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Author Pau Torras; Arnau Baro; Alicia Fornes; Lei Kang
Title Improving Handwritten Music Recognition through Language Model Integration Type Conference Article
Year 2022 Publication 4th International Workshop on Reading Music Systems (WoRMS2022) Abbreviated Journal
Volume Issue Pages 42-46
Keywords optical music recognition; historical sources; diversity; music theory; digital humanities
Abstract 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.
Address November 18, 2022
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Area Expedition Conference WoRMS
Notes DAG; 600.121; 600.162; 602.230 Approved no
Call Number Admin @ si @ TBF2022 Serial 3735
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