Correcting document image warping based on regression of curved text lines
Image warping is a common problem when one scans or photocopies a document page from a thick bound volume, resulting in shading and curved text lines in the spine area of the bound volume. This will not only impair readability, but will also reduce the OCR accuracy. Further to our earlier attempt to correct such images, this paper proposes a simpler connected component analysis and regression technique. Compared to our earlier method, the present system is computationally less expensive and is resolution independent too. The implementation of the new system and improvement of OCR accuracy are presented in this paper.

title: Improving Pronoun Resolution Using Statistics-Based Semantic Compatibility Information
abstract: In this paper we focus on how to improve pronoun resolution using the statistics-based semantic compatibility information. We investigate two unexplored issues that influence the effectiveness of such information: statistics source and learning framework. Specifically, we for the first time propose to utilize the web and the twin-candidate model, in addition to the previous combination of the corpus and the single-candidate model, to compute and apply the semantic information. Our study shows that the semantic compatibility obtained from the web can be effectively incorporated in the twin-candidate learning model and significantly improve the resolution of neutral pronouns.

title: Information Retrieval in Document Image Databases
abstract: With the rising popularity and importance of document images as an information source, information retrieval in document image databases has become a growing and challenging problem. In this paper, we propose an approach with the capability of matching partial word images to address two issues in document image retrieval: word spotting and similarity measurement between documents. First, each word image is represented by a primitive string. Then, an inexact string matching technique is utilized to measure the similarity between the two primitive strings generated from two word images. Based on the similarity, we can estimate how a word image is relevant to the other and, thereby, decide whether one is a portion of the other. To deal with various character fonts, we use a primitive string which is tolerant to serif and font differences to represent a word image. Using this technique of inexact string matching, our method is able to successfully handle the problem of heavily touching characters. Experimental results on a variety of document image databases confirm the feasibility, validity, and efficiency of our proposed approach in document image retrieval.

title: A Comparative Study on Chinese Text Categorization Methods
abstract: This paper reports our comparative evaluation of three machine learning methods on Chinese text categorization. Whereas a wide range of methods have been applied to English text categorization, relatively few studies have been done on Chinese text categorization. Based on a People's Daily news corpus, a series of controlled experiments evaluate three machine learning methods, namely k Nearest Neighbor (kNN) algorithm, Support Vector Machines (SVM), and Adaptive Resonance Associative Map (ARAM), in terms of their capabilities in mining categorization knowledge from high dimensional, sparse, and relatively noisy document feature vectors. Experiments reveal that all three methods produce satisfactory performance on the test corpus while ARAM exhibits a marginally better generalization capability, especially from relatively small and noisy training sets.

title: Adaptive image-smoothing using a coplanar matrix and its application to document image binarization
abstract: For document images corrupted by various kinds of noise, direct binarization images may be severely blurred and degraded. A common treatment for this problem is to pre-smooth input images using noise-suppressing filters. This article proposes an image-smoothing method used for prefiltering the document image binarization. Conceptually, we propose that the influence range of each pixel affecting its neighbors should depend on local image statistics. Technically, we suggest using coplanar matrices to capture the structural and textural distribution of similar pixels at each site. This property adapts the smoothing process to the contrast, orientation, and spatial size of local image structures. Experimental results demonstrate the effectiveness of the proposed method, which compares favorably with existing methods in reducing noise and preserving image features. In addition, due to the adaptive nature of the similar pixel definition, the proposed filter output is more robust regarding different noise levels than existing methods.

title: Elliptic arc vectorization for 3D pie chart recognition
abstract: In this paper, we present a novel approach to vectorize elliptic arcs for 3D pie chart recognition and data extraction. As a preprocessing step, a set of straight line segments are obtained first. By estimating the parameters based on the line segments, we are able to generate elliptic arc candidates. Curve fitting is then performed to reject false positives and further refine the parameters of each elliptic arc. The vectorized elliptic arcs are the key components in the 3D pie charts that allow us to determine the chart type and to extract data values embedded. Experiments using a group of testing images show that the proposed method works well and is computationally efficient.

title: Low-power group key agreement for heterogeneous wireless networks
abstract: Heterogeneous wireless networks are gaining popularity as users can be connected to these networks without any cables and even when they are mobile. The high power nodes in heterogeneous wireless networks do not have energy constraints but the user nodes, which can be large in numbers, are usually low power energy constrained devices. Therefore, the GKA protocol used to secure group communications in these networks has to take into consideration both the low power nature of the user nodes and the network size. In this paper, we present an energy efficient and scalable GKA protocol, which uses our proposed Contributory Ring-Centralized (ContRi-Central) group model. Besides providing complexity analysis, we also show the computational and communication energy consumption costs analysis of all nodes running our proposed scheme and four other efficient GKA protocols. Both the complexity analysis and energy consumption costs analysis indicate that our proposed scheme is more efficient and suitable for heterogeneous wireless networks.

title: Energy-efficient ID-based group key agreement protocols for wireless networks
abstract: One useful application of wireless networks is for secure group communication, which can be achieved by running a group key agreement (GKA) protocol. One well-known method of providing authentication in GKA protocols is through the use of digital signatures. Traditional certificate-based signature schemes require users to receive and verify digital certificates before verifying the signatures but this process is not required in ID-based signature schemes. In this paper, we present an energy-efficient ID-based authenticated GKA protocol and four energy-efficient ID-based authenticated dynamic protocols, namely join, leave, merge and partition protocol, to handle dynamic group membership events, which are frequent in wireless networks. We provide complexity and energy cost analysis of our protocols and show that our protocols are more energy-efficient and suitable for wireless networks.

title: Signature Schemes Based on 3rd Order Shift Registers
abstract: In this paper, we propose two digital signature schemes based on a third order linear feedback shift register. One of them is a normal signature scheme for signing document and the other is with encryption for intended reciever. These two signature schemes are different from most of the signature schemes which are based on discrete logarithm problem, elliptic curves discrete logarithm problem, RSA or quadratic residues. The efficient computational algorithm for computing kth term of a sequence is also presented. The advantage of these two schemes is that the computation is carried out in the ground field and not in an extension field. We also show that the security of these two signature schemes is equivalent to that of Schnorr signature scheme and Signed-ElGamal encryption scheme respectively.

title: Key Substitution Attacks on Some Provably Secure Signature Schemes

title: E-governance: towards a strategic convergence of stakeholder interests
abstract: E-governments have become an integral part of our lives and the study of this phenomenon has revealed valuable insights from a governance perspective. Based on an in-depth case study, this paper addresses the strategic management of stakeholders within the e-governmental environment. In particular, the research looks at how advances in information technology have altered the power structures of governance systems and reshaped its fundamental principles .The study concludes that the strategic conversion of interests between the organizations and its stakeholders is an important determining factor for the success of any e-initiative. This requires both a rethinking of fundamental functions and a transformation of organizational business mentalities in order to be flexible and adaptable to ever-changing expectations of a dynamic system.