Results 141 to 150 of about 57,588 (195)

HANDWRITING-BASED PERSONAL IDENTIFICATION

International Journal of Pattern Recognition and Artificial Intelligence, 2006
Handwriting-based personal identification, which is also called handwriting-based writer identification, is an active research topic in pattern recognition. Despite continuous effort, offline handwriting-based writer identification still remains as a challenging problem because writing features can only be extracted from the handwriting image.
ZHENYU HE, XINGE YOU, YUAN YAN TANG, BIN FANG, JIANWEI DU   +4 more
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Automatic Handwriting Identification on Medieval Documents

14th International Conference on Image Analysis and Processing (ICIAP 2007), 2007
In this paper, we evaluate the performance of text-independent writer identification methods on a handwriting dataset containing medieval English documents. Applicable identification rates are achieved by combining textural features (joint directional probability distributions) with allographic features (grapheme-emission distributions).
Bulacu, M.L., Schomaker, L.R.B.
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Handwriting identification: a direction review

2009 IEEE International Conference on Signal and Image Processing Applications, 2009
Handwriting is a behavioral trait that is personal to individual. The character shape and the style of writing are visually different from one to another. Handwriting identification is a process to identify or verify the authorship of a handwriting document.
null Khaled Mohammed bin Abdl, null Siti Zaiton Mohd Hashim   +1 more
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The Identification of Handwriting

Australian Journal of Forensic Sciences, 1968
(1968). The Identification of Handwriting. Australian Journal of Forensic Sciences: Vol. 1, No. 1, pp. 23-33.
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Writer identification based on handwriting

IEE Third European Workshop on Handwriting Analysis and Recognition, 1998
This paper describes a text-independent writer identification method. The difficulties with writer identification are discussed. These include the sensitivity of the identification algorithm to variations in the size of the training samples, in the words, line and character spacing, point sizes, and scanner resolutions.
H.E.S. Said, T.N. Tan, K.D. Baker
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Top interpretable neural network for handwriting identification

Journal of Forensic Sciences, 2022
AbstractMachine learning (ML) has become one of the most promising tools in forensics, despite its dominant method of artificial neural networks (ANNs) suffering from the black‐box problem. While forensic methodology demands explainability and evaluativity, neural networks are unexplainable, hence almost unfalsifiable.
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The Practice of Handwriting Identification

The Library, 2007
This article describes, in detail and with examples, a methodology for analysing handwriting in order to determine the identity of the writer. This method is based on the procedure followed in forensic science laboratories and used as the basis for evidence in criminal and civil trials, and is based on its author's extensive experience in forensic ...
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Methodological Aspects of Handwriting Identification

Journal of Forensic Document Examination, 2018
Signature authentication and identification of writing or printing is one of the most common issues presented to forensic document examiners. Handwriting is a complex motor skill expressed individually as a result of learned symbols that are stored in long term memory.
H.J.J. Hardy, W. Fagel
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Influence of Serious Illness on Handwriting Identification

Postgraduate Medicine, 1956
(1956). Influence of Serious Illness on Handwriting Identification. Postgraduate Medicine: Vol. 19, No. 2, pp. A-36-A-48.
Thomas A. Gonzales, Milton Helpern, Samuel A. Levinson, Ordway Hilton   +3 more
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Personal handwriting identification based on PCA

SPIE Proceedings, 2002
In this paper, a novel algorithm is presented for writer identification from handwritings. Principal Component Analysis is applied to the gray-scale handwriting images to find a set of individual words which best characterize a person's handwriting style and have maximal difference from other people style. During identification, we only need to utilize
Long Zuo, Yunhong Wang, Tieniu Tan
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