Document image understanding: integrating recovery and interpretation
Document image understanding: integrating recovery and interpretation
Recovery of temporal information from static images of handwriting
International Journal of Computer Vision - Special issue: image understanding research at the University of Maryland
Off-Line Signature Verification by Local Granulometric Size Distributions
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Static Signature Verification Employing a Kosko-Neuro-fuzzy Approach
AFSS '02 Proceedings of the 2002 AFSS International Conference on Fuzzy Systems. Calcutta: Advances in Soft Computing
The IRESTE On/Off (IRONOFF) Dual Handwriting Database
ICDAR '99 Proceedings of the Fifth International Conference on Document Analysis and Recognition
Ink Texture Analysis for Writer Identification
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Hidden Loop Recovery for Handwriting Recognition
IWFHR '02 Proceedings of the Eighth International Workshop on Frontiers in Handwriting Recognition (IWFHR'02)
Generating Realistic Kanji Character Images from On-Line Patterns
ICDAR '01 Proceedings of the Sixth International Conference on Document Analysis and Recognition
Use of distance measures in handwriting analysis
Use of distance measures in handwriting analysis
A Hybrid On/Off Line Handwritten Signature Verification System
ICDAR '03 Proceedings of the Seventh International Conference on Document Analysis and Recognition - Volume 1
Ink-Deposition Model: The Relation of Writing and Ink Deposition Processes
IWFHR '04 Proceedings of the Ninth International Workshop on Frontiers in Handwriting Recognition
Estimating the Pen Trajectories of Static Signatures Using Hidden Markov Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
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Handwritten signatures play an important role in daily life. Consequently, there is a strong need for objective signature evaluation. This paper focuses on a new computational method for discovering and evaluating ink-trace characteristics related to the writing process. It aims (i) to provide a scientific basis for procedures applied in forensic casework and (ii) to derive advanced computational methods for the analysis of signature-stroke morphology. It work towards methods for inferring writer-specific behaviors from the residual ink trace. The respective micro-patterns, caused by biomechanical writing and physical ink-deposition processes, provide important clues for the analysis. These inner ink-trace characteristics of signatures, which are determined by the individual movements of a person, will be studied in depth, taking into account the effects of writing materials, such as the type of pen used. By means of recorded and super-imposed writing movements, ink traces are sampled, and local ink-trace characteristics are encoded in one feature vector per sample record. These data establish a sequence which faithfully reflects the spatial distribution of ink-trace characteristics and solves problems of methods previously available.