Abstract
A person can hardly write a totally same handwriting character, more or less, there will be some tiny difference between each character. Usually, we use a neural network to generate handwriting characters, but each time we want this model to output a character, it will always the totally same. To solve this tiny different problem, we use a special neural network called DCGANs (deep convolutional generative adversarial networks). Experiments show that our method achieves good performance.
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Gu, C., Liu, J., Kong, L. (2018). Generating Realistic Chinese Handwriting Characters via Deep Convolutional Generative Adversarial Networks. In: Park, J., Loia, V., Yi, G., Sung, Y. (eds) Advances in Computer Science and Ubiquitous Computing. CUTE CSA 2017 2017. Lecture Notes in Electrical Engineering, vol 474. Springer, Singapore. https://doi.org/10.1007/978-981-10-7605-3_81
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DOI: https://doi.org/10.1007/978-981-10-7605-3_81
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