TL;DR
DeepWriteSYN introduces a flexible deep learning-based method for on-line handwriting synthesis that generates realistic short-term handwriting segments, useful for variations, signatures, and verification tasks.
Contribution
It presents the first deep learning approach capable of realistic on-line handwriting synthesis in short segments, enhancing variability and verification applications.
Findings
Effective generation of handwriting variations for digits and signatures.
Significant improvement in one-shot signature verification scenarios.
Flexible synthesis in short-time segments enables diverse handwriting outputs.
Abstract
This study proposes DeepWriteSYN, a novel on-line handwriting synthesis approach via deep short-term representations. It comprises two modules: i) an optional and interchangeable temporal segmentation, which divides the handwriting into short-time segments consisting of individual or multiple concatenated strokes; and ii) the on-line synthesis of those short-time handwriting segments, which is based on a sequence-to-sequence Variational Autoencoder (VAE). The main advantages of the proposed approach are that the synthesis is carried out in short-time segments (that can run from a character fraction to full characters) and that the VAE can be trained on a configurable handwriting dataset. These two properties give a lot of flexibility to our synthesiser, e.g., as shown in our experiments, DeepWriteSYN can generate realistic handwriting variations of a given handwritten structure…
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