Towards Writing Style Adaptation in Handwriting Recognition
Jan Koh\'ut, Michal Hradi\v{s}, Martin Ki\v{s}\v{s}

TL;DR
This paper introduces a writer style adaptation method for handwriting recognition using a Writer Style Block and learned embeddings, improving accuracy in writer-dependent scenarios and enabling new writer embedding estimation.
Contribution
It proposes a novel Writer Style Block with adaptive normalization conditioned on learned writer embeddings for handwriting recognition.
Findings
WSB improves accuracy in writer-dependent recognition
Embeddings can be estimated for new writers
Domain adaptation with fine-tuning outperforms WSB in some cases
Abstract
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…
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Taxonomy
TopicsHandwritten Text Recognition Techniques · Hand Gesture Recognition Systems · Human Pose and Action Recognition
MethodsInstance Normalization · Adaptive Instance Normalization
