  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 finetuning 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.
