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

High-Performance Transformers for Table Structure Recognition Need Early Convolutions

CVPR 2022: Table Structure Recognition

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

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

GloSAT Historical Measurement Table Dataset

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

A Python package that converts table images into HTML format using Object Detection model and OCR.

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).

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