Search Results for author: Melissa Dell

Found 4 papers, 3 papers with code

LayoutParser: A Unified Toolkit for Deep Learning Based Document Image Analysis

6 code implementations29 Mar 2021 Zejiang Shen, Ruochen Zhang, Melissa Dell, Benjamin Charles Germain Lee, Jacob Carlson, Weining Li

Recent advances in document image analysis (DIA) have been primarily driven by the application of neural networks.

OLALA: Object-Level Active Learning for Efficient Document Layout Annotation

1 code implementation5 Oct 2020 Zejiang Shen, Jian Zhao, Melissa Dell, YaoLiang Yu, Weining Li

Document images often have intricate layout structures, with numerous content regions (e. g. texts, figures, tables) densely arranged on each page.

Active Learning Object Detection

A Large Dataset of Historical Japanese Documents with Complex Layouts

3 code implementations18 Apr 2020 Zejiang Shen, Kaixuan Zhang, Melissa Dell

Deep learning-based approaches for automatic document layout analysis and content extraction have the potential to unlock rich information trapped in historical documents on a large scale.

Document Layout Analysis

Information Extraction from Text Regions with Complex Tabular Structure

no code implementations NeurIPS Workshop Document_Intelligen 2019 Kaixuan Zhang, Zejiang Shen, Jie zhou, Melissa Dell

Recent innovations have improved layout analysis of document images, significantly improving our ability to identify text and non-text regions.

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