Manchu is a low-resource language that is rarely involved in text recognition technology. Because of the combination of typefaces, ordinary text recognition practice requires segmentation before recognition, which affects the recognition accuracy. In this paper, we propose a Manchu text recognition system divided into two parts: text recognition and text retrieval. First, a deep CNN model is used for text recognition, using a sliding window instead of manual segmentation. Second, text retrieval finds similarities within the image and locates the position of the recognized text in the database; this process is described in detail. We conducted comparative experiments on the FAST-NU dataset using different quantities of sample data, as well as comparisons with the latest model. The experiments revealed that the optimal results of the proposed deep CNN model reached 98.84%.

中文翻译：

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带有深度 CNN 模型的 OCR 用于基于连字脚本的语言（如满语）

满语是一种资源匮乏的语言，很少涉及文本识别技术。由于字体的组合，普通的文本识别实践需要先切分再识别，影响识别准确率。在本文中，我们提出了一个满文文本识别系统，分为文本识别和文本检索两部分。首先，深度CNN模型用于文本识别，使用滑动窗口代替手动分割。其次，文本检索寻找图像内的相似之处，定位识别出的文本在数据库中的位置；详细描述了这个过程。我们使用不同数量的样本数据对 FAST-NU 数据集进行了对比实验，并与最新模型进行了比较。