1,720,972 research outputs found
Statistical pattern recognition for automatic writer identification and verification
Full text linkThe thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document analysis fields.We describes a number of new and very effective techniques that we have developed in recent years for automatic writer identification and verification.
Writer individuality is encoded using probability distribution functions (PDFs) extracted handwritten text blocks. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of the individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common codebook of graphemes obtained by clustering.
There are two distinguishing characteristics of our approach: human intervention is minimized in the writer identification process and we encode individual handwriting style using features designed to be independent of the textual content of the handwritten sample. In our methods the computer is completely unaware of what has been written in the sample. The handwriting is merely seen as a texture characterized by some directional probability distributions or as a simple stochastic shape-emission process characterized by a grapheme occurrence probability. Our methods were statistically evaluated using datasets with handwriting samples collected from up to 900 subjects.
The development of our writer identification techniques takes place at a time when many biometric methods (e.g. iris, fingerprint, face) undergo a transition from research to real full-scale deployment. Our methods also have practical feasibility and hold the promise of direct applicability.
The main chapters of the thesis are based on a number of scientific papers that were published during the duration of the PhD project. Here is an outline with short abstracts for the main chapters and references to the corresponding papers.
Statistical pattern recognition for automatic writer identification and verification
Full text linkThe thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document analysis fields.We describes a number of new and very effective techniques that we have developed in recent years for automatic writer identification and verification. Writer individuality is encoded using probability distribution functions (PDFs) extracted handwritten text blocks. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of the individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common codebook of graphemes obtained by clustering. There are two distinguishing characteristics of our approach: human intervention is minimized in the writer identification process and we encode individual handwriting style using features designed to be independent of the textual content of the handwritten sample. In our methods the computer is completely unaware of what has been written in the sample. The handwriting is merely seen as a texture characterized by some directional probability distributions or as a simple stochastic shape-emission process characterized by a grapheme occurrence probability. Our methods were statistically evaluated using datasets with handwriting samples collected from up to 900 subjects. The development of our writer identification techniques takes place at a time when many biometric methods (e.g. iris, fingerprint, face) undergo a transition from research to real full-scale deployment. Our methods also have practical feasibility and hold the promise of direct applicability. The main chapters of the thesis are based on a number of scientific papers that were published during the duration of the PhD project. Here is an outline with short abstracts for the main chapters and references to the corresponding papers
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Statistical pattern recognition for automatic writer identification and verification
Full text linkThe thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document analysis fields.We describes a number of new and very effective techniques that we have developed in recent years for automatic writer identification and verification. Writer individuality is encoded using probability distribution functions (PDFs) extracted handwritten text blocks. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of the individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common codebook of graphemes obtained by clustering. There are two distinguishing characteristics of our approach: human intervention is minimized in the writer identification process and we encode individual handwriting style using features designed to be independent of the textual content of the handwritten sample. In our methods the computer is completely unaware of what has been written in the sample. The handwriting is merely seen as a texture characterized by some directional probability distributions or as a simple stochastic shape-emission process characterized by a grapheme occurrence probability. Our methods were statistically evaluated using datasets with handwriting samples collected from up to 900 subjects. The development of our writer identification techniques takes place at a time when many biometric methods (e.g. iris, fingerprint, face) undergo a transition from research to real full-scale deployment. Our methods also have practical feasibility and hold the promise of direct applicability. The main chapters of the thesis are based on a number of scientific papers that were published during the duration of the PhD project. Here is an outline with short abstracts for the main chapters and references to the corresponding papers
Statistical pattern recognition for automatic writer identification and verification
No full textThe thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document analysis fields.We describes a number of new and very effective techniques that we have developed in recent years for automatic writer identification and verification. Writer individuality is encoded using probability distribution functions (PDFs) extracted handwritten text blocks. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of the individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common codebook of graphemes obtained by clustering. There are two distinguishing characteristics of our approach: human intervention is minimized in the writer identification process and we encode individual handwriting style using features designed to be independent of the textual content of the handwritten sample. In our methods the computer is completely unaware of what has been written in the sample. The handwriting is merely seen as a texture characterized by some directional probability distributions or as a simple stochastic shape-emission process characterized by a grapheme occurrence probability. Our methods were statistically evaluated using datasets with handwriting samples collected from up to 900 subjects. The development of our writer identification techniques takes place at a time when many biometric methods (e.g. iris, fingerprint, face) undergo a transition from research to real full-scale deployment. Our methods also have practical feasibility and hold the promise of direct applicability. The main chapters of the thesis are based on a number of scientific papers that were published during the duration of the PhD project. Here is an outline with short abstracts for the main chapters and references to the corresponding papers
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Statistical pattern recognition for automatic writer identification and verification
Full text linkThe thesis addresses the problem of automatic person identification using scanned images of handwriting.Identifying the author of a handwritten sample using automatic image-based methods is an interesting pattern recognition problem with direct applicability in the forensic and historic document analysis fields.We describes a number of new and very effective techniques that we have developed in recent years for automatic writer identification and verification. Writer individuality is encoded using probability distribution functions (PDFs) extracted handwritten text blocks. Our methods operate at two levels of analysis: the texture level and the character-shape (allograph) level. At the texture level, we use contour-based joint directional PDFs that encode orientation and curvature information to give an intimate characterization of the individual handwriting style. In our analysis at the allograph level, the writer is considered to be characterized by a stochastic pattern generator of ink-trace fragments, or graphemes. The PDF of these simple shapes in a given handwriting sample is characteristic for the writer and is computed using a common codebook of graphemes obtained by clustering. There are two distinguishing characteristics of our approach: human intervention is minimized in the writer identification process and we encode individual handwriting style using features designed to be independent of the textual content of the handwritten sample. In our methods the computer is completely unaware of what has been written in the sample. The handwriting is merely seen as a texture characterized by some directional probability distributions or as a simple stochastic shape-emission process characterized by a grapheme occurrence probability. Our methods were statistically evaluated using datasets with handwriting samples collected from up to 900 subjects. The development of our writer identification techniques takes place at a time when many biometric methods (e.g. iris, fingerprint, face) undergo a transition from research to real full-scale deployment. Our methods also have practical feasibility and hold the promise of direct applicability. The main chapters of the thesis are based on a number of scientific papers that were published during the duration of the PhD project. Here is an outline with short abstracts for the main chapters and references to the corresponding papers
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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