Search in collections of digitised historical documents is hindered by a two-prong problem, orthographic variety and optical character recognition (OCR) mistakes. We present a new search engine for historical documents, DuoSearch, which uses ElasticSearch and machine learning methods based on deep neural networks to offer a solution to this problem. It was tested on a collection of historical newspapers in Bulgarian from the mid-19th to the mid-20th century. The system provides an interactive and intuitive interface for the end-users allowing them to enter search terms in modern Bulgarian and search across historical spellings. This is the first solution facilitating the use of digitised historical documents in Bulgarian.

翻译：数字化历史文献集的检索面临两大障碍：正字法变异与光学字符识别（OCR）错误。我们提出了一种面向历史文献的新型搜索引擎DuoSearch，该引擎结合ElasticSearch与基于深度神经网络的机器学习方法，针对上述问题提供了解决方案。系统以19世纪中叶至20世纪中叶的保加利亚语历史报纸语料库为测试对象，通过交互式直观界面允许终端用户输入现代保加利亚语搜索词，并跨历史拼写变体进行检索。这是首个促进保加利亚语数字化历史文献应用的解决方案。