Historical Document Enhancement Using LUT Classification

The fast evolution of scanning and computing technologies in recent years has led to the creation of large collections of scanned historical documents. It is almost always the case that these scanned documents suffer from some form of degradation. Large degradations make documents hard to read and substantially deteriorate the performance of automated document processing systems. Enhancement of degraded document images is normally performed assuming global degradation models. When the degradation is large, global degradation models do not perform well. In contrast, we propose to learn local degradation models and use them in enhancing degraded document images. Using a semi-automated enhancement system, we have labeled a subset of the Frieder diaries collection (The diaries of Rabbi Dr. Avraham Abba Frieder. http://ir.iit.edu/collections/). This labeled subset was then used to train classifiers based on lookup tables in conjunction with the approximated nearest neighbor algorithm. The resulting algorithm is highly efficient and effective. Experimental evaluation results are provided using the Frieder diaries collection (The diaries of Rabbi Dr. Avraham Abba Frieder. http://ir.iit.edu/collections/). © Springer-Verlag 2009

Webpage design optimization using genetic algorithm driven CSS

In the rapid emergence of globalization, e-commerce, and internet accessibility in remote parts of the world, ongoing feedback and participation from site visitors are essential for attaining clear and effective communication on a web site. This thesis presents a computational experiment for optimizing design of a webpage in an evolutionary manner. Webpage personalization is viewed as a configuration problem whose goal is to determine the optimal presentation of a webpage while taking into account the preference of the web author (designer), layout constraints (web design/editing language: HTML, CSS), and viewer interaction with the browser. The study proposes use of genetic algorithm-driven Cascading Style Sheets (CSS) to assist the process of webpage design optimization. This method will engage visitors to remotely modify and enhance the style (type, layout and color) of web site to fit their aesthetic and functional representation of well-received design. The preference feedback from user will be stored in an application server for automated evolutionary selection process and reinitialized for the next generation of users. Through the experimentation of web prototype and user evaluation test, the implementation of this method is examined and the derived design solutions are analyzed based on web aesthetics, standards, and accessibility

Detecting Multilingual Lines of Text with Fusion Moves

This thesis proposes an optimization-based algorithm for detecting lines of text in images taken by hand-held cameras. The majority of existing methods for this problem assume alphabet-based texts (e.g. in Latin or Greek) and they use heuristics specific to such texts: proximity between letters within one line, larger distance between separate lines, etc. We are interested in a more challenging problem where images combine alphabet and logographic characters from multiple languages where typographic rules vary a lot (e.g. English, Korean, and Chinese). Significantly higher complexity of fitting multiple lines of text in different languages calls for an energy-based formulation combining a data fidelity term and a regularization prior. Our data cost combines geometric errors and likelihoods given by a classifier trained to low-level features in each language. Our regularization term encourages sparsity based on label costs. Our energy can be efficiently minimized by fusion moves. The algorithm was evaluated on a database of images from the subway of metropolitan area of Seoul and was proven to be robust

Character Recognition

Character recognition is one of the pattern recognition technologies that are most widely used in practical applications. This book presents recent advances that are relevant to character recognition, from technical topics such as image processing, feature extraction or classification, to new applications including human-computer interfaces. The goal of this book is to provide a reference source for academic research and for professionals working in the character recognition field