一个用于识别历史和手写乐谱的数据库
A Dataset for the Recognition of Historical and Handwritten Music Scores in Western Notation
AI总结：
本文提出了一个包含1309页历史乐谱的数据库，用于训练光学音乐识别系统，该数据库提供了音乐XML转录和符号注释，是目前最大的手写音乐数据集，适用于训练和评估端到端和基于目标检测的OMR系统。
摘要：
大量的音乐遗产已由记忆机构（图书馆、博物馆和档案馆）数字化。然而，尽管深度学习的进步，光学音乐识别（OMR）领域在使音乐可机读方面仍然面临困难，主要是因为缺乏可用于真实条件训练的数据库。MusiCorpus数据集旨在通过提供1,309页的历史乐谱（主要是手写乐谱）以及音乐XML转录和符号注释来解决这一问题。它是目前最大的手写音乐数据集，也是首个包含来自记忆机构的真实且具有代表性的音乐文档集合的数据集，适用于训练和评估端到端和基于目标检测的OMR系统，并比较其性能。
英文摘要：
A large amount of musical heritage has been digitised by memory institutions: libraries, museums, and archives. Nevertheless, the field of Optical Music Recognition (OMR) has struggled with making this music machine-readable, despite advances in deep learning, mostly because no datasets for training systems in realistic conditions were available. The MusiCorpus dataset aims to remedy this situation by providing 1,309 pages of historical sheet music, primarily handwritten, with MusicXML transcriptions and symbol annotations. It is the largest dataset of handwritten music to date and the first dataset containing a realistic and representative sample of musical document collections from memory institutions, suitable for training and evaluating both end-to-end and object detection-based OMR systems and comparing their performance.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
