Journal ArticleDOI
Segmentation for document layout analysis: not dead yet
Logan Markewich,Hao Zhang,Yubin Xing,Navid Lambert-Shirzad,Zhexin Jiang,Roy Ka-Wei Lee,Zhi Li,Seok-Bum Ko +7 more
- Vol. 25, Iss: 2, pp 1-11
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The article was published on 2022-01-13. It has received 5 citations till now. The article focuses on the topics: Computer science & Segmentation.read more
Citations
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Proceedings ArticleDOI
DocLayNet: A Large Human-Annotated Dataset for Document-Layout Segmentation
TL;DR: DocLayNet is presented, a new, publicly available, document-layout annotation dataset in COCO format that contains 80863 manually annotated pages from diverse data sources to represent a wide variability in layouts and compares models trained on PubLayNet, DocBank and DocLay net, showing that layout predictions of the DocLayNet-trained models are more robust and thus the preferred choice for general-purpose document- layout analysis.
Proceedings ArticleDOI
A Mining approach for Automatic Processing of Regulatory Document
TL;DR: In this paper , a proposal for automatically detecting the structure of regulatory documents, tagging management and text segmentation in units able to be processed as entries in a database is presented, based on simple processing, clustering and a rule-based automatic algorithm.
Book ChapterDOI
An End-to-End Framework for Evaluating Explainable Deep Models: Application to Historical Document Image Segmentation
Proceedings ArticleDOI
A Methodological Study of Document Layout Analysis
TL;DR: In this article , the authors summarized the traditional learning algorithms based on tour smoothing and segmentation projection, deep learning algorithms using recurrent convolutional neural networks and twin networks, and algorithms combining traditional learning and deep learning proposed in recent years.
References
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Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings ArticleDOI
ImageNet: A large-scale hierarchical image database
TL;DR: A new database called “ImageNet” is introduced, a large-scale ontology of images built upon the backbone of the WordNet structure, much larger in scale and diversity and much more accurate than the current image datasets.
Book ChapterDOI
U-Net: Convolutional Networks for Biomedical Image Segmentation
TL;DR: Neber et al. as discussed by the authors proposed a network and training strategy that relies on the strong use of data augmentation to use the available annotated samples more efficiently, which can be trained end-to-end from very few images and outperforms the prior best method (a sliding-window convolutional network) on the ISBI challenge for segmentation of neuronal structures in electron microscopic stacks.
Posted Content
Deep Residual Learning for Image Recognition
TL;DR: This work presents a residual learning framework to ease the training of networks that are substantially deeper than those used previously, and provides comprehensive empirical evidence showing that these residual networks are easier to optimize, and can gain accuracy from considerably increased depth.
Journal ArticleDOI
Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks
TL;DR: This work introduces a Region Proposal Network (RPN) that shares full-image convolutional features with the detection network, thus enabling nearly cost-free region proposals and further merge RPN and Fast R-CNN into a single network by sharing their convolutionAL features.
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