On-Line and Off-Line Handwriting Recognition: A Comprehensive Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
Visual Identification by Signature Tracking
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training Set Expansion in Handwritten Character Recognition
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Generation of Signatures by Deformations
BSDIA '97 Proceedings of the First Brazilian Symposium on Advances in Document Image Analysis
Generation of Synthetic Training Data for an HMM-based Handwriting Recognition System
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
ICPR '06 Proceedings of the 18th International Conference on Pattern Recognition - Volume 01
Biometrics: Personal Identification in Networked Society
Biometrics: Personal Identification in Networked Society
A Method for the Synthesis of Dynamic Biometric Signature Data
ICDAR '07 Proceedings of the Ninth International Conference on Document Analysis and Recognition - Volume 01
A writer identification system for on-line whiteboard data
Pattern Recognition
Improving the Enrollment in Dynamic Signature Verfication with Synthetic Samples
ICDAR '09 Proceedings of the 2009 10th International Conference on Document Analysis and Recognition
Biometric recognition using online uppercase handwritten text
Pattern Recognition
Automatic Signature Verification: The State of the Art
IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews
A New Method for the Synthesis of Signature Data With Natural Variability
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
Online text-independent writer identification based on stroke's probability distribution function
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
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This paper presents a new method to generate synthetic executions of on-line words from real samples. The proposed generation method takes advantage of the characteristics of a writer recognition system developed by the authors and can be seamlessly integrated into it. Both the generation method and the recognition system consider strokes as the structural units of handwriting with words being regarded as two sequences, one of pen-up and one of pen-down strokes. Given two samples from the same word and writer, a new sample is produced by aligning their sequences of strokes and then averaging the matching pairs. Thanks to a self-organising map used to categorise strokes, the alignment and comparison of sequences of strokes are performed in a straightforward and computationally efficient way. The synthetically generated words not only retain much of the discriminative power (i.e. the capability to discriminate among writers) of the words from which they are generated, but in some cases exhibit an increased recognition performance. Also, the newly generated words allow enlarging the number of available samples in the enrolment sets that are used to build writers' models. In most cases, this enlargement has the effect to improve the performance of the recognition system. Experimenting with 320 writers and enrolment sets containing 3 real samples and 6 synthetically generated ones, verification is improved for 15 of the 16 words in the BiosecurID database, with the minimum of the detection cost function being reduced by up to a 26.5%.