Hi Andrei

I'm not sure what you exactly meant by "well-formed" pdf. In fact, we
already have some deskewing function in MuseScore's lightweight omr, so
we're able to handle some moderate distortion of the page. We also take
scanned scores as default input; the test data was acquired from the online
score repository IMSLP (see some here:
https://github.com/liang-chen/MuseScore/tree/omr_dev_clean/omr/data). But if
we can enhance or restore the low-quality pdf's in an efficient way, I
believe that'll benefit the later recognition process.

Regarding OCR, the previous version developed by Werner Schweer had already
used tesseract. We might need to revisit this part and enable it for the
current program. If you're more interested in the current status, just take
a look at this folder:
https://github.com/musescore/MuseScore/tree/master/omr.

I don't know in detail how Audiveris works, but I believe the future
development of OMR should depend more on the recent breakthroughs of
computer vision (graphical models, deep learning etc.) rather than
heuristic-based approach (ad hoc segmentation, pixel projections etc).
Traditional OMR used a lot of heuristics and the performance was quite
unreliable. 

Liang





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