Mattick A, Mayr M, Seuret M, Maier A, Christlein V (2021)
Publication Type: Conference contribution
Publication year: 2021
Publisher: Springer Science and Business Media Deutschland GmbH
Book Volume: 12821 LNCS
Pages Range: 268-283
Conference Proceedings Title: Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Event location: Lausanne, CHE
ISBN: 9783030865481
URI: https://arxiv.org/abs/2105.10528
DOI: 10.1007/978-3-030-86549-8_18
As of recent generative adversarial networks have allowed for big leaps in the realism of generated images in diverse domains, not the least of which being handwritten text generation. The generation of realistic-looking handwritten text is important because it can be used for data augmentation in handwritten text recognition (HTR) systems or human-computer interaction. We propose SmartPatch, a new technique increasing the performance of current state-of-the-art methods by augmenting the training feedback with a tailored solution to mitigate pen-level artifacts. We combine the well-known patch loss with information gathered from the parallel trained handwritten text recognition system and the separate characters of the word. This leads to a more enhanced local discriminator and results in more realistic and higher-quality generated handwritten words.
APA:
Mattick, A., Mayr, M., Seuret, M., Maier, A., & Christlein, V. (2021). SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators. In Josep Lladós, Daniel Lopresti, Seiichi Uchida (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 268-283). Lausanne, CHE: Springer Science and Business Media Deutschland GmbH.
MLA:
Mattick, Alexander, et al. "SmartPatch: Improving Handwritten Word Imitation with Patch Discriminators." Proceedings of the 16th International Conference on Document Analysis and Recognition, ICDAR 2021, Lausanne, CHE Ed. Josep Lladós, Daniel Lopresti, Seiichi Uchida, Springer Science and Business Media Deutschland GmbH, 2021. 268-283.
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