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Определение автора текста

Analysis of Handwriting Individuality Using Word Features Авторы: Bin Zhang Sargur N. Srihari
Организация: CEDAR, Computer Science and Engineering Department State University of New York at Buffalo
Дата: 2003 год
Кол-во страниц: 5
Analysis of allographs (characters) and allograph combinations (words) is the key for obtaining the discriminating elements of handwriting. While allographs usually inhabit in words and segregation of a word into allographs is more subjective than objective, especially for cursive writing, analysis of handwritten words is a natural and better option. In this study, a handwritten word image is characterized by gradient, structural, and concavity features, and individuality of handwritten words is experimented through writer ship identification and verification on over 12,000 word images extracted from 3000 handwriting samples of 1000 individuals in U.S.. Experimental results show that handwritten words are very effective in differentiating handwriting and carry more individuality than most handwritten characters(allographs).
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Binary Vector Dissimilarity Measures for Handwriting Identification Авторы: Bin Zhang and Sargur N. Srihari
Организация: CEDAR, Computer Science and Engineering Department State University of New York at Buffalo
Дата: 2003 год
Кол-во страниц: 11
Several dissimilarity measures for binary vectors are formulated and examined for their recignition capability in handwritten identification for whitch the binary micro-features are used to characterize handwritten character shapes. Pertaining to eight dissimilarity measures, i.e., Jaccard-Needham, Dice, Correlation, Yule, Russell-Rao, Sokal-Michener, Rogers-Tanmoto and Kulzinsky, the discriminative power of ten individual characters and their combination is exhaustively studied. It is that different character shapes and different positions of a character in a word bear different capability for identifying handwritted individuality. Moreover, a character alone usually is not informative enough to identify handwriting differences, but a combination of many characters presents good performance in handwriting identification. While five measures, Rogers-Tanmoto, Jaccard-Needham, Dice, Correlation, and Sokal-Michener, cinsistently perform very well in identification, Kulzinsky and Russell-Rao ones give low identification accuracy.
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Writer and Writing-Style Classification in the Recognition of Online Handwriting. Авторы: Lambert Schomaker, Gerben Abbink, & Sjoerd Selen
Организация: Nijmegen Institute for Cognition and Information (NICI).
Дата: 1994 год
Кол-во страниц: 4
The results indicate that a reliable classification writers is possible, and interpretable style groupings can be formed. As opposed to the broad distinction between cursive, mixed and handprint, we propose to use a more subtle classification based on the characteristical set of stroke shapes produced by a given writer. Utilization of this type of knowledge will ultimately allow for recognition systems to adapt to a given writer with less user intervention, such as training the system at the character or word-level.
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Individuality of Handwritten Characters Авторы: Bin hang Sargur N. Srihari Sangjik Lee
Организация: CEDAR, Computer Science and Engineering Department State University of New Yorkat Buffalo
Дата: 2003 год
Кол-во страниц: 5
Analysis of handwritten characters (allographs) plays an important role in forensic document examination. However, so far there lacks a comprehensive and quantitative study on individuality of handwritten characters. Based on a large number of handwritten characters extracted from handwriting samples of 1000 individuals in US, the individuality of handwritten characters has been quantitatively measured through identification and verification models. Our study shows that in general alphabetic characters bear more individuality than numerals and use of a certain number of characters will significantly outperform the global features of handwriting samples in handwriting identification and verification. Moreover, the quantitative measurement of discriminative powers of characters offers a general guidance for selecting most-informative characters in examin- ingforensic documents.
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Individuality of Numerals Авторы: Sargur N. Srihari, CatalinTomai, Sangjik Lee and Bin Zhang
Организация: CEDAR, Computer Science and Engineering Department State University of New Yorkat Buffalo
Дата: 2003 год
Кол-во страниц: 5
The analysis of handwritten documents from the viewpoint of determining their authorship has great bearing on the criminal justice system. In many cases, only a limited amount of handwriting is available and sometimes it consists of only numerals. Using a large number of handwritten numeral images extracted from about 3000 samples written by 1000 writers, a study of the individuality of numerals for identification/verification purposes was conducted. We have studied the individuality of numerals using clusterization and measured the numerals discriminability for verificationpurposes. The study shows that some numerals present a higher discriminatory power and that their performances for the verification/identification tasks are very different.
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