The subject of this article is the application of NLP and text-mining methods to the analysis of two large bibliographies: Polish one, based on the catalogs of the National Library in Warsaw, and the other German one, created by Deutsche Nationalbibliothek. The data in both collections are stored in MARC 21 format, allowing the selection of relevant fields that are used for further processing (basically author, title, and date). The volume of the Polish corpus (after filtering out non-relevant or incomplete items) includes 1.4 mln of records, and that of the German corpus 7.5 mln records. The time span of both bibliographies extends from 1801 to 2021. The aim of the study is to compare the gender distribution of book authors in Polish and German databases over more than two centuries. The proportions of male and female authors since 1801 were calculated automatically, and NLP methods such as document vector embedding based on deep BERT networks were used to extract topics from titles. The gender of the Polish authors was recognized based on the morphology of the first names, and that of the German authors based on a predefined list. The study found that the proportion of female authors has been steadily increasing both in Poland and in German countries (currently around 43%). However, the topics of women’s and men’s writings invariably remain different since 1801.