Many studies on (Offline) Handwritten Text Recognition (HTR) systems have
focused on building state-of-the-art models for line recognition on small
corpora. However, adding HTR capability to a large scale multilingual OCR
system poses new challenges. This paper addresses three problems in building
such systems: data, efficiency, and integration. Firstly, one of the biggest
challenges is obtaining sufficient amounts of high quality training data. We
address the problem by using online handwriting data collected for a large
scale production online handwriting recognition system. We describe our image
data generation pipeline and study how online data can be used to build HTR
models. We show that the data improve the models significantly under the
condition where only a small number of real images is available, which is
usually the case for HTR models. It enables us to support a new script at
substantially lower cost. Secondly, we propose a line recognition model based
on neural networks without recurrent connections. The model achieves a
comparable accuracy with LSTM-based models while allowing for better
parallelism in training and inference. Finally, we present a simple way to
integrate HTR models into an OCR system. These constitute a solution to bring
HTR capability into a large scale OCR system.

本研究主要解决将手写文本识别能力添加到大规模多语言 OCR 系统中的难点，包括数据获取，效率和集成等三个方面，通过使用在线手写数据集和基于神经网络的线识别模型等方式解决了这些问题，实现了 HTR 能力在 OCR 系统中的集成。