%0 Conference Proceedings %T Improving Handwritten Music Recognition through Language Model Integration %A Pau Torras %A Arnau Baro %A Alicia Fornes %A Lei Kang %B 4th International Workshop on Reading Music Systems (WoRMS2022) %D 2022 %F Pau Torras2022 %O DAG; 600.121; 600.162; 602.230 %O exported from refbase (http://refbase.cvc.uab.es/show.php?record=3735), last updated on Thu, 27 Apr 2023 14:55:49 +0200 %X Handwritten Music Recognition, especially in the historical domain, is an inherently challenging endeavour; paper degradation artefacts and the ambiguous nature of handwriting make recognising such scores an error-prone process, even for the current state-of-the-art Sequence to Sequence models. In this work we propose a way of reducing the production of statistically implausible output sequences by fusing a Language Model into a recognition Sequence to Sequence model. The idea is leveraging visually-conditioned and context-conditioned output distributions in order to automatically find and correct any mistakes that would otherwise break context significantly. We have found this approach to improve recognition results to 25.15 SER (%) from a previous best of 31.79 SER (%) in the literature. %K optical music recognition %K historical sources %K diversity %K music theory %K digital humanities %U http://refbase.cvc.uab.es/files/TBF2022.pdf %P 42-46