TY  - Type of reference 
TI  - Data correction for transcription in crowdsourcing. A feedback from RECITAL platform. 
AU  - Benjamin HERVY 
AU  - Pierre PÉTILLON 
AU  - Hugo PIGEON 
AU  - Guillaume RASCHIA 
AB  - Crowdsourcing have been widely deployed to cover some challenges in digital humanities, like in the transcription of old handwritten documents. Such approach is especially useful to tackle existing limits in automatic handwriting recognition techniques. Crowdsourcing allows workers to help experts in extraction and classification of information, when the workload is daunting. Yet, it yields
some specific challenges related to the quality of produced data. In this paper, we discuss data quality in a research project called CIRESFI which aims at transcribing Italian Comedy financial archives through the RECITAL web platform.We finally propose some leads to tackle these issues. 
DO  - 10.21494/ISTE.OP.2019.0348 
JF  - Information Retrieval, Document and Semantic Web 
KW  - Citizen sciences,  Digital Humanities,  Old handwritten documents,  Transcription,  Data quality,  Italian Comedy, Sciences participatives,  Humanités numériques,  Manuscrits anciens,  Transcription,  Qualité des données,  Comédie Italienne,  
L1  - https://openscience.fr/IMG/pdf/iste_ridows18v2n1_6.pdf 
LA  - en 
PB  - ISTE OpenScience 
DA  - 2019/03/18 
SN  - 2516-3280 
TT  - Correction des données : retour d’expérience sur la plate-forme RECITAL de transcription participative 
UR  - https://openscience.fr/Data-correction-for-transcription-in-crowdsourcing-A-feedback-from-RECITAL 
IS  - Issue 1 
VL  - 2 
ER  -