Read and extract text and other content from PDFs in C# (port of PDFBox)

A curated list of resources for Document Understanding (DU) topic

This repository provides train＆test code, dataset, det.&rec. annotation, evaluation script, annotation tool, and ranking.

Code for the paper "PICK: Processing Key Information Extraction from Documents using Improved Graph Learning-Convolutional Networks" (ICPR 2020)

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, Alibaba DAMO Academy.

Official PyTorch implementation of LiLT: A Simple yet Effective Language-Independent Layout Transformer for Structured Document Understanding (ACL 2022)

Pandora is an analysis framework to discover if a file is suspicious and conveniently show the results

RObust document image BINarization

AssemblyLine 4: File triage and malware analysis

Local adaptive image binarization

Document Visual Question Answering

(ICFHR 2020 oral) Code for "docExtractor: An off-the-shelf historical document element extraction" paper

Post-process Amazon Textract results with Hugging Face transformer models for document understanding

An unofficial PyTorch implementation of "Lin et al. ViBERTgrid: A Jointly Trained Multi-Modal 2D Document Representation for Key Information Extraction from Documents. ICDAR, 2021"

Trained Detectron2 object detection models for document layout analysis based on PubLayNet dataset

Effortlessly extract information from unstructured data with this library, utilizing advanced AI techniques. Compose AI in customizable pipelines and diverse sources for your projects.

Improving Document Binarization via Adversarial Noise-Texture Augmentation (ICIP 2019)

Code for ICPR2022 paper: "Graph Neural Networks and Representation Embedding for table extraction in PDF Documents"

DL models that take a document image file as input, locate the position of paragraphs, lines, images, etc. with their labels and confidence scores.

Powerful web application that combines Streamlit, LangChain, and Pinecone to simplify document analysis. Powered by OpenAI's GPT-3, RAG enables dynamic, interactive document conversations, making it ideal for efficient document retrieval and summarization.