Open-Source Table Detection Model: Efficiently Identify and Extract Tables from Unstructured Documents

Table Detection

Developed by grays-ai

A table detection model based on DETR architecture, specifically designed to identify and extract tables from unstructured documents

Object Detection

Transformers

Open Source License:MIT #Document Table Detection #Transformer Architecture #Structured Data Extraction

Downloads 17

Release Time : 7/27/2023

Model Overview

This model is trained on the PubTables1M dataset and can efficiently detect table structures in documents, suitable for document processing and analysis tasks

Model Features

Transformer-based Architecture

Adopts DETR's 'Pre-Normalization' setup, applying layer normalization before self-attention and cross-attention

Specialized Table Detection

Optimized and fine-tuned specifically for table detection tasks in documents

Large-scale Training Data

Trained on the PubTables1M dataset, which contains a vast number of table samples

Model Capabilities

Document Table Detection

Table Structure Recognition

Document Layout Analysis

Use Cases

Document Processing

Invoice Processing

Automatically detect and extract table data from invoice documents

Accurately identifies table regions in invoices

Report Analysis

Process business reports or research papers containing tables

Effectively extracts structured data from reports

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Table Detection

Model Overview

Model Features

Model Capabilities

Use Cases

🚀 Table Transformer (fine-tuned for Table Detection)

🚀 Quick Start

✨ Features

💻 Usage Examples

Basic Usage

📄 License