The paper discusses an approach to decipher large collections of handwritten index cards of historical dictionaries. Our study provides a working solution that reads the cards, and links their lemmas to a searchable list of dictionary entries, for a large historical dictionary entitled the Dictionary of the 17th- and 18th-century Polish, which comprizes 2.8 million index cards. We apply a tailored handwritten text recognition (HTR) solution that involves (1) an optimized detection model; (2) a recognition model to decipher the handwritten content, designed as a spatial transformer network (STN) followed by convolutional neural network (RCNN) with a connectionist temporal classification layer (CTC), trained using a synthetic set of 500,000 generated Polish words of different length; (3) a post-processing step using constrained Word Beam Search (WBC): the predictions were matched against a list of dictionary entries known in advance. Our model achieved the accuracy of 0.881 on the word level, which outperforms the base RCNN model. Within this study we produced a set of 20,000 manually annotated index cards that can be used for future benchmarks and transfer learning HTR applications.

翻译：本文探讨了一种解读历史词典手写索引卡片大规模收藏的方法。我们的研究提供了一套可行的解决方案，能够读取这些卡片并将其词目链接到可搜索的词条列表中，针对名为《17-18世纪波兰语词典》（包含280万张索引卡片）的大型历史词典。我们采用了一种定制化的手写文本识别（HTR）解决方案，包括：（1）优化的检测模型；（2）用于解读手写内容的识别模型，设计为空间变换网络（STN）后接卷积神经网络（RCNN），并搭配连接主义时序分类层（CTC），使用50万个不同长度的合成波兰语词汇进行训练；（3）后处理步骤采用约束词汇束搜索（WBC）：将预测结果与事先已知的词条列表进行匹配。我们的模型在单词级别上达到了0.881的准确率，优于基础RCNN模型。在本研究中，我们制作了20000张人工标注的索引卡片，可用于未来的基准测试和迁移学习HTR应用。