Results 21 to 30 of about 39,490,265 (332)
LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis [PDF]
IEEE International Conference on Document Analysis and Recognition, 2021 Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks. Ideally, research outcomes could be easily deployed in production and extended for further investigation.
Shannon Zejiang Shen +5 more
semanticscholar +1 more source
Historical document image analysis using controlled data for pre-training
International Journal on Document Analysis and Recognition, 2023 Using neural networks for semantic labeling has become a dominant technique for layout analysis of historical document images. However, to train or fine-tune appropriate models, large labeled datasets are needed.
Najoua Rahal, Lars Vögtlin, R. Ingold
semanticscholar +1 more source
DiT: Self-supervised Pre-training for Document Image Transformer [PDF]
ACM Multimedia, 2022 Image Transformer has recently achieved significant progress for natural image understanding, either using supervised (ViT, DeiT, etc.) or self-supervised (BEiT, MAE, etc.) pre-training techniques.
Junlong Li +5 more
semanticscholar +1 more source
A Survey on Document Image Analysis
International Journal for Research in Applied Science and Engineering Technology, 2018 Dr. S. Vijayarani
openaire +2 more sources
Cross-Depicted Historical Motif Categorization and Retrieval with Deep Learning
Journal of Imaging, 2020 In this paper, we tackle the problem of categorizing and identifying cross-depicted historical motifs using recent deep learning techniques, with aim of developing a content-based image retrieval system. As cross-depiction, we understand the problem that
Vinaychandran Pondenkandath +4 more
doaj +1 more source
Data in Brief, 2022 This article presents a dataset of hyperspectral images of handwriting samples collected from 54 individuals. The purpose of the presented dataset is to further explore the use of hyperspectral imaging in document image analysis and to benchmark the ...
Ammad Ul Islam +4 more
doaj +1 more source
A Comprehensive Study of ImageNet Pre-Training for Historical Document Image Analysis [PDF]
IEEE International Conference on Document Analysis and Recognition, 2019 Automatic analysis of scanned historical documents comprises a wide range of image analysis tasks, which are often challenging for machine learning due to a lack of human-annotated learning samples.
Linda Studer +7 more
semanticscholar +1 more source
DocSegTr: An Instance-Level End-to-End Document Image Segmentation Transformer [PDF]
arXiv.org, 2022 Understanding documents with rich layouts is an essential step towards information extraction. Business intelligence processes often require the extraction of useful semantic content from documents at a large scale for subsequent decision-making tasks ...
Sanket Biswas +3 more
semanticscholar +1 more source
Fast Binarization of Unevenly Illuminated Document Images Based on Background Estimation for Optical Character Recognition Purposes [PDF]
Journal of Universal Computer Science, 2019 One of the key operations during the image preprocessing step in Optical Character Recognition (OCR) algorithms is image binarization. Although for uniformly illuminated images, obtained typically by atbed scanners, the use of a single global threshold ...
Hubert Michalak, Krzysztof Okarma
doaj +3 more sources
Journal of Imaging, 2021 Table detection is a preliminary step in extracting reliable information from tables in scanned document images. We present CasTabDetectoRS, a novel end-to-end trainable table detection framework that operates on Cascade Mask R-CNN, including Recursive ...
Khurram Azeem Hashmi +4 more
doaj +1 more source