Discovering Legible And Readable Chinese Typefaces For Reading Digital Documents

In recent years, more and more fonts have been implemented in the digital publishing industry and in reading devices. In this thesis, we focus on the methods of evaluating digital Chinese fonts and their typeface characteristics. Our goal is to seek a good way to enhance the legibility and readability of Chinese characters displayed on digital devices such as cell phones, tablets and e-book devices. To accomplish this goal, we have combined methods in data mining, and pattern recognition with psychological and statistical analyses. Our research involved an extensive survey of the distinctive features of eighteen popular Chinese digital typefaces. Survey results were tabulated and analyzed statistically. Then, two objective experiments were conducted, using the best six fonts derived from the survey results. These experimental results have revealed an effective way of choosing legible and readable Chinese digital fonts that are most suitable for the comfortable reading of books, magazines, newspapers, and for the display of texts on cell-phones, e-books, and digital libraries. Results also helped us find out the features for improving character legibility and readability of different Chinese typefaces. The relationships among legibility, readability, eye-strain, and myopia, will be discussed. Moreover, digital market requirements and analyses will be provided

Reliable pattern recognition system with novel semi-supervised learning approach

Over the past decade, there has been considerable progress in the design of statistical machine learning strategies, including Semi-Supervised Learning (SSL) approaches. However, researchers still have difficulties in applying most of these learning strategies when two or more classes overlap, and/or when each class has a bimodal/multimodal distribution. In this thesis, an efficient, robust, and reliable recognition system with a novel SSL scheme has been developed to overcome overlapping problems between two classes and bimodal distribution within each class. This system was based on the nature of category learning and recognition to enhance the system's performance in relevant applications. In the training procedure, besides the supervised learning strategy, the unsupervised learning approach was applied to retrieve the "extra information" that could not be obtained from the images themselves. This approach was very helpful for the classification between two confusing classes. In this SSL scheme, both the training data and the test data were utilized in the final classification. In this thesis, the design of a promising supervised learning model with advanced state-of-the-art technologies is firstly presented, and a novel rejection measurement for verification of rejected samples, namely Linear Discriminant Analysis Measurement (LDAM), is defined. Experiments on CENPARMI's Hindu-Arabic Handwritten Numeral Database, CENPARMI's Numerals Database, and NIST's Numerals Database were conducted in order to evaluate the efficiency of LDAM. Moreover, multiple verification modules, including a Writing Style Verification (WSV) module, have been developed according to four newly defined error categories. The error categorization was based on the different costs of misclassification. The WSV module has been developed by the unsupervised learning approach to automatically retrieve the person's writing styles so that the rejected samples can be classified and verified accordingly. As a result, errors on CENPARMI's Hindu-Arabic Handwritten Numeral Database (24,784 training samples, 6,199 testing samples) were reduced drastically from 397 to 59, and the final recognition rate of this HAHNR reached 99.05%, a significantly higher rate compared to other experiments on the same database. When the rejection option was applied on this database, the recognition rate, error rate, and reliability were 97.89%, 0.63%, and 99.28%, respectivel

Fast Linear Discriminant Analysis Using Binary Bases

Linear Discriminant Analysis (LDA) is a widely used technique for pattern classification. It seeks the linear projection of the data to a low dimensional subspace where the data features can be modelled with maximal discriminative power. The main computation involved in LDA is the dot product between LDA base vector and the data which is costly element-wise floating point multiplications. In this paper, we present a fast linear discriminant analysis method called binary LDA, which possesses the desirable property that the subspace projection operation can be computed very efficiently. We investigate the LDA guided non-orthogonal binary subspace method to find the binary LDA bases, each of which is a linear combination of a small number of Haar-like box functions. The proposed approach is applied to face recognition. Experiments show that the discriminative power of binary LDA is preserved and the projection computation is significantly reduced. 1