Automatic Indexing of Newspaper Microfilm Images
This paper describes a proposed document analysis system that aims at automatic indexing of digitized images of old newspaper microfilms. This is done by extracting news headlines from microfilm images. The headlines are then converted to machine readable text by OCR to serve as indices to the respective news articles. A major challenge to us is the poor image quality of the microfilm as most images are usually inadequately illuminated and considerably dirty. To overcome the problem we propose a new effective method for separating characters from noisy background since conventional threshold selection techniques are inadequate to deal with these kinds of images. A Run Length Smearing Algorithm (RLSA) is then applied to the headline extraction. Experimental results confirm the validity of the approach.

title: Correcting document image warping based on regression of curved text lines
abstract: 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: Chart analysis and recognition in document images
abstract: Hidden Markov models are a probabilistic modeling tool for time series data. It has been successfully applied to many areas, such as speech recognition, hand-written character recognition, etc. In this paper, we present a novel statistical approach using ergodic hidden Markov models to recognize scientific charts. We also present a newly developed feature extraction method for chart images. Unlike traditional primitive-based diagram recognition method, our approach need not recognize the graphic primitives in charts thus bypassing the recognition error problem caused by the inaccurate primitive extraction that is also a major obstacle to the construction of a general chart recognition system.

title: Document Flattening through Grid Modeling and Regularization
abstract: For document images captured by a digital camera, perspective and geometric distortions make it hard to recognize the document content properly. In this paper, we propose an integrated document restoration technique, which is capable of removing perspective and geometric distortions, and producing a flattened and fronto-parallel text image that is friendly to the generic OCR systems. The proposed document restoration is accomplished through grid modeling, which divides camera images into multiple quadrilateral grids using vertical text directions and the x lines and base lines. The global distortions are then removed through grid regularization that transforms the quadrilateral grids together with the pixel contents to the regular square grids. Experimental results show the proposed method is fast and easy for implementation

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: Motion deblurring for optical character recognition
abstract: This paper investigates the problem of blurring caused by motion during image capture of text documents. Motion blurring prevents proper optical character recognition of the document text contents. One area of such applications is to deblur name card images obtained from handheld cameras. In this paper, a complete motion deblurring procedure for document images has been proposed. The method handles both uniform linear motion blur and uniform acceleration motion blur. Experiments on synthetic and real-life blurred images prove the feasibility and reliability of this algorithm provided that the motion is not too irregular. The restoration procedure consumes only small amount of computation time.

title: The use of bigrams to enhance text categorization
abstract: In this paper, we present an efficient text categorization algorithm that generates bigrams selectively by looking for ones that have an especially good chance of being useful. The algorithm uses the information gain metric, combined with various frequency thresholds. The bigrams, along with unigrams, are then given as features to two different classifiers: Naive Bayes and maximum entropy. The experimental results suggest that the bigrams can substantially raise the quality of feature sets, showing increases in the break-even points and F1 measures. The McNemar test shows that in most categories the increases are very significant. Upon close examination of the algorithm, we concluded that the algorithm is most successful in correctly classifying more positive documents, but may cause more negative documents to be classified incorrectly.

title: Chosen Ciphertext Security from Identity-Based Encryption Without Strong Condition
abstract: Recently, Canetti et al [11] gave a generic construction (called CHK construction) of public key encryption (PKE) from a selective identity-based encryption scheme combined with a strong one-time signature scheme. Later, few schemes were proposed to improve the efficiency of CHK construction [11], for example, Boneh-Katz scheme  replaced a strong one-time signature with a message authentication code and Boyen-Mei-Waters scheme  was constructed directly from Waters' IBE scheme. But, both constructions have either trade-off the publicly verifiable property or security against adaptive chosen-ciphertext attack. We ask a question whether it is possible to construct an efficient and publicly verifiable PKE scheme from a selective IBE scheme with a weak one-time signature scheme. In this paper, we provide an affirmative answer and construct a public key encryption scheme which preserves the publicly verifiable property and is secure against adaptive chosen-ciphertext attack. The construction of the proposed scheme is based on Boneh-Boyen identity-based encryption (IBE) scheme  and a weak one-time signature scheme (using Waters' signature scheme ) built within Boneh-Boyen IBE scheme. In this construction, one-time signature scheme is not required to be strongly existential unforgeable as Waters' signature scheme is not a strongly existential unforgeability. We also show that the proposed scheme is ”almost” as efficient as the original Boneh-Boyen IBE scheme.

title: Public-Key Encryption from ID-Based Encryption Without One-Time Signature
abstract: Design a secure public key encryption scheme and its security proof are one of the main interests in cryptography In 2004, Canetti, Halevi and Katz  constructed a public key encryption (PKE) from a selective identity-based encryption scheme with a strong one-time signature scheme In 2005, Boneh and Katz  improved Canetti-Halevi-Katz construction by replacing a strong one-time signature with a message authentication code, but it is not publicly verifiable Later, Boyen, Mei and Waters  constructed PKE scheme directly from Waters' IBE scheme  , which is only secure against direct chosen-ciphertext attack and is not secure against adaptive chosen-ciphertext attack In 2006,  further improved the efficiency of Canetti-Halevi-Katz (CHK) construction by directly from Boneh-Boyen identity based encryption (IBE) scheme  with a weak one-time signature In this paper, we construct an efficient public key encryption scheme without one-time signature, which preserves a publicly verifiable property and secure against adaptive chosen-ciphertext attack The construction of the proposed scheme is based on Boneh-Boyen identity-based encryption (IBE) scheme  and a trapdoor function We also show that the proposed scheme is more efficient than CHK construction.

title: User Response to Free Trial Restrictions: A Coping Perspective
abstract: Software vendors often provide software for free download but with restrictions (e.g., time and/or functionality restrictions). The question that arises is to what extent the restrictions should be set to induce users to procure the full version. This study seeks to answer this question by looking from two perspectives: expectation-disconfirmation and coping behavior. Building on these perspectives, we present a research model of user's coping reactions toward software restrictions. We seek to understand user reactions (i.e., derivation of coping strategy) when their expectation toward trial restrictions is negatively disconfirmed. It is further posited that situational control could moderate the relationship between expectation disconfirmation and coping responses. We believe this research will contribute to enrich the current IS field and benefit market practitioners.

title: A Distributed Throttling Approach for Handling High Bandwidth Aggregates
abstract: Public-access networks need to handle persistent congestion and overload caused by high bandwidth aggregates that may occur during times of flooding-based DDoS attacks or flash crowds. The often unpredictable nature of these two activities can severely degrade server performance. Legitimate user requests also suffer considerably when traffic from many different sources aggregates inside the network and causes congestion. This paper studies a family of algorithms that "proactively" protect a server from overload by installing rate throttles in a set of upstream routers. Based on an optimal control setting, we propose algorithms that achieve throttling in a distributed and fair manner by taking important performance metrics into consideration, such as minimizing overall load variations. Using ns-2 simulations, we show that our proposed algorithms 1) are highly adaptive by avoiding unnecessary parameter configuration, 2) provide max-min fairness for any number of throttling routers, 3) respond very quickly to network changes, 4) are extremely robust against extrinsic factors beyond the system control, and 5) are stable under given delay bounds.