One of the challenges of handwriting recognition is to transcribe a large number of vastly different writing styles. State-of-the-art approaches do not explicitly use information about the writer’s style, which may be limiting overall accuracy due to various ambiguities. We explore models with writer-dependent parameters which take the writer’s identity as an additional input. The proposed models can be trained on datasets with partitions likely written by a single author (e.g. single letter, diary, or chronicle). We propose a Writer Style Block (WSB), an adaptive instance normalization layer conditioned on learned embeddings of the partitions. We experimented with various placements and settings of WSB and contrastively pre-trained embeddings. We show that our approach outperforms a baseline with no WSB in a writer-dependent scenario and that it is possible to estimate embeddings for new writers. However, domain adaptation using simple fine-tuning in a writer-independent setting provides superior accuracy at a similar computational cost. The proposed approach should be further investigated in terms of training stability and embedding regularization to overcome such a baseline.
CITATION STYLE
Kohút, J., Hradiš, M., & Kišš, M. (2023). Towards Writing Style Adaptation in Handwriting Recognition. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 14190 LNCS, pp. 377–394). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-41685-9_24
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