1977537
Active layout engine: Algorithms and applications in variable data printing
Variable Data Printing (VDP) refers to the process of generating and printing dynamic or personalized contents. A core technology required by highly customized VDP applications is the automatic document layout design engine, whose task is to adjust the original design or generate a new layout to present variable contents. This paper presents a novel document layout design engine, called Active Layout Engine (ALE). 'Active' reflects several unique features of the engine: first, through linear text block modeling and two-pass constraint solving algorithm, it supports a rich set of layout operations, such as simultaneous optimization of text block width and height, integrated image cropping, and non-rectangular text wrapping. Second, it does not rely on a particular layout description language and thus can actively pursue emerging formats and standards. This paper describes the various technical aspects of ALE: linearization of the text block modeling, two-pass constraint solving algorithm, format-neutral Active Layout Template (ALT), system optimization, and typical VDP applications around the core engine.
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.
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.
15560852
Performance analysis of pattern classifier combination by plurality voting
Plurality voting is widely used in pattern recognition. However, there is little theoretical analysis of plurality voting. In this paper, we attempt to explore the rationale behind plurality voting. The recognition/error/rejection rates of plurality voting are compared with those of majority voting under different conditions. It is demonstrated that plurality voting is more efficient in achieving the tradeoff between rejection rate and error rate. We also discuss some practical problems when applying plurality voting to real-world applications.
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.
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).
4018890
Clustering Moving Objects for Spatio-temporal Selectivity Estimation
Many spatio-temporal applications involve managing and querying moving objects. In such an environment, predictive spatio-temporal queries become an important query class to be processed to capture the nature of moving objects. In this paper, we investigated the problem of selectivity estimation for predictive spatio-temporal queries. We propose a novel histogram technique based on a clustering paradigm. To avoid expensive computation costs, we developed linear time heuristics to construct such a histogram. Our performance study indicated that the new techniques improve the accuracy of the existing techniques by one order of magnitude.
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.
8754175
Summarizing level-two topological relations in large spatial datasets
Summarizing topological relations is fundamental to many spatial applications including spatial query optimization. In this article, we present several novel techniques to effectively construct cell density based spatial histograms for range (window) summarizations restricted to the four most important level-two topological relations: contains, contained, overlap, and disjoint. We first present a novel framework to construct a multiscale Euler histogram in 2D space with the guarantee of the exact summarization results for aligned windows in constant time. To minimize the storage space in such a multiscale Euler histogram, an approximate algorithm with the approximate ratio 19/12 is presented, while the problem is shown NP-hard generally. To conform to a limited storage space where a multiscale histogram may be allowed to have only k Euler histograms, an effective algorithm is presented to construct multiscale histograms to achieve high accuracy in approximately summarizing aligned windows. Then, we present a new approximate algorithm to query an Euler histogram that cannot guarantee the exact answers; it runs in constant time. We also investigate the problem of nonaligned windows and the problem of effectively partitioning the data space to support nonaligned window queries. Finally, we extend our techniques to 3D space. Our extensive experiments against both synthetic and real world datasets demonstrate that the approximate multiscale histogram techniques may improve the accuracy of the existing techniques by several orders of magnitude while retaining the cost efficiency, and the exact multiscale histogram technique requires only a storage space linearly proportional to the number of cells for many popular real datasets.
2266606
Space-efficient Relative Error Order Sketch over Data Streams
We consider the problem of continuously maintaining order sketches over data streams with a relative rank error guarantee ∊. Novel space-efficient and one-scan randomised techniques are developed. Our first randomised algorithm can guarantee such a relative error precision ∊ with confidence 1 - \delta using O( 1\_ \in \frac{1} {2}2 log 1d log ∊^2N) space, where N is the number of data elements seen so far in a data stream. Then, a new one-scan space compression technique is developed. Combined with the first randomised algorithm, the one-scan space compression technique yields another one-scan randomised algorithm that guarantees the space requirement is O( 1\frac{1} { \in } log(1\frac{1}{ \in } log 1\begin{gathered} \frac{1}{\delta } \hfill \\ \hfill \\ \end{gathered} )\frac{{\log ^{2 + \alpha } \in N}} {{1 - 1/2^\alpha }} (for\alpha \gt 0) on average while the worst case space remains O( \frac{1}{{ \in ^2 }}\log \frac{1} {\delta }\log \in ^2 N). These results are immediately applicable to approximately computing quantiles over data streams with a relative error guarantee \in and significantly improve the previous best space bound O( \frac{1} {{ \in ^3 }}\log \frac{1}{\delta }\log N). Our extensive experiment results demonstrate that both techniques can support an on-line computation against high speed data streams.
14038708
Efficiently computing frequent tree-like topology patterns in a Web environment
Data mining has recently emerged in a number of real life applications, such as banking, supermarketing, and interneting. The authors investigate a novel data mining problem derived from the applications of WWW. They present an efficient algorithm for solving the problem. The initial implementation suggests that the algorithm is very efficient and scalable in practice.