Realistic Handwriting Generation using Generative Adversarial Networks (Rnn)

Generating handwritings of different kinds is quite a challenging task, an area in which not much work has been done yet. Though there has been substantial research done in the area of text recognition, the opposite of handwriting generation. Handwriting generation can prove to be extremely useful for children from blind schools where their speech can get converted into text and be used to generate handwritings of different kinds for them. Handwriting generation also has an important role in field of captcha generation. Our study exhibits in what way recurrent neural networks (RNN) of the type Long Short Term Memory (LSTM) could be used in order to create a composite sequence with structure covering a long range. We propose to use that the Generative Adversarial Network algorithm can be used to generate more realistic handwriting styles with better accuracy than other algorithms. Here, we will be trying to predict one point of data at a time. Our approach is shownfor text, where the type of data is discrete. It can also be used for online handwriting, that is real-valued data. It will then be further drawn out to handwriting generation. The created network will be conditioning its predictions based on a sequence of text. We will be using the resulting system to generate highly realistic cursive handwriting in a wide variety of styles. Experiments that have been carried out on online handwriting databases that are public predict that the method that has been proposed can be used to achieve satisfactory performance, the resultant writing samples achieved a high level of similarity with original samples of handwriting.

Modeling Stability in On-line Signatures

A novel definition of stability regions and a new method for detecting them from on-line signatures is introduced in this paper. Building upon handwriting generation and motor control studies, the stability regions is defined as the longest similar sequences of strokes between a pair of genuine signatures. The stability regions are then used to select the most stable signatures, as well as to estimate the extent to which these stability regions are encountered in both genuine and simulated (forged) signatures, thus modeling the signing habit of a subject. Experimental results on the SUSig database show that the proposed model can be effectively used for signature verification. Purchase Article for $10

Understanding Parkinsonian Handwriting Through a Computational Model of Basal Ganglia

Handwriting in Parkinson's disease (PD) is typically characterized by micrographia, jagged line contour, and unusual fluctuations in pen tip velocity. Although PD handwriting features have been used for diagnostics, they are not based on a signaling model of basal ganglia (BG). In this letter, we present a computational model of handwriting generation that highlights the role of BG. When PD conditions like reduced dopamine and altered dynamics of the subthalamic nucleus and globus pallidus externa subsystems are simulated, the handwriting produced by the model manifested characteristic PD handwriting distortions like micrographia and velocity fluctuations. Our approach to PD modeling is in tune with the perspective that PD is a dynamic disease.