Smashcima

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A10513440" target="_blank" >RIV/00216208:11320/25:10513440 - isvavai.cz</a>

Výsledek na webu

<a href="http://hdl.handle.net/11234/1-5835" target="_blank" >http://hdl.handle.net/11234/1-5835</a>

DOI - Digital Object Identifier

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Jazyk výsledku

angličtina

Název v původním jazyce

Smashcima

Popis výsledku v původním jazyce

Smashcima is a library and framework for synthesizing images containing handwritten music for creating synthetic training data for OMR models. It is primarily intended to be used as part of optical music recognition workflows, esp. with domain adaptation in mind. The target user is therefore a machine-learning, document processing, library sciences, or computational musicology researcher with minimal skills in python programming. Smashcima brings a unique new capability for optical music recognition (OMR): synthesizing a near-realistic image of handwritten sheet music from just a MusicXML file. As opposed to notation editors, which work with a fixed set of fonts and a set of layout rules, it can adapt handwriting styles from existing OMR datasets to arbitrary music (beyond the music encoded in existing OMR datasets), and randomize layout to simulate the imprecisions of handwriting, while guaranteeing the semantic correctness of the output rendering. Crucially, the rendered image is provided also with

Název v anglickém jazyce

Smashcima

Popis výsledku anglicky

Smashcima is a library and framework for synthesizing images containing handwritten music for creating synthetic training data for OMR models. It is primarily intended to be used as part of optical music recognition workflows, esp. with domain adaptation in mind. The target user is therefore a machine-learning, document processing, library sciences, or computational musicology researcher with minimal skills in python programming. Smashcima brings a unique new capability for optical music recognition (OMR): synthesizing a near-realistic image of handwritten sheet music from just a MusicXML file. As opposed to notation editors, which work with a fixed set of fonts and a set of layout rules, it can adapt handwriting styles from existing OMR datasets to arbitrary music (beyond the music encoded in existing OMR datasets), and randomize layout to simulate the imprecisions of handwriting, while guaranteeing the semantic correctness of the output rendering. Crucially, the rendered image is provided also with

Druh

R - Software

CEP obor

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OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

<a href="/cs/project/DH23P03OVV008" target="_blank" >DH23P03OVV008: OmniOMR – rozpoznávání hudebního záznamu pomocí strojového učení pro digitální knihovny</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Interní identifikační kód produktu

[http://hdl.handle.net/11234/1-5

Technické parametry

Výsledek volně dostupný na adrese http://hdl.handle.net/11234/1-5835.

Ekonomické parametry

1000000

IČO vlastníka výsledku

00216208

Název vlastníka

Univerzita Karlova