Robust virtual unrolling of historical parchment XMT images

We develop a framework to virtually unroll fragile historical parchment scrolls, which cannot be physically unfolded via a sequence of X-ray tomographic slices, thus providing easy access to those parchments whose contents have remained hidden for centuries. The first step is to produce a topologically correct segmentation, which is challenging as the parchment layers vary significantly in thickness, contain substantial interior textures and can often stick together in places. For this purpose, our method starts with linking the broken layers in a slice using the topological structure propagated from its previous processed slice. To ensure topological correctness, we identify fused regions by detecting junction sections, and then match them using global optimization efficiently solved by the blossom algorithm, taking into account the shape energy of curves separating fused layers. The fused layers are then separated using as-parallel-as-possible curves connecting junction section pairs. To flatten the segmented parchment, pixels in different frames need to be put into alignment. This is achieved via a dynamic programming-based global optimization, which minimizes the total matching distances and penalizes stretches. Eventually, the text of the parchment is revealed by ink projection. We demonstrate the effectiveness of our approach using challenging real-world data sets, including the water damaged fifteenth century Bressingham scroll

Recognition of off-line handwritten cursive text

The author presents novel algorithms to design unconstrained handwriting recognition systems organized in three parts: In Part One, novel algorithms are presented for processing of Arabic text prior to recognition. Algorithms are described to convert a thinned image of a stroke to a straight line approximation. Novel heuristic algorithms and novel theorems are presented to determine start and end vertices of an off-line image of a stroke. A straight line approximation of an off-line stroke is converted to a one-dimensional representation by a novel algorithm which aims to recover the original sequence of writing. The resulting ordering of the stroke segments is a suitable preprocessed representation for subsequent handwriting recognition algorithms as it helps to segment the stroke. The algorithm was tested against one data set of isolated handwritten characters and another data set of cursive handwriting, each provided by 20 subjects, and has been 91.9% and 91.8% successful for these two data sets, respectively. In Part Two, an entirely novel fuzzy set-sequential machine character recognition system is presented. Fuzzy sequential machines are defined to work as recognizers of handwritten strokes. An algorithm to obtain a deterministic fuzzy sequential machine from a stroke representation, that is capable of recognizing that stroke and its variants, is presented. An algorithm is developed to merge two fuzzy machines into one machine. The learning algorithm is a combination of many described algorithms. The system was tested against isolated handwritten characters provided by 20 subjects resulting in 95.8% recognition rate which is encouraging and shows that the system is highly flexible in dealing with shape and size variations. In Part Three, also an entirely novel text recognition system, capable of recognizing off-line handwritten Arabic cursive text having a high variability is presented. This system is an extension of the above recognition system. Tokens are extracted from a onedimensional representation of a stroke. Fuzzy sequential machines are defined to work as recognizers of tokens. It is shown how to obtain a deterministic fuzzy sequential machine from a token representation that is capable'of recognizing that token and its variants. An algorithm for token learning is presented. The tokens of a stroke are re-combined to meaningful strings of tokens. Algorithms to recognize and learn token strings are described. The. recognition stage uses algorithms of the learning stage. The process of extracting the best set of basic shapes which represent the best set of token strings that constitute an unknown stroke is described. A method is developed to extract lines from pages of handwritten text, arrange main strokes of extracted lines in the same order as they were written, and present secondary strokes to main strokes. Presented secondary strokes are combined with basic shapes to obtain the final characters by formulating and solving assignment problems for this purpose. Some secondary strokes which remain unassigned are individually manipulated. The system was tested against the handwritings of 20 subjects yielding overall subword and character recognition rates of 55.4% and 51.1%, respectively

Recognition of mathematical handwriting on whiteboards

Automatic recognition of handwritten mathematics has enjoyed significant improvements in the past decades. In particular, online recognition of mathematical formulae has seen a number of important advancements. However, in reality most mathematics is still taught and developed on regular whiteboards and offline recognition remains an open and challenging task in this area. In this thesis we develop methods to recognise mathematics from static images of handwritten expressions on whiteboards, while leveraging the strength of online recognition systems by transforming offline data into online information. Our approach is based on trajectory recovery techniques, that allow us to reconstruct the actual stroke information necessary for online recognition. To this end we develop a novel recognition process especially designed to deal with whiteboards by prudently extracting information from colour images. To evaluate our methods we use an online recogniser for the recognition task, which is specifically trained for recognition of maths symbols. We present our experiments with varying quality and sources of images. In particular, we have used our approach successfully in a set of experiments using Google Glass for capturing images from whiteboards, in which we achieve highest accuracies of 88.03% and 84.54% for segmentation and recognition of mathematical symbols respectively

Robust segmentation of historical parchment XMT images for virtual unrolling

Historical parchment scrolls are fragile, and prone to damage from a variety of causes such as fire, water, and general mistreatment. Consequently many of these scrolls cannot be unrolled, so that their contents have remained hidden for centuries. To overcome these difficulties, we have developed a method of segmenting X-ray tomographic scans of parchment which enables a “virtual unrolling” of these documents. After an initial segmentation we link the broken layers of the parchment. Then, junction sections are extracted from the boundaries of the parchment. Subsequently, we find the fused regions which are formed by layers stuck together, and separate them into several layers by reconstructing the missing boundaries using parallel connecting curves. Experiments on the fifteenth century Bressingham scroll validate the effectiveness of our segmentation method

Information Preserving Processing of Noisy Handwritten Document Images

Many pre-processing techniques that normalize artifacts and clean noise induce anomalies due to discretization of the document image. Important information that could be used at later stages may be lost. A proposed composite-model framework takes into account pre-printed information, user-added data, and digitization characteristics. Its benefits are demonstrated by experiments with statistically significant results. Separating pre-printed ruling lines from user-added handwriting shows how ruling lines impact people\u27s handwriting and how they can be exploited for identifying writers. Ruling line detection based on multi-line linear regression reduces the mean error of counting them from 0.10 to 0.03, 6.70 to 0.06, and 0.13 to 0.02, com- pared to an HMM-based approach on three standard test datasets, thereby reducing human correction time by 50%, 83%, and 72% on average. On 61 page images from 16 rule-form templates, the precision and recall of form cell recognition are increased by 2.7% and 3.7%, compared to a cross-matrix approach. Compensating for and exploiting ruling lines during feature extraction rather than pre-processing raises the writer identification accuracy from 61.2% to 67.7% on a 61-writer noisy Arabic dataset. Similarly, counteracting page-wise skew by subtracting it or transforming contours in a continuous coordinate system during feature extraction improves the writer identification accuracy. An implementation study of contour-hinge features reveals that utilizing the full probabilistic probability distribution function matrix improves the writer identification accuracy from 74.9% to 79.5%

Comparison of Perceptions of Single Mothers and Christian Leaders through Transformational Change

The purpose of this phenomenological qualitative study was to examine the transformational change process of single mothers from their perspective, and to then assess the influence of Christian leaders in this transformational change process from the leaders’ perspective, with respect to the single mother’s family stability: spiritually, emotionally, financially and generationally. The two theories working simultaneously in this study was the transformational leadership theory and transformational learning theory. The implementation of these two theories could spark an authentic life-long change in the single mother. Understanding the role of transformational leadership in Christian leaders then becomes necessary to guide single mothers towards spiritual formation and family stability, breaking the generational pattern and ultimately becoming who God intends her to be “in His image”. Through interviews and surveys of Christian leaders, who then identified potential participants for the sample group of single mothers, the perceptions of both groups were compared to determine the effectiveness of transformational change. By reviewing the responses, perceived barriers surfaced as possible hindrances to a transformational change, as well as perceived indicators which could be used as a future predictor of transformational change that is generational

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

Lonergan's early development in ethics: a study of archives notes on general ethics (a metaphysics of customs)

In 1940, Bernard Lonergan was forced to flee Rome quickly. He took a few pages of an essay on Newman, seven essays and sketches later found in File 713, a set of handwritten notes on his reading of Kant’s Groundwork for the Metaphysics of Customs, and a set of hand written sketches titled “General Ethic [Metaphysics of Customs].” These sketches have gone relatively untouched within the body of existing scholarship on Lonergan. First, this project establishes the significance of these sketches, dates their composition, and discusses a context for understanding their relevance. Secondly, using the functional specialization of research, it provides preliminary research notes that will aid in a future interpretation of the text. Thirdly, it establishes the sketches as an early outline of Lonergan’s understanding of the metaphysic of ethics found in chapter 18 of Insight. The project highlights connections between the sketches and Lonergan’s thoughts on Kant, the dialectic of history, and Ethics