table transformer structure recognition

Introduction

The Table Transformer is a model based on the Detection Transformer (DETR) architecture, fine-tuned specifically for recognizing table structures within documents. It was trained on the PubTables1M dataset and is designed to identify elements like rows and columns in tables found in unstructured documents. The model was introduced in the paper "PubTables-1M: Towards Comprehensive Table Extraction From Unstructured Documents" by Smock et al.

Architecture

This model uses the Transformer-based DETR architecture, which is known for its object detection capabilities. The "normalize before" setting of DETR is employed, meaning that layer normalization is applied prior to self-attention and cross-attention mechanisms. This approach aligns with certain variations of the Transformer architecture that prioritize stable training and performance.

Training

The Table Transformer was fine-tuned on the PubTables1M dataset, which is a large collection of tables extracted from unstructured documents. The model is capable of detecting table structures, which include identifying rows and columns, helping in the automated analysis and organization of table data.

Guide: Running Locally

1. Install Dependencies: Ensure you have Python and PyTorch installed. You can install the necessary libraries using pip:

   pip install torch transformers
   

2. Download the Model: You can download the model from Hugging Face's Model Hub using the Transformers library:

   from transformers import TableTransformerForObjectDetection
   model = TableTransformerForObjectDetection.from_pretrained('microsoft/table-transformer-structure-recognition')
   

3. Prepare Your Data: Format your data to match the expected input format of the model. Generally, this involves ensuring your input images or documents are preprocessed correctly.

4. Run Inference: Use the model to detect table structures in your data:

   # Example code for inference
   outputs = model(data)
   

5. Cloud GPU Suggestions: For improved performance, consider using cloud GPU services such as AWS EC2, Google Cloud's AI Platform, or Azure's Machine Learning Studio.

License

The Table Transformer model is released under the MIT License, which allows for wide usage in both commercial and private applications, given that the license terms are met.