-------- Forwarded Message -------- Subject: [AISWorld] CFP: FinLayout Shared Task on document layout analysis in the financial domain Date: Tue, 12 Apr 2022 12:15:40 +0200 From: FinLayout SharedTask fin.layout.task@gmail.com To: fin.layout.task@gmail.com
Greetings,
We would like to invite you to submit to FinLayout, a shared task on document layout analysis in the financial domain, held in conjunction with IJCAI-ECAI-2022, Messe Wien, Vienna, Austria 23th -25th July, 2022 as part of the FinNLP-2022 workshop.
Shared Task URL: https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp-2022/shared-task-finlayo... Workshop URL: https://sites.google.com/nlg.csie.ntu.edu.tw/finnlp-2022/home Registration Form: https://docs.google.com/forms/d/1EZTTheA4rLomtLOHKU0xQnraCRc1obuk1IGR1TCqwBc...
=====Introduction===== The 1st edition of FinLayout introduces a shared task on document layout analysis in the financial domain. Visual features allow to give indications about different aspects of the structure of documents. Therefore, most approaches in document layout analysis rely on image-based methods to extract the structure of each page of the document. Deep neural networks that are developed for computer vision have been proven to be an effective method to analyze layout of document images.
=====Task Description===== In this shared task, we propose to recognize the layout of financial documents represented through 4 labels: Text, Title, Figure and List.
As training set, we propose to use PubLayNet (), a large dataset (~100GB) of document images, of which the layout is annotated with bounding boxes. The dataset contains overs 1 million PDF articles that are publicly available on PubMed Central, a free full-text archive of biomedical and life sciences journal literature at the U.S. National Institutes of Health's National Library of Medicine.
The idea will be to demonstrate that models trained on PubLayNet containing scientific articles can accurately recognize the layout on different type of documents and typically in the financial domain, which proves the effectiveness of transfer learning.
=====Reference===== Zhong, Xu and Tang, Jianbin and Yepes, Antonio Jimeno (2019), "PubLayNet: largest dataset ever for document layout analysis". International Conference on Document Analysis and Recognition (ICDAR)
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=====Registration===== To register your interest in participating in FinLayout shared task, please use the following google form: https://docs.google.com/forms/d/1EZTTheA4rLomtLOHKU0xQnraCRc1obuk1IGR1TCqwBc...
=====Prize===== A USD$1000 prize will be rewarded to the best-performing teams.
=====Important Dates===== April 12, 2022: First announcement of the shared task and beginning of registration April 20, 2022 : Release of training set & scoring scripts. May 20, 2022: Release of test set. May 26, 2022: System's outputs submission deadline. May 30, 2022: Release of results. May 30, 2022: Shared task title and abstract due June 06, 2022: Shared task paper submissions due June 17, 2022: Registration deadline. June 17, 2022: Camera-ready version of shared task paper due July 23-25, 2022: FinNLP-2022 workshop @IJCAI-ECAI-2022
=====Contact===== For any questions on the shared task, please contact us on fin.layout.task@gmail.com.
=====Shared Task Co-organizers - Fortia Financial Solutions===== Willy au Abderrahim AIT AZZI Sandra Bellato Mei Gan Juyeon KANG _______________________________________________ AISWorld mailing list AISWorld@lists.aisnet.org