CAMIO: A Corpus for OCR in Multiple Languages

Michael Arrigo, Stephanie Strassel, Nolan King, Thao Tran, Lisa Mason


Abstract
CAMIO (Corpus of Annotated Multilingual Images for OCR) is a new corpus created by Linguistic Data Consortium to serve as a resource to support the development and evaluation of optical character recognition (OCR) and related technologies for 35 languages across 24 unique scripts. The corpus comprises nearly 70,000 images of machine printed text, covering a wide variety of topics and styles, document domains, attributes and scanning/capture artifacts. Most images have been exhaustively annotated for text localization, resulting in over 2.3M line-level bounding boxes. For 13 of the 35 languages, 1250 images/language have been further annotated with orthographic transcriptions of each line plus specification of reading order, yielding over 2.4M tokens of transcribed text. The resulting annotations are represented in a comprehensive XML output format defined for this corpus. The paper discusses corpus design and implementation, challenges encountered, baseline performance results obtained on the corpus for text localization and OCR decoding, and plans for corpus publication.
Anthology ID:
2022.lrec-1.129
Volume:
Proceedings of the Thirteenth Language Resources and Evaluation Conference
Month:
June
Year:
2022
Address:
Marseille, France
Editors:
Nicoletta Calzolari, Frédéric Béchet, Philippe Blache, Khalid Choukri, Christopher Cieri, Thierry Declerck, Sara Goggi, Hitoshi Isahara, Bente Maegaard, Joseph Mariani, Hélène Mazo, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association
Note:
Pages:
1209–1216
Language:
URL:
https://aclanthology.org/2022.lrec-1.129
DOI:
Bibkey:
Cite (ACL):
Michael Arrigo, Stephanie Strassel, Nolan King, Thao Tran, and Lisa Mason. 2022. CAMIO: A Corpus for OCR in Multiple Languages. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 1209–1216, Marseille, France. European Language Resources Association.
Cite (Informal):
CAMIO: A Corpus for OCR in Multiple Languages (Arrigo et al., LREC 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.lrec-1.129.pdf