An Approach Based on Transformer and Deformable Convolution for Realistic Handwriting Samples Generation.

In the field of handwritten recognition, it usually needs to collect a large number of handwriting samples to obtain better results. However, it is so time-consuming and expensive that such a large number of samples cannot be collected manually. There is an effective solution to the above problem through synthesizing handwriting samples. In this study, an approach based on Transformer and deformable convolution has been proposed to generate much more realistic handwriting samples. To be specific, a Transformer has been utilized to capture the relations between writing style and text strings. By this way, global features (e.g. thickness of stroke, slant and so on) of writing style can be obtained. Meanwhile, a feature deformation fusion (FDF) module has been designed for obtaining local features (i.e. personalization) of writing style. Moreover, a focal frequency loss (FFL) is employed to solve the problem of pen-level artifacts. Experimental results demonstrate that the proposed approach can be competent for the task of handwriting samples generation and outperforms various baseline and state-of-the-art methods.