Research paper and code on information extraction from image/pdf
Document layout analysis is the process of identifying and categorizing the regions of interest in the scanned image of a text document. A reading system requires the segmentation of text zones from non-textual ones and the arrangement in their correct reading order.Detection and labeling of the different zones (or blocks) as text body, illustrations, math symbols, and tables embedded in a document is called geometric layout analysis.(https://en.wikipedia.org/wiki/Document_layout_analysis)
- PubLayNet
- PRIMA
- HJDatasets (Historical Japanese Documents with Complex Layouts)
- Newspaper Navigator
- TableBank
- DocBank
- German-Brazilian Newspapers (GBN) Dataset
- TableBank
- DocBank
- FUNSD
- RVL-CDIP
- SROIE
- Document Visual Question Answering
- HTR Dataset ICFHR 2016
- Tobacco3482
- PubLay
- Tobacco800 Complex Document Image Database and Groundtruth
- NIST Forms
- Layoutlm
- Graph Convolution on Structured Documents
- Graph Matric
- Feature Extraction from Graph
- Extract data from Invoice
- CascadeTabNet
- Tabulo
- PubLayNet
- InvoiceNet Extract text from invoice
- Cutie
- LayoutLM
- PICK
- Deep Convolutional Nets for Document Image Classification and Retrieval
- Table Detection in Invoice Documents by Graph Neural Network
- Graph Convolution on Structured Documents
- PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks
- Evaluation of Deep Convolutional Nets for Document Image Classification and Retrieval
- Few-Shot Learning with Graph Neural Networks
- MMDetection: Open MMLab Detection Toolbox and Benchmark
- Efficient, Lexicon-Free OCR using Deep Learning
- An Overview of the Tesseract OCR Engine
- Semi-Supervised Classification with Graph Convolutional Networks
- An Invoice Reading System Using a Graph Convolutional Network
- Spatial Dependency Parsing for Semi-Structured Document Information Extraction