The Booknis dataset is a collection of manually annotated images featuring medieval singing representations for segmentation and object detection. These images are derived from manuscripts that date back to the period between the 12th and 15th centuries and consist of various genres, including liturgical, secular, musical, scientific, and historical works. The dataset includes five classes, namely books, altars, lecterns, sheets, and phylacteries, which serve as indicators of potential musical representation. We have meticulously annotated 341 images and 1513 annotations, which musicology experts have verified. The primary objective of Booknis is to advance research on transfer learning by providing a more challenging benchmark for few-shot object detection.