For the multilingual interoperation in cross-country industrial systems, character recognition is a research issue that can largely facilitate the automatic information integration of an enormous number of forms, but has not been well resolved. Character recognition using the deep convolutional neural network depends on large scale training data collection and labor-intensive labeling work to train an effective model. Synthetic data generation and data augmentation are the typical means to compensate for the scarcity of labeled training data. However, the domain shift between synthetic data and real data inevitably results in unsatisfying recognition accuracy, bringing a significant challenge. To alleviate such an issue, a recognition system with enhanced two-phase transfer learning is proposed to utilize unlabeled real data in existing industrial forms. In the framework, massive training data are generated automatically with a configurable font and character library. A proposed convolutional neural network suitable for character recognition is pre-trained with the generated training data as the source model. In the first transfer phase, the source model is adapted to the target model with real samples of a specific writing style in an unsupervised manner. In the second supervised transfer phase, the target model is further optimized with a few labels available. The recognition application is described based on the target model. The effectiveness of the proposed enhanced two-phase model transfer method is validated on the public dataset as the target domain data through systematic experiments. Furthermore, a comparison with related works is provided to show the transferability and efficiency of the proposed framework.

对于跨国工业系统中的多语言互操作，字符识别是一个研究问题，可以在很大程度上促进大量表格的自动信息集成，但尚未得到很好的解决。使用深度卷积神经网络的字符识别依赖于大规模的训练数据收集和劳动密集型的标注工作来训练有效模型。合成数据生成和数据增强是补偿标记训练数据稀缺性的典型手段。然而，合成数据和真实数据之间的领域转移不可避免地导致识别准确性不令人满意，带来了重大挑战。为了缓解这样的问题，提出了一种具有增强的两阶段转移学习的识别系统，以利用现有工业形式中未标记的真实数据。在框架中，自动生成海量训练数据，并带有可配置的字体和字符库。使用生成的训练数据作为源模型对提出的适用于字符识别的卷积神经网络进行预训练。在第一个传输阶段，源模型以无监督的方式适应具有特定写作风格的真实样本的目标模型。在第二个监督转移阶段，目标模型进一步优化，提供一些可用的标签。基于目标模型描述识别应用程序。通过系统实验，在公共数据集作为目标域数据上验证了所提出的增强型两阶段模型转移方法的有效性。此外，还提供了与相关工作的比较，以显示所提出框架的可转移性和效率。