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I was looking around, a promising approach was this:

https://github.com/mpralat/notesRecognizer

the problem is:

it doesn't seem good enough. One should be able to read musical notes with lower quality images. You can see in her: "bad" images folder just tiny variations of lightning can already cause her problems with her high res images.

others are here, they all use high res sharp images:

https://github.com/suyalcinkaya/music-note-recognition

https://github.com/suyalcinkaya/music-note-recognition/blob/master/input_images/im2s.JPG?raw=true

https://github.com/nikolalsvk/note-play

https://github.com/nikolalsvk/note-play/blob/master/images/notes-1.png?raw=true

now this is unsatisfying if you want to snap a photo of some tunes, and want them to be recognized.

so what could one do to achieve a good solution?

I was thinking about treating the musical notes just like written letters. The computer can easily learn written characters with the arabic symbols. I wonder though, how easy would it be for a none arabic? e.g. in chinese or japanese, several characters combine into one. The same applies to music notes, they can be connected and form something slightly different through that e.g.

enter image description here

or:

enter image description here

in contrast to just simple notes like:

enter image description here

what would be a good approach to try ones luck, with interpreting those symbols successfully, even for slightly low res images or bit blurry deformed images. I'm not saying to read out a symfony out of a thumbnail. But less than optimal captures.

any subjective ideas or comments are more than welcome

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  • $\begingroup$ Its possible if you can cut the images to chunks of musical notes. as they are evenly spaced and within 5 lines boundary. problem to tackle here is detecting rotation. would be first step to doing so. and pass through identifier to guess what note is it will be a better way of going forward. $\endgroup$ – Devidas Mar 25 at 8:26
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refer to MNIST on kagle to train model to recognize given set of notes in your case.

your example

if you start training model will understand pattern depending on bits in your image.

issue comes with blurry images when last 2 sets of pics with minute change. will be difficult task for computer to distinguish.

there is one sure shot method which works almost every time but is resource intensive and needs plethora of data to train. is DNN.

this is my take on your question.

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