1,721,057 research outputs found
Support vector machines for the classification of western handwritten capitals
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Allograph based writer identification, handwriting analysis and character recognition
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82000.pdf (Publisher’s version ) (Open Access)In this thesis, techniques and features are described that were developed for the automatic comparison of handwritten characters by first matching them to prototypical character shapes (allographs). These techniques and features were evaluated in experiments simulating different real-world applications. The majority of the experiments regard forensic writer identification, where the objective is to find the writer of a piece of handwriting by comparing it to a large set of handwritten documents of which the writer is already known. The assumption is that if two documents contain many similar allographs, they may have been produced by the same writer. In the experiment described, it is demonstrated that using the techniques and features, it is indeed possible to match the correct writer with a piece of unknown handwriting. Other experiments were performed to evaluate the usefulness of the techniques and features for the classification of hand-drawn symbols and characters in differentive is not to find out who produced the writing, but what it represents) and the analysis of children's handwriting to diagnose Developmental Coordination Disorder.Radboud Universiteit Nijmegen, 04 oktober 2010Promotores : Schomaker, L.R.B., Desain, P.W.M. Co-promotor : Vuurpijl, L.G.227 p
Advances in Writer Identification and Verification
No full textThe behavioral-biometrics methods of writer identification and verification are currently enjoying renewed interest, with very promising results. This paper presents a general background and basis for handwriting biometrics. A range of current methods and applications is given. Results on a number of methods are summarized and a more in-depth example of two combined approaches is presented. By combining textural, allographic and placement features, modern systems are starting to display useful performance levels. However, user acceptance will be largely determined by explainability of system results and the integration of system decisions within a (Bayesian) framework of reasoning that is currently becoming forensic practice
Reading Systems: An introduction to Digital Document Processing
No full textAbstract. As an introduction to the area of digital document processing we first take a few steps back and take a look at the purpose of digital document processing. Subsequently a detailed comparison between the human and the artificial reading system is made. Finally, the chapter provides an overview on the book as a whole. Methods for the creation and persistent storage of text [10] have existed since the Mesopotamian clay tablets, the Chinese writings on bamboo and silk as well as the Egyptian writings on papyrus. For search and retrieval, methods for systematic archiving of complete documents in a library were developed by monks and by the clerks of emperors and kings in several cultures. However, the technology of editing an existing document by local addition and correction of text elements has a much younger history. Traditional copying and improvement of text was a painstakingly slow process, sometimes involving many man years for one single document of importance. The invention of the pencil and eraser in 1858 was one of the signs of things to come. The advent of the typing machine by Sholes in 1860 allowed for faster copying and a simultaneous on-the-fly editing of text. The computer, finally, allowed for a very convenient processing of text in digital form. However, even today, methods for generating a new document are still more advanced and mature than are the methods for processing an existing document
Text-independent writer identification and verification using textural and allographic features
No full textThe identification of a person on the basis of scanned images of handwriting is a useful biometric modality with application in forensic and historic document analysis and constitutes an exemplary study area within the research field of behavioral biometrics. We developed new and very effective techniques for automatic writer identification and verification that use probability distribution functions (PDFs) extracted from the handwriting images to characterize writer individuality. A defining property of our methods is that they are designed to be independent of the textual content of the handwritten samples. 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 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 shape codebook obtained by grapheme clustering. Combining multiple features (directional, grapheme, and run-length PDFs) yields increased writer identification and verification performance. The proposed methods are applicable to free-style handwriting (both cursive and isolated) and have practical feasibility, under the assumption that a few text lines of handwritten material are available in order to obtain reliable probability estimates
A dialogue game approach to multi-agent system programming
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64709.pdf (Publisher’s version ) (Open Access
Poster: Catch project Scratch: Script Analysis Tools for Cultural Heritage. Statistics on Queries and Line Matching
No full textRobotic writing trace synthesis and its application in the study of signature line quality
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