Document Domain Randomization for Deep Learning Document Layout Extraction - Inria - Institut national de recherche en sciences et technologies du numérique Access content directly
Conference Papers Year : 2021

Document Domain Randomization for Deep Learning Document Layout Extraction


We present document domain randomization (DDR), the first successful transfer of CNNs trained only on graphically rendered pseudo-paper pages to real-world document segmentation. DDR renders pseudo-document pages by modeling randomized textual and non-textual contents of interest, with userdefined layout and font styles to support joint learning of fine-grained classes. We demonstrate competitive results using our DDR approach to extract nine document classes from the benchmark CS-150 and papers published in two domains, namely annual meetings of Association for Computational Linguistics (ACL) and IEEE Visualization (VIS). We compare DDR to conditions of style mismatch, fewer or more noisy samples that are more easily obtained in the real world. We show that high-fidelity semantic information is not necessary to label semantic classes but style mismatch between train and test can lower model accuracy. Using smaller training samples had a slightly detrimental effect. Finally, network models still achieved high test accuracy when correct labels are diluted towards confusing labels; this behavior hold across several classes.
Fichier principal
Vignette du fichier
docrandomization.pdf (12.84 Mo) Télécharger le fichier
Vignette du fichier
Ling_2021_DDR.jpg (1006.88 Ko) Télécharger le fichier
Origin : Files produced by the author(s)
Format : Figure, Image

Dates and versions

hal-03336444 , version 1 (07-09-2021)



Meng Ling, Jian Chen, Torsten Möller, Petra Isenberg, Tobias Isenberg, et al.. Document Domain Randomization for Deep Learning Document Layout Extraction. Proceedings of the 16th International Conference on Document Analysis and Recognition (ICDAR, September 5--10, Lausanne, Switzerland), Sep 2021, Lausanne, Switzerland. pp.497-513, ⟨10.1007/978-3-030-86549-8_32⟩. ⟨hal-03336444⟩
57 View
73 Download



Gmail Facebook Twitter LinkedIn More