Modeling the lexical morphology of Western handwritten signatures

Static and Dynamic Synthesis of Bengali and Devanagari Signatures

Developing an automatic signature verification system is challenging and demands a large number of training samples. This is why synthetic handwriting generation is an emerging topic in document image analysis. Some handwriting synthesizers use the motor equivalence model, the well-established hypothesis from neuroscience, which analyses how a human being accomplishes movement. Specifically, a motor equivalence model divides human actions into two steps: 1) the effector independent step at cognitive level and 2) the effector dependent step at motor level. In fact, recent work reports the successful application to Western scripts of a handwriting synthesizer, based on this theory. This paper aims to adapt this scheme for the generation of synthetic signatures in two Indic scripts, Bengali (Bangla), and Devanagari (Hindi). For this purpose, we use two different online and offline databases for both Bengali and Devanagari signatures. This paper reports an effective synthesizer for static and dynamic signatures written in Devanagari or Bengali scripts. We obtain promising results with artificially generated signatures in terms of appearance and performance when we compare the results with those for real signatures.

Myoelectronic signal-based methodology for the analysis of handwritten signatures

With the overall aim of improving the synthesis of handwritten signatures, we have studied how muscle activation depends on handwriting style for both text and flourish. Surface electromyographic (EMG) signals from a set of twelve arm and trunk muscles were recorded in synchronization with handwriting produced on a digital Tablet. Correlations between these EMG signals and handwritten trajectory signals were analyzed so as to define the sequence of muscles activated during the different parts of the signature. Our results establish a correlation between the speed of the movement, stroke size, handwriting style and muscle activation. Muscle activity appeared to be clustered as a function of movement speed and handwriting style, a finding which may be used for filter design in a signature synthesizer.

Generation of Duplicated Off-Line Signature Images for Verification Systems

Biometric researchers have historically seen signature duplication as a procedure relevant to improving the performance of automatic signature verifiers. Different approaches have been proposed to duplicate dynamic signatures based on the heuristic affine transformation, nonlinear distortion and the kinematic model of the motor system. The literature on static signature duplication is limited and as far as we know based on heuristic affine transforms and does not seem to consider the recent advances in human behavior modeling of neuroscience. This paper tries to fill this gap by proposing a cognitive inspired algorithm to duplicate off-line signatures. The algorithm is based on a set of nonlinear and linear transformations which simulate the human spatial cognitive map and motor system intra-personal variability during the signing process. The duplicator is evaluated by increasing artificially a training sequence and verifying that the performance of four state-of-the-art off-line signature classifiers using two publicly databases have been improved on average as if we had collected three more real signatures.