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 -