Automation-Driven Dataset Preparation for Continuous Czech Sign Language Recognition

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3A78XTM3FI" target="_blank" >RIV/00216208:11320/25:78XTM3FI - isvavai.cz</a>

Výsledek na webu

<a href="https://ieeexplore.ieee.org/document/10789742/" target="_blank" >https://ieeexplore.ieee.org/document/10789742/</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1109/ME61309.2024.10789742" target="_blank" >10.1109/ME61309.2024.10789742</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Automation-Driven Dataset Preparation for Continuous Czech Sign Language Recognition

Popis výsledku v původním jazyce

This paper presents an automation-driven solution for preparing a continuous Czech Sign Language dataset, addressing the lack of resources in this area. Manual processing of daily sign language news recordings would be extremely time-consuming, as the videos vary in quality, use different overlays, and have no captions. To streamline this process, we use the Structural Similarity Index Measure (SSIM) to compare key frames and extract relevant parts of the recording, such as weather forecast segments. Automatic speech recognition (ASR) then processes the accompanying audio and generates textual transcriptions of the spoken content. The outcome is the highly automated preparation pipeline and the dataset containing 4699 annotated videos of weather forecast news in Czech Sign Language providing a foundation for future research in sign language recognition.

Název v anglickém jazyce

Automation-Driven Dataset Preparation for Continuous Czech Sign Language Recognition

Popis výsledku anglicky

This paper presents an automation-driven solution for preparing a continuous Czech Sign Language dataset, addressing the lack of resources in this area. Manual processing of daily sign language news recordings would be extremely time-consuming, as the videos vary in quality, use different overlays, and have no captions. To streamline this process, we use the Structural Similarity Index Measure (SSIM) to compare key frames and extract relevant parts of the recording, such as weather forecast segments. Automatic speech recognition (ASR) then processes the accompanying audio and generates textual transcriptions of the spoken content. The outcome is the highly automated preparation pipeline and the dataset containing 4699 annotated videos of weather forecast news in Czech Sign Language providing a foundation for future research in sign language recognition.

Druh

D - Stať ve sborníku

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

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

2024 21st International Conference on Mechatronics - Mechatronika (ME)

ISBN

9798350394900

ISSN

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e-ISSN

—

Počet stran výsledku

5

Strana od-do

1-5

Název nakladatele

IEEE

Místo vydání

—

Místo konání akce

Brno, Czech Republic

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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