The task of multi-scene ancient Chinese text recognition (MACR) is challenging due to large-scale categories, high intra-class variance and inter-class similarity and complicated backgrounds. Little effort has been devoted to MACR research due to insufficient datasets and language barrier. Because the sub-dataset generation process of sub-dataset is mutually blind, there are discrepancies in the class category number, deep feature representation and class center distribution after the dataset statistics and character analysis are performed. The general deep learning method that assumes that data are independent and identically distributed is inappropriate. The deep coupled alignments (CA) module based on domain adaptation is presented to alleviate domain and class center shifts. In addition, a cross-domain fusion (CF) module is proposed to mitigate negative transfer in partial domain adaptation by updating the target domain with the full-class and augmenting the source domain with pseudo labeled samples. Extensive experiments of the proposed method are conducted, and the results illustrate the superiority of CA–CF to previous methods in terms of the model size and recognition accuracy.

由于种类繁多，类别间差异大，类别间相似度高以及背景复杂，多场景古代中文文本识别（MACR）的任务具有挑战性。由于数据集不足和语言障碍，MACR研究工作很少。由于子数据集的子数据集生成过程是相互盲目的，因此在进行数据集统计和字符分析后，类类别编号，深层特征表示和类中心分布存在差异。假设数据是独立且均匀分布的通用深度学习方法是不合适的。提出了基于域自适应的深度耦合比对（CA）模块，以减轻域和类中心的偏移。此外，提出了一种跨域融合（CF）模块，通过使用全类更新目标域并使用伪标记样本扩展源域，来缓解部分域自适应中的负迁移。对该方法进行了广泛的实验，结果证明了CA–CF在模型大小和识别精度方面优于以前的方法。