  Handwriting recognition has seen significant success with the use of deep
learning. However, a persistent shortcoming of neural networks is that they are
not well-equipped to deal with shifting data distributions. In the field of
handwritten text recognition (HTR), this shows itself in poor recognition
accuracy for writers that are not similar to those seen during training. An
ideal HTR model should be adaptive to new writing styles in order to handle the
vast amount of possible writing styles. In this paper, we explore how HTR
models can be made writer adaptive by using only a handful of examples from a
new writer (e.g., 16 examples) for adaptation. Two HTR architectures are used
as base models, using a ResNet backbone along with either an LSTM or
Transformer sequence decoder. Using these base models, two methods are
considered to make them writer adaptive: 1) model-agnostic meta-learning
(MAML), an algorithm commonly used for tasks such as few-shot classification,
and 2) writer codes, an idea originating from automatic speech recognition.
Results show that an HTR-specific version of MAML known as MetaHTR improves
performance compared to the baseline with a 1.4 to 2.0 improvement in word
error rate (WER). The improvement due to writer adaptation is between 0.2 and
0.7 WER, where a deeper model seems to lend itself better to adaptation using
MetaHTR than a shallower model. However, applying MetaHTR to larger HTR models
or sentence-level HTR may become prohibitive due to its high computational and
memory requirements. Lastly, writer codes based on learned features or Hinge
statistical features did not lead to improved recognition performance.
