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aashrafh

Mozart

An optical music recognition (OMR) system. Converts sheet music to a machine-readable version.

ApplicationsObject detectionJupyter Notebook
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创建于 2020-12-14 · 更新于 2026-10-13 · 今日第 14649 名
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aashrafhaashrafh22AhmedMGZ7

aashrafh/Mozart 排名第 14,649，有 714 个星标。

同类别比较

aashrafh/Mozart 排名第 14,649，此处有 8 个同类条目；hmmnxkl/LLM-Based-Intelligent-Public-Opinion-Analytics-Assistant 排名第 14,652。

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关键参数与规格
关键参数与规格
参数值
语言Jupyter Notebook
类别Applications
子类别Object detection
关键词Music, OCR
星标数714
分叉数106
贡献者6
创建日期2020-12-14
更新日期2026-10-13
1 天星标数0
7 天星标数2
排名14,649
上次排名14,501
排名变化-148
7 天星标排名8,586
上次 7 天星标排名8,655
7 天星标排名变化69
总星标排名14,659
上次总星标排名14,636
总星标排名变化-23
国家Egypt
README 摘录

:notes: Convert sheet music to a machine-readable version. This is called Optical Music Recognition (OMR). Its objective is to convert sheet music to a machine-readable version. We take a simplified version where we convert an image of sheet music to a textual representation that can be further processed to produce midi files or audio files like wav or mp3. Symbol Detection and Recognition 🏁 Install 1. You can use the attached notebook for quick testing and visualization. You can

setup an environment on your local machine to run the project: You can find the dataset on Google Drive. Please check the following issue for another file. 3 NumPy OpenCV scikit-learn scikit-image

常见问题

记录了多少星标和分叉？

aashrafh/Mozart 有 714 个星标和 106 个分叉。

仓库排名是多少？

aashrafh/Mozart 排名第 14,649。

记录的语言和类别是什么？

aashrafh/Mozart 有 714 个星标，语言为 Jupyter Notebook，类别为 Applications。

哪个仓库排名最接近？

aashrafh/Mozart 与 hmmnxkl/LLM-Based-Intelligent-Public-Opinion-Analytics-Assistant 比较，后者排名第 14,652，有 714 个星标。

README

:notes: Convert sheet music to a machine-readable version.

📝 Table of Contents

🧐 About

The aim of this project is to develop a sheet music reader. This is called Optical Music Recognition (OMR). Its objective is to convert sheet music to a machine-readable version. We take a simplified version where we convert an image of sheet music to a textual representation that can be further processed to produce midi files or audio files like wav or mp3.

:computer: Methodology

1. Noise Filtering and Binarization

2. Segmentation

3. Staff Line Detection and Removal

4. Construct The New Staff Lines

5. Symbol Detection and Recognition

🏁 Install

  1. You can use the attached notebook for quick testing and visualization.
  2. You can setup an environment on your local machine to run the project:
    1. Install Conda
    2. conda env create -f requirements.yml
    3. conda activate mozart
    4. python3 main.py

You can find the dataset on Google Drive.

Please check the following issue for another requirements.yml file.

⛏️ Built Using

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