RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding.

Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images). This is also the official repository for the PubTables-1M dataset and GriTS evaluation metric.

This repository contains the code and implementation details of the CascadeTabNet paper "CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents"

Table structure recognition dataset of the paper: Complicated Table Structure Recognition

Extracting Tables from Document Images using a Multi-stage Pipeline for Table Detection and Table Structure Recognition:

Algorithms, papers, datasets, performance comparisons for Document AI. Continuously updating.

CVPR 2022: Table Structure Recognition

Google Colab Demo of CascadeTabNet: An approach for end to end table detection and structure recognition from image-based documents

A Curated List of Awesome Table Structure Recognition (TSR) Research. Including models, papers, datasets and codes. Continuously updating.

High-Performance Transformers for Table Structure Recognition Need Early Convolutions

Table detection and table structure recognition using Yolov5

智能文本自动处理工具（Intelligent text automatic processing tool）。AutoText的功能主要有文本纠错，图片ocr、版面检测以及表格结构识别等。The main functions of this project include text error correction, ocr, layout-detection and table structure recognition.

Official PyTorch implementation of PyramidTabNet: Transformer-based Table Recognition in Image-based Documents

GloSAT Historical Measurement Table Dataset

利用Swin-Unet(Swin Transformer Unet)实现对文档图片里表格结构的识别，Swin-unet (Swin Transformer Unet) is used to identify the document table structure

VHAC 2023 - OCR - Top 1 of track Table structure recognition

Add the Grid Search functionality to search for optimal hyperparameters while fine-tuning the model. Table Transformer (TATR) is a deep learning model for extracting tables from unstructured documents (PDFs and images).

PDF Table Extractor is an innovative Python project designed to tackle the challenge of extracting tables from scanned PDF documents. Leveraging advanced optical character recognition (OCR) and image processing techniques.

Table Structure Recognition package containing server-client application with a trained neural network for detecting tables and recognizing their structure

Struto: Table Structure Recognition using deep learning