic combination and reliability based speedup scheme. ACT, which can estimate a posteriori probabilities from raw measurement values, is the focus of this paper. Experimental results show a significant reduction of error rates in both printed (PCCR) and handwritten Chinese character recognition (HCCR).
8341476
Active document layout synthesis
Document layout analysis has been researched for many years. However, there is little work on the reverse of document layout analysis: document layout synthesis, whose goal is to generate logically correct and aesthetically appealing layout given the text/image contents and the flexible layout template. This paper introduces a new automatic document layout synthesis method, which can actively pursue the optimal text block height-width tradeoff simultaneously with the block position adjustment. The key idea is to use a cluster of linear functions to approximate the nonlinear height-width curve. Then two-pass simplex algorithm is used to solve the layout synthesis problem. This method has a wide range of applications, such as variable data printing (VDP), automatic table formatting, and automating document layout ground truth generation.
8724659
Estimating device availability in pervasive peer-to-peer environment
In pervasive computing environments, devices often communicate in a peer-to-peer manner, either for passing messages or collaboratively running applications. For any particular device, information about the availability of other devices can lead to more efficient communication and better execution of distributed tasks. For example, a device can distribute computation tasks to reliable devices and temporarily avoid unreliable ones. Since there usually is not a central server to coordinate or monitor the communication in such environment, a key technical challenge is for each individual device to predict the availability of other devices. In this paper, we describe some methods for predicting the availability of other devices using historic availability data of those devices obtained in routine usage. In this scheme, each device separately maintains data about the past communication with the other devices, and predicts current and future availability using the statistics of those data. These methods do not require any centralized monitoring or extra probing, and thus have very low computation cost. These characteristics make them suitable for small devices in a peer-to-peer environment.
2250003
ICDAR 2003 robust reading competitions: entries, results, and future directions
Abstract.This paper describes the robust reading competitions for ICDAR 2003. With the rapid growth in research over the last few years on recognizing text in natural scenes, there is an urgent need to establish some common benchmark datasets and gain a clear understanding of the current state of the art. We use the term ‘robust reading’ to refer to text images that are beyond the capabilities of current commercial OCR packages. We chose to break down the robust reading problem into three subproblems and run competitions for each stage, and also a competition for the best overall system. The subproblems we chose were text locating, character recognition and word recognition. By breaking down the problem in this way, we hoped to gain a better understanding of the state of the art in each of the subproblems. Furthermore, our methodology involved storing detailed results of applying each algorithm to each image in the datasets, allowing researchers to study in depth the strengths and weaknesses of each algorithm. The text-locating contest was the only one to have any entries. We give a brief description of each entry and present the results of this contest, showing cases where the leading entries succeed and fail. We also describe an algorithm for combining the outputs of the individual text locators and show how the combination scheme improves on any of the individual systems.
13382977
Quality assurance in high volume document digitization: a survey
Quality assurance (QA) plays a critical role in high volume document digitization projects by making sure that the specified quality standard is reached under cost and time constraints. This paper takes a systematic view on this issue by summarizing and abstracting related existing work: quality bottlenecks and technical solutions throughout the whole processing pipeline, including cataloging, capture, image analysis and recognition, and error cascading; various strategies to conduct cost-effective QA, such as combination of auto-QA and manual QA, batch QA, special QA user interface, and open source QA
17501980
Term Graph Model for Text Classification
Most existing text classification methods (and text mining methods at large) are based on representing the documents using the traditional vector space model. We argue that important information, such as the relationship among words, is lost. We propose a term graph model to represent not only the content of a document but also the relationship among the keywords. We demonstrate that the new model enables us to define new similarity functions, such as considering rank correlation based on PageRank-style algorithms, for the classification purpose. Our preliminary results show promising results of our new model.
18690541
Real-time author co-citation mapping for online searching
Author searching is traditionally based on the matching of name strings. Special characteristics of authors as personal names and subject indicators are not considered. This makes it difficult to identify a set of related authors or to group authors by subjects in retrieval systems. In this paper, we describe the design and implementation of a prototype visualization system to enhance author searching. The system, called AuthorLink, is based on author co-citation analysis and visualization mapping algorithms such as Kohonen's feature maps and Pathfinder networks. AuthorLink produces interactive author maps in real time from a database of 1.26 million records supplied by the Institute for Scientific Information. The maps show subject groupings and more fine-grained intellectual connections among authors. Through the interactive interface the user can take advantage of such information to refine queries and retrieve documents through point-and-click manipulation of the authors' names.
207195547
Graphical table of contents
This paper proposes a graphical table of contents (GTOC) that is functionally analogous to the table of contents. The proposed GTOC can be generated automatically from the text of documents. It visualizes document contents and relationships to allow easy access of underlying documents. It also provides various interactive tools to let the user explore the documents. Issues of how to generate such GTOC include how documents are indexed and organized, how the organized documents are visualized, and what interactive means are needed to provide necessary functionality of GTOC. These issues are discussed in this paper with a GTOC prototype based on Kohonen’s selforganizing featore map algorithm.
306293
A self-organizing semantic map for information retrieval
Self-organizing Semantic Map for Information Retrieval Xia Lin Dagobert Soergel Gary Marchionini College of Library and Information Services University of Maryland College Park, A neural network’s unsupervised learning algorithm, Kohonen’s feature map, is applied to constructing a selforganizing semantic map for information retrieval. The semantic map visualizes semantic relationships between input documents, and has properties of economic representation of data with their interrelationships. The potentials of the semantic map include using the map as a retrieval interface for an online bibliographic system. A prototype system that demonstrates this potential is described.
1863895
Low-Complexity and Distributed Energy Minimization in Multihop Wireless Networks
In this work, we study the problem of minimizing the total power consumption in a multihop wireless network subject to a given offered load. It is well-known that the total power consumption of multihop wireless networks can be substantially reduced by jointly optimizing power control, link scheduling, and routing. However, the known optimal cross-layer solution to this problem is centralized and with high computational complexity. In this paper, we develop a low-complexity and distributed algorithm that is provably power-efficient. In particular, under the node-exclusive interference model and with suitable assumptions on the power-rate function, we can show that the total power consumption of our algorithm is at most (2+¿) times as large as the power consumption of the optimal (but centralized and complex) algorithm, where ¿ is an arbitrarily small positive constant. Our algorithm is not only the first such distributed solution with provable performance bound, but its power-efficiency ratio is also tighter than that of another suboptimal centralized algorithm in the literature.