In handwritten text recognition, compared to human, computers are far short of linguistic context knowledge, especially domain-matched knowledge. In this paper, we present a novel retrieval-based method to obtain an adaptive language model for offline recognition of unconstrained handwritten Chinese texts. The content of handwritten texts to be recognized is varied and usually unknown a priori. Therefore we adopt a two-pass recognition strategy. In the first pass, we utilize a common language model to obtain initial recognition results, which are used to retrieve the related contents from Internet. In the content retrieval, we evaluate different types of semantic representation from BERT output and the traditional TF–IDF representation. Then, we dynamically generate an adaptive language model from these related contents, which will consequently be combined with the common language model and applied in the second-pass recognition. We evaluate the proposed method on two benchmark unconstrained handwriting datasets, namely CASIA-HWDB and ICDAR-2013. Experimental results show that the proposed retrieval-based language model adaptation yields improvements in recognition performance, despite the reduced Internet contents hereby employed.

在手写文本识别方面，与人类相比，计算机远远缺乏语言上下文知识，尤其是领域匹配知识。在本文中，我们提出了一种新的基于检索的方法来获得用于离线识别无约束手写中文文本的自适应语言模型。要识别的手写文本的内容是多种多样的，通常是先验未知的。因此，我们采用两遍识别策略。在第一遍中，我们利用通用语言模型获得初始识别结果，用于从 Internet 检索相关内容。在内容检索中，我们从 BERT 输出和传统的 TF-IDF 表示中评估不同类型的语义表示。然后，我们从这些相关内容动态生成自适应语言模型，因此，它将与通用语言模型相结合，并应用于第二遍识别。我们在两个基准无约束手写数据集上评估所提出的方法，即 CASIA-HWDB 和 ICDAR-2013。实验结果表明，尽管减少了互联网内容，但所提出的基于检索的语言模型自适应改进了识别性能。