Table Transformer Structure Recognition
| Property | Value |
|---|---|
| Parameters | 28.8M |
| License | MIT |
| Framework | PyTorch |
| Paper | PubTables-1M Paper |
What is table-transformer-structure-recognition?
Table Transformer is a specialized model based on DETR (Detection Transformer) architecture, fine-tuned specifically for recognizing and extracting table structures from documents. Developed by Microsoft and trained on the PubTables1M dataset, this model employs a "normalize before" approach in its transformer architecture for enhanced table structure detection.
Implementation Details
The model leverages the DETR architecture with a crucial modification in its normalization strategy, applying layer normalization before self- and cross-attention operations. It utilizes F32 tensor types and incorporates transformer-based object detection principles for table structure analysis.
- Built on DETR architecture with specialized table detection capabilities
- Implements "normalize before" setting for enhanced performance
- Utilizes PyTorch framework with Safetensors support
- Optimized for inference endpoints deployment
Core Capabilities
- Accurate detection of table structures in documents
- Recognition of rows and columns within tables
- Processing of unstructured document layouts
- Integration-ready with inference endpoints
Frequently Asked Questions
Q: What makes this model unique?
This model's uniqueness lies in its specialized training on the PubTables1M dataset and its adapted DETR architecture specifically optimized for table structure recognition, making it particularly effective for document analysis tasks.
Q: What are the recommended use cases?
The model is ideal for applications requiring automatic table structure extraction from documents, such as document processing systems, data extraction pipelines, and automated document analysis tools.
