# Optical Medieval Music Recognition--A Complete Pipeline for Historic Chants
## 9 Conclusions  
In this paper, we have shown that automatic transcription achieves low error rates on historical music documents. For this purpose, we have presented and evaluated our pipeline, which is integrated into OMMR4 all [35]. We implemented the pipeline using neural networks and background knowledge. Our pipeline includes the following steps: pre-processing, staff recognition, layout analysis, symbol recognition, text and syllable recognition and finally syllable note assignment. The staff recognition and layout recognition steps generate very few errors that need to be corrected during transcription. More errors occur in symbol recognition. On average, between one and seven errors occur per 100 symbols. However, the most frequent errors occur at the text level. Here, a post-processing with a chant text database was introduced in the pipeline, which improves the raw transcription results of the CNN/LSTM network drastically. It reduced the errors that need to be corrected from 24.4% to 4.7%, or to 3.8% if the model is also fine-tuned. It should also be noted that errors are often grouped together making manual correction much easier, as these are largely limited to extra words/missing words or errors due to incorrect line separation. Nevertheless, there is still room for improvement. An obvious improvement would be the optimization of the text transcription network with more training material, which could lead to an improvement in the localization of the characters and, as such, improve the line splitting and the comparison. In addition, an expansion of the chant database would make matches more likely. Using background knowledge when assigning syllables to notes would also improve this critical step. The correction of the automatic transcription takes currently about 2 min per page, while the manual transcription with an optimized tool takes about 18 min. So, the use of this pipeline accelerates the transcription process by a factor of up to 9. The pipeline was developed as part of the Corpus Monodicum (https://corpus-m onodicum.de/about, accessed on 10 August 2024) project, which includes chants of Latin medieval music, with a large proportion of mass chants, tropes, sequences, songs, and liturgical dramas. Integration into the larger system of Cantus Index is underway, which, similar to a metasearch engine for medieval chant, connects over 15 autonomous projects. The goal is to automatically transcribe parts of this collection using the presented pipeline, thereby significantly reducing the time required for transcription. The transcription has \begin{table}
\begin{tabular}{l l l l} \hline \hline
**Post-Processing** & **Experiment** & **M1** & **M2** \\ \hline \multirow{8}{*}{No} & Mixed & 36.7/35.3\% & 16.1/14.0\% \\  & Fine-tuned & 26.1/24.8\% & 12.3/10.1\% \\  & GT (Symbols) no Fine-tune & 31.9\% & 8.2\% \\  & GT (Symbols) with Fine-tune & 21.4\% & 5.2\% \\  & GT (Symbols \& Text) & 2.3\% & 0.1\% \\ \hline \multirow{8}{*}{Yes} & Mixed & 14.1/12.8\% & 10.2/8.4\% \\  & Fine-tuned & 13.1/11.2\% & 9.7/7.5\% \\ \cline{1-1}  & GT (Symbols) no Fine-tune & 8.4\% & 3.6\% \\ \cline{1-1}  & GT (Symbols) with Fine-tune & 7.2\% & 3.0\% \\ \cline{1-1}  & GT (Symbols \& Text) & 2.3\% & 0.1\% \\ \hline \hline \end{tabular}
\end{table} 
Table 8: Experiments of the syllable assignment step. Several different experiment setups were evaluated. Post-processing = yes means that the database with chant texts was used to post-correct the raw OCR transcriptions. “Fine-tuned” means that the text models were finetuned, while mixed means that the text model was not fine-tuned. The values after the “/” indicate the error rate of the syllable assignment if the symbol recognition was also fine-tuned. In the optimal case, when text and symbols have been fully corrected in advance, only 2.3% of all syllables need correction. When only using the results of the pipeline without intermediate correction, 11.2%/12.8% of all syllables need correction.