# End-to-End Neural Optical Music Recognition of Monophonic Scores
## 5 Experiments
### Commercial OMR Tool Analysis  
There exist many commercial OMR tools with which a comparison can be carried out. For this analysis, we choose one of the best commercial tools available: Photoscore Ultimate (www.neuratron.com/photoscore.htm), version 8.8.2. It is publicized as having \"_22 years_\" of recognition experience and accuracies \"_over 99.5%_\". However, since Photoscore is conceived for its interactive use, it does not allow for batch processing. Therefore, the comparison of our approach with this tool will be conducted qualitatively by studying the behavior in some selected examples.  
Images supplied to Photoscore have been converted into TIFF format with 8-bit depth, following the requirements of the tool. Below, we will show some snapshots of the output of the application, where the white spaces around the staves have been manually trimmed for saving space. Note that the output of Photoscore is not a list of recognized music glyphs, but the musical content itself. Thus, in its output, the tool superimposes some musical symbols that, despite being not present at the input image, indicate the musical context that the tool has inferred. Namely, these visual hints are time signatures drawn in gray, tempo marks in red, and musical figures preceded by a plus or a minus sign showing the difference between the sum of the figures actually present in the measure and the expected duration given the current time signature. From our side, we provide the sequence of music symbols predicted by our model so as to analyze the difference between both outputs.  
For illustrating the qualitative comparison, we manually looked for samples that cover all possible scenarios. For instance, Figure 8b shows an incipit in which both systems fail, yet for different reasons: Photoscore misses the last _appoggiatura_, whereas our method predicts an ending _barline_ that is not in the image (this error was already discussed in Section 5.2.1).  
For the sample depicted in Figure 9, the output of Photoscore is exact, but our system misses the first _tie_. The opposite situation is given by the sample of Figure 10, where Photoscore makes many mistakes. It is not able to correctly recognize the _tie_ between the last two measures. In addition, the _acciacatura_ are wrongly detected: they are identified as either _appoggiatura_ or totally different  
Figure 8: Incipit RISM ID no. 000100369. _Sonatas_. [PERSON]. Both systems fail at recognizing the content within the image. (**a**) Photoscore output: The last _appoggiatura_ is not detected; (**b**) Output of our system: It generates an ending barline not present in the original score.  
Finally, the sample of Figure 11 is perfectly recognized by both Photoscore and our system.  
The examples given above show some of the most representative errors found. During the search of these examples, however, it was difficult to find samples where both system failed. In turn, it was easy to find examples where Photoscore failed and our system did not. Obviously, we do not mean that our system behaves better than Photoscore, but rather that our approach is competitive with respect to it.