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.

手写识别中，深度学习取得了显著成就。然而，神经网络在处理数据分布转变时存在问题。本文讨论如何使手写识别模型能够自适应不同风格的书写，通过使用少量新人笔迹的例子进行适应。通过两种基本模型以及模型无关元学习和作家代码两种方法，实验结果表明 MetaHTR 在适应性上优于基准模型，提高了 1.4 到 2.0 的词错误率，并且深层模型适应性好于浅层模型。然而，MetaHTR 在更大模型或句子级别的手写识别中的计算和内存需求可能变得不切实际，而基于学习特征或 Hinge 统计特征的作家代码并未提高识别性能。