A note on binary template matching
Pattern Recognition
Off-line writer verification using ordinary characters as the object
Pattern Recognition
Text-Independent Writer Identification and Verification Using Textural and Allographic Features
IEEE Transactions on Pattern Analysis and Machine Intelligence
Invariants Discretization for Individuality Representation in Handwritten Authorship
IWCF '08 Proceedings of the 2nd international workshop on Computational Forensics
A Machine Learning Approach to Off-Line Signature Verification Using Bayesian Inference
IWCF '09 Proceedings of the 3rd 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
Machine learning for signature verification
ICVGIP'06 Proceedings of the 5th Indian conference on Computer Vision, Graphics and Image Processing
A novel sparse model based forensic writer identification
Pattern Recognition Letters
SOCIFS feature selection framework for handwritten authorship
International Journal of Hybrid Intelligent Systems
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Analysis of allographs (characters) and allograph combinations(words) is the key for obtaining the discriminatingelements of handwriting. While allographs usually inhabitin words and segregation of a word into allographs ismore subjective than objective, especially for cursive writing,analysis of handwritten words is a natural and betteroption. In this study, a handwritten word image is characterizedby gradient, structural, and concavity features, andindividuality of handwritten words is experimented throughwritership identification and verification on over 12,000word images extracted from 3000 handwriting samples of1000 individuals in U.S.. Experimental results show thathandwritten words are very effective in differentiating handwritingand carry more individuality than most handwrittencharacters (allographs).