Official PyTorch implementation of LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (ACL 2022)
-
Updated
Oct 31, 2022 - Python
Official PyTorch implementation of LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (ACL 2022)
This small module connects Label Studio with Fonduer by creating a fonduer labeling function for gold labels from a label studio export. Documentation: https://irgroup.github.io/labelstudio-to-fonduer/
Implementation of the paper: Going Full-TILT Boogie on Document Understanding with Text-Image-Layout Transformer.
Doc2Graph transforms documents into graphs and exploit a GNN to solve several tasks.
A curated list of resources for Document Understanding (DU) topic
(WIP) ✨ A comprehensive resource for understanding the world of software used in the Document Understanding field. 🧙✨
QuickCapture Mobile Scanning SDK Specially designed for native IOS
This project tackles a real-world challenge of automating client document processing, with a focus on enhancing document classification, error detection, data extraction, and validation.
Object Detection Model for Scanned Documents
A hands-on CLI tool sample showcasing the integration of Dart with Google Cloud's DocumentAI.
Checkbox Detection Model for Scanned Documents
TAT-DQA: Towards Complex Document Understanding By Discrete Reasoning
QuickCapture Mobile Scanning SDK Specially designed for native ANDROID from Extrieve
Official release of RFUND introduced in the paper "PEneo: Unifying Line Extraction, Line Grouping, and Entity Linking for End-to-end Document Pair Extraction" (arXiv:2401.03472).
Datasets and Evaluation Scripts for CompHRDoc
Minimal sharded dataset loaders, decoders, and utils for multi-modal document, image, and text datasets.
Run optical character recognition with PyTesseract from the FiftyOne App!
A Curated List of Awesome Table Structure Recognition (TSR) Research. Including models, papers, datasets and codes. Continuously updating.
A collection of original, innovative ideas and algorithms towards Advanced Literate Machinery. This project is maintained by the OCR Team in the Language Technology Lab, Tongyi Lab, Alibaba Group.
Add a description, image, and links to the document-understanding topic page so that developers can more easily learn about it.
To associate your repository with the document-understanding topic, visit your repo's landing page and select "manage topics."