Writer Identification using Innovative Binarised Features of Handwritten Numerals
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 2
Invariants Discretization for Individuality Representation in Handwritten Authorship
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
Discretization of integrated moment invariants for Writer Identification
ACST '08 Proceedings of the Fourth IASTED International Conference on Advances in Computer Science and Technology
Embedded scale united moment invariant for identification of handwriting individuality
ICCSA'07 Proceedings of the 2007 international conference on Computational science and its applications - Volume Part I
Biometric recognition using online uppercase handwritten text
Pattern Recognition
Extraction and analysis of document examiner features from vector skeletons of grapheme ‘th'
DAS'06 Proceedings of the 7th international conference on Document Analysis Systems
SOCIFS feature selection framework for handwritten authorship
International Journal of Hybrid Intelligent Systems
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Abstract: Motivated by several rulings in United States courts concerning expert testimony in general and handwriting testimony in particular, we undertook a study to objectively validate the hypothesis that handwriting is individualistic. Handwriting samples of one thousand five hundred individuals, representative of the US population with respect to gender, age, ethnic groups, etc., were obtained. Analyzing differences in handwriting was done by using computer algorithms for extracting features from scanned images of handwriting. Attributes characteristic of the handwriting were obtained, e.g., line separation, slant, character shapes, etc. These attributes, which are a subset of attributes used by expert document examiners, were used to quantitatively establish individuality by using machine learning approaches. Using global attributes of handwriting and very few characters in the writing, the ability to determine the writer with a high degree of confidence was established. The work is a step towards providing scientific support for admitting handwriting evidence in court. The mathematical approach and the resulting software also have the promise of aiding the expert document examiner.