Mahatir Ahmed Tusher1, Saket Choudary Kongara1, Sagar Dhanraj Pande2, SeongKi Kim3,*, Salil Bharany4,*
CMC-Computers, Materials & Continua, Vol.79, No.3, pp. 4063-4080, 2024, DOI:10.32604/cmc.2024.049007
Abstract The challenging task of handwriting style synthesis requires capturing the individuality and diversity of human handwriting. The majority of currently available methods use either a generative adversarial network (GAN) or a recurrent neural network (RNN) to generate new handwriting styles. This is why these techniques frequently fall short of producing diverse and realistic text pictures, particularly for terms that are not commonly used. To resolve that, this research proposes a novel deep learning model that consists of a style encoder and a text generator to synthesize different handwriting styles. This network excels in generating conditional… More >
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