This paper presents an overview of feature extraction methods for off-line recognition of segmented (isolated) characters. Selection of a feature extraction method is probably the single most important factor in achieving high recognition performance in character recognition systems. Different feature extraction methods are designed for different representations of the characters, such as solid binary characters, character contours, skeletons (thinned characters), or gray level subimages of each individual character. The feature extraction methods are discussed in terms of invariance properties, reconstructability, and expected distortions and variability of the characters. The problem of choosing the appropriate feature extraction method for a given application is also discussed. When a few promising feature extraction methods have been identified, they need to be evaluated experimentally to find the best method for the given application. Feature extraction Optical character recogniti...

We explore a new approach to shape recognition based on a virtually in nite family of binary features (\queries") of the image data, designed to accommodate prior in-formation about shape invariance and regularity. Each query corresponds to a spatial arrangement ofseveral local topographic codes (\tags") which are in themselves too primitive and common to be informative about shape. All the discriminating power derives from relative angles and distances among the tags. The important attributes of the queries are (i) a natural partial ordering corresponding to increasing structure and complexity � (ii) semi-invariance, meaning that most shapes of a given class will answer the same way totwo queries which are successive in the ordering � and (iii) stability, since the queries are not based on distinguished points and substructures. No classi er based on the full feature set can be evaluated and it is impossible to determine a priori which arrangements are informative. Our approach istoselect informative features and build tree classi ers at the same time by inductive learning. In e ect, each tree provides an approximation to the full posterior where the features

. The satisfiability (SAT) problem is a core problem in mathematical logic and computing theory. In practice, SAT is fundamental in solving many problems in automated reasoning, computer-aided design, computeraided manufacturing, machine vision, database, robotics, integrated circuit design, computer architecture design, and computer network design. Traditional methods treat SAT as a discrete, constrained decision problem. In recent years, many optimization methods, parallel algorithms, and practical techniques have been developed for solving SAT. In this survey, we present a general framework (an algorithm space) that integrates existing SAT algorithms into a unified perspective. We describe sequential and parallel SAT algorithms including variable splitting, resolution, local search, global optimization, mathematical programming, and practical SAT algorithms. We give performance evaluation of some existing SAT algorithms. Finally, we provide a set of practical applications of the sat...

Many images---especially those used for page design on web pages---as well as videos contain visible text. If these text occurrences could be detected, segmented, and recognized automatically, they would be a valuable source of high-level semantics for indexing and retrieval. In this paper, we propose a novel method for localizing and segmenting text in complex images and videos. Text lines are identified by using a complex-valued multilayer feed-forward network trained to detect text at a fixed scale and position. The network's output at all scales and positions is integrated into a single text-saliency map, serving as a starting point for candidate text lines. In the case of video, these candidate text lines are refined by exploiting the temporal redundancy of text in video. Localized text lines are then scaled to a fixed height of 100 pixels and segmented into a binary image with black characters on white background. For videos, temporal redundancy is exploited to improve segmentation performance. Input images and videos can be of any size due to a true multiresolution approach. Moreover, the system is not only able to locate and segment text occurrences into large binary images, but is also able to track each text line with sub-pixel accuracy over the entire occurrence in a video, so that one text bitmap is created for all instances of that text line. Therefore, our text segmentation results can also be used for object -based video encoding such as that enabled by MPEG-4.

. Automatic recognition of mathematical expressions is one of the key vehicles in the drive towards transcribing documents in scientific and engineering disciplines into electronic form. This problem typically consists of two major stages, namely, symbol recognition and structural analysis. In this survey paper, we will review most of the existing work with respect to each of the two major stages of the recognition process. In particular, we try to put emphasis on the similarities and differences between systems. Moreover, some important issues in mathematical expression recognition will be addressed in depth. All these together serve to provide a clear overall picture of how this research area has been developed to date. Key words: error detection and correction -- mathematical expression recognition -- performance evaluation -- structural analysis -- symbol recognition 1

A~s~Qc~-A method for the recognition of multifont printed characters is proposed, giving emphasis to the identification of structural descriptions of character shapes using prototypes. Noise and shape variations are modeled as series of transformations from groups of features in the data to features in each prototype. Thus, the method manages systematically the relative distortion between a candidate shape and its prototype, accomplishing robustness to noise with less than two prototypes per class, on average. Our method uses a flexible matching between components and a flexible grouping of the individual components to be matched. A number of shape transformations are defined, including filling of gaps, so that the method handles broken characters. Also, a measure of the amount of distortion that these transformations cause is given. Classification of character shapes is defined as a minimization problem among the possible transformations that map an input shape into prototypical shapes. Some tests with hand-printed numerals confirmed the method’s high robustness level. Zndex Terms-Shape distance, graph matching, relative neighborhood graph, broken character recognition, subgraph homeomorphism. I.

Social networking sites have been increasingly gaining popularity. Well-known sites such as Facebook have been reporting growth rates as high as 3 % per week [5]. Many social networking sites have millions of registered users who use these sites to share photographs, contact long-lost friends, establish new business contacts and to keep in touch. In this paper, we investigate how easy it would be for a potential attacker to launch automated crawling and identity theft attacks against a number of popular social networking sites in order to gain access to a large volume of personal user information. The first attack we present is the automated identity theft of existing user profiles and sending of friend requests to the contacts of the cloned victim. The hope, from the attacker’s point of view, is that the contacted users simply trust and accept the friend request. By establishing a friendship relationship with the contacts of a victim, the attacker is able to access the sensitive personal information provided by them. In the second, more advanced attack we present, we show that it is effective and feasible to launch an automated, cross-site profile cloning attack. In this attack, we are able to automatically create a forged profile in a network where the victim is not registered yet and contact the victim’s friends who are registered on both networks. Our experimental results with real users show that the automated attacks we present are effective and feasible in practice. Categories andSubject Descriptors

There are many historical manuscripts written in a single hand which it would be useful to index. Examples include the early Presidential papers at the Library of Congress and the collected works of W. B. DuBois at the library of the University of Massachusetts. The standard technique for indexing documents is to scan them in, convert them to machine readable form (ASCII) using Optical Character Recognition (OCR) and then index them using a text retrieval engine. However, OCR does not work well on handwriting. Here an alternative scheme is proposed for indexing such texts. Each page of the document is segmented into words. The images of the words are then matched against each other to create equivalence classes (each equivalence classes contains multiple instances of the same word). The user then provides ASCII equivalents for say the top 2000 equivalence classes. The current paper deals with the matching aspects of this process. Due to variations in even a single person's handwriting,...

Recognition of degraded text is a challenging problem. To improve the performance of an OCR system on degraded images of text, postprocessing techniques are critical. The objective of postprocessing is to correct errors or to resolve ambiguities in OCR results by using contextual information. Depending on the extent of context used, there are different levels of postprocessing. In current commercial OCR systems, word-level postprocessing methods, such as dictionary-lookup, have been applied successfully. However, many OCR errors cannot be corrected by word-level postprocessing. To overcome this limitation, passage-level postprocessing, in which global contextual information is utilized, is necessary. In most current studies on passage-level postprocessing, linguistic context is the major resource to be exploited. This thesis addresses problems in degraded text recognition and discusses potential solutions through passage-level postprocessing. The objective is to develop a postprocessin...

There is extensive interest in automating the collection, organization and summarization of biological data. Data in the form of figures and accompanying captions in literature present special challenges for such efforts. Based on our previously developed search engines to find fluorescence microscope images depicting protein subcellular patterns, we introduced text mining and Optical Character Recognition (OCR) techniques for caption understanding and figure-text matching, so as to build a robust, comprehensive toolset for extracting information about protein subcellular localization from the text and images found in online journals. Our current system can generate assertions such as "Figure N depicts a localization of type L for protein P in cell type C".