In this work, we explore massive pre-training on synthetic word images for
enhancing the performance on four benchmark downstream handwriting analysis
tasks. To this end, we build a large synthetic dataset of word images rendered
in several handwriting fonts, which offers a complete supervision signal. We
use it to train a simple convolutional neural network (ConvNet) with a fully
supervised objective. The vector representations of the images obtained from
the pre-trained ConvNet can then be considered as encodings of the handwriting
style. We exploit such representations for Writer Retrieval, Writer
Identification, Writer Verification, and Writer Classification and demonstrate
that our pre-training strategy allows extracting rich representations of the
writers' style that enable the aforementioned tasks with competitive results
with respect to task-specific State-of-the-Art approaches.

本文探讨了在合成单词图像上进行大规模预训练，以提高四项基准笔迹分析任务的性能。作者利用全监督目标对简单的卷积神经网络进行训练，得到编码笔迹风格的图像向量表示，进而实现了笔迹风格的作者检索、鉴别、验证、分类任务，并展示了该预训练策略提取了作者风格的丰富表征，可以在这些任务中得到与任务特定的最先进方法相竞争的结果。