HOW TO OVERCOME ANALPHABETISM IN READING CHINESE CHARACTER

Most languages in the world use some system of alphabetical characters: Latin, Greek, Cyrillic, Hebrew, Arabic, Hindi and so on. A foreigner that does not know the language can get some information from a written text provided he knows the alphabet and disposes of a dictionary. When there is no longer an alphabet, but only pictorial characters, the problem becomes at first unsolvable. Chinese is the main language where an ignorant foreigner is completely analphabet. Fortunately there are methods that after some training allow the recognition of pictorial characters. In our university some twenty pupils of the Excellence School participated to an experiment of Chinese alphabetization gluing a traditional practical Chinese first course with information theory methods for dealing with image data bases. In this article first we discuss both the theoretical foundations. Then we give a report of the merging of the two conceptual schemes as it was performed at the excellence school. Finally we draw some conclusions about improvements of the method

Use of prior knowledge in classification of similar and structured objects

Statistical machine learning has achieved great success in many fields in the last few decades. However, there remain classification problems that computers still struggle to match human performance. Many such problems share the same properties---large within class variability and complex structure in the examples, which is often true for real world objects. This does not mean lack of information for classification in the examples. On the contrary, there is still a clear pattern in the examples, but hidden behind a many-way covariance structure such that useful information is too dilute for conventional statistical machine learners to pick up. However, if we can exploit the structural nature of the objects and concentrate information about the classification, the problem can become much easier. In this dissertation we propose a framework using prior knowledge about modeling the structures in the examples to concentrate information for classification. The framework is instantiated to the task of classifying pairs of similar offline handwritten Chinese characters. We empirically demonstrate that our proposed framework indeed concentrates useful information for classification and makes the classification problem easier for statistical learning. Our approach advances the state of the art both in offline handwritten character recognition and in machine learning

Feature Extraction Methods for Character Recognition

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Constructing a concept of number

Numbers are concepts whose content, structure, and organization are influenced by the material forms used to represent and manipulate them. Indeed, as argued here, it is the inclusion of multiple forms (distributed objects, fingers, single- and two-dimensional forms like pebbles and abaci, and written notations) that is the mechanism of numerical elaboration. Further, variety in employed forms explains at least part of the synchronic and diachronic variability that exists between and within cultural number systems. Material forms also impart characteristics like linearity that may persist in the form of knowledge and behaviors, ultimately yielding numerical concepts that are irreducible to and functionally independent of any particular form. Material devices used to represent and manipulate numbers also interact with language in ways that reinforce or contrast different aspects of numerical cognition. Not only does this interaction potentially explain some of the unique aspects of numerical language, it suggests that the two are complementary but ultimately distinct means of accessing numerical intuitions and insights. The potential inclusion of materiality in contemporary research in numerical cognition is advocated, both for its explanatory power, as well as its influence on psychological, behavioral, and linguistic aspects of numerical cognition

Active Shape Modeling of the Hip in the Prediction of Incident Hip Fracture

The objective of this study was to evaluate right proximal femur shape as a risk factor for incident hip fracture using active shape modeling (ASM). A nested case-control study of white women 65 years of age and older enrolled in the Study of Osteoporotic Fractures (SOF) was performed. Subjects (n = 168) were randomly selected from study participants who experienced hip fracture during the follow-up period (mean 8.3 years). Controls (n = 231) had no fracture during follow-up. Subjects with baseline radiographic hip osteoarthritis were excluded. ASM of digitized right hip radiographs generated 10 independent modes of variation in proximal femur shape that together accounted for 95% of the variance in proximal femur shape. The association of ASM modes with incident hip fracture was analyzed by logistic regression. Together, the 10 ASM modes demonstrated good discrimination of incident hip fracture. In models controlling for age and body mass index (BMI), the area under receiver operating characteristic (AUROC) curve for hip shape was 0.813, 95% confidence interval (CI) 0.771–0.854 compared with models containing femoral neck bone mineral density (AUROC = 0.675, 95% CI 0.620–0.730), intertrochanteric bone mineral density (AUROC = 0.645, 95% CI 0.589–0.701), femoral neck length (AUROC = 0.631, 95% CI 0.573–0.690), or femoral neck width (AUROC = 0.633, 95% CI 0.574–0.691). The accuracy of fracture discrimination was improved by combining ASM modes with femoral neck bone mineral density (AUROC = 0.835, 95% CI 0.795–0.875) or with intertrochanteric bone mineral density (AUROC = 0.834, 95% CI 0.794–0.875). Hips with positive standard deviations of ASM mode 4 had the highest risk of incident hip fracture (odds ratio = 2.48, 95% CI 1.68–3.31, p < .001). We conclude that variations in the relative size of the femoral head and neck are important determinants of incident hip fracture. The addition of hip shape to fracture-prediction tools may improve the risk assessment for osteoporotic hip fractures. © 2011 American Society for Bone and Mineral Research

Chinese calligraphy: character style recognition based on full-page document

Calligraphy plays a very important role in the history of China. From ancient times to modern times, the beauty of calligraphy has been passed down to the present. Different calligraphy styles and structures have made calligraphy a beauty and embodiment in the field of writing. However, the recognition of calligraphy style and fonts has always been a blank in the computer field. The structural complexity of different calligraphy also brings a lot of challenges to the recognition technology of computers. In my research, I mainly discussed some of the main recognition techniques and some popular machine learning algorithms in this field for more than 20 years, trying to find a new method of Chinese calligraphy styles recognition and exploring its feasibility. In our research, we searched for research papers 20 years ago. Most of the results are about the content recognition of modern Chinese characters. At first, we analyze the development of Chinese characters and the basic Chinese character theory. In the analysis of the current recognition of Chinese characters (including handwriting online and offline) in the computer field, it is more important to analyze various algorithms and results, and to analyze how to use the experimental data, besides how they construct the data set used for their test. The research on the method of image processing based on Chinese calligraphy works is very limited, and the data collection for calligraphy test is very limited also. The test of dataset that used between different recognition technologies is also very different. However, it has far-reaching significance for inheriting and carrying forward the traditional Chinese culture. It is very necessary to develop and promote the recognition of Chinese characters by means of computer tecnchque. In the current application field, the font recognition of Chinese calligraphy can effectively help the library administrators to identify the problem of the classification of the copybook, thus avoiding the recognition of the calligraphy font which is difficult to perform manually only through subjective experience. In the past 10 years of technology, some techniques for the recognition of single Chinese calligraphy fonts have been given. Most of them are the pre-processing of calligraphy characters, the extraction of stroke primitives, the extraction of style features, and the final classification of machine learning. The probability of the classification of the calligraphy works. Such technical requirements are very large for complex Chinese characters, the result of splitting and recognition is very large, and it is difficult to accurately divide many complex font results. As a result, the recognition rate is low, or the accuracy of recognition of a specific word is high, but the overall font recognition accuracy is low. We understand that Chinese calligraphy is a certain research value. In the field of recognition, many research papers on the analysis of Chinese calligraphy are based on the study of calligraphy and stroke. However, we have proposed a new method for dealing with font recognition. The recognition technology is based on the whole page of the document. It is studied in three steps: the first step is to use Fourier transform and some Chinese calligraphy images and analyze the results. The second is that CNN is based on different data sets to get some results. Finally, we made some improvements to the CNN structure. The experimental results of the thesis show that the full-page documents recognition method proposed can achieve high accuracy with the support of CNN technology, and can effectively identify the different styles of Chinese calligraphy in 5 styles. Compared with the traditional analysis methods, our experimental results show that the method based on the full-page document is feasible, avoiding the cumbersome font segmentation problem. This is more efficient and more accurate

Using contour information and segmentation for object registration, modeling and retrieval

This thesis considers different aspects of the utilization of contour information and syntactic and semantic image segmentation for object registration, modeling and retrieval in the context of content-based indexing and retrieval in large collections of images. Target applications include retrieval in collections of closed silhouettes, holistic w ord recognition in handwritten historical manuscripts and shape registration. Also, the thesis explores the feasibility of contour-based syntactic features for improving the correspondence of the output of bottom-up segmentation to semantic objects present in the scene and discusses the feasibility of different strategies for image analysis utilizing contour information, e.g. segmentation driven by visual features versus segmentation driven by shape models or semi-automatic in selected application scenarios. There are three contributions in this thesis. The first contribution considers structure analysis based on the shape and spatial configuration of image regions (socalled syntactic visual features) and their utilization for automatic image segmentation. The second contribution is the study of novel shape features, matching algorithms and similarity measures. Various applications of the proposed solutions are presented throughout the thesis providing the basis for the third contribution which is a discussion of the feasibility of different recognition strategies utilizing contour information. In each case, the performance and generality of the proposed approach has been analyzed based on extensive rigorous experimentation using as large as possible test collections

Semantic radical consistency and character transparency effects in Chinese: an ERP study

BACKGROUND: This event-related potential (ERP) study aims to investigate the representation and temporal dynamics of Chinese orthography-to-semantics mappings by simultaneously manipulating character transparency and semantic radical consistency. Character components, referred to as radicals, make up the building blocks used dur...postprin