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Table Transformer (TATR) - PubTables1M Table Structure Recognition
A Transformer-based object detection model pre-trained on PubTables1M for recognizing table structures, including row and column segmentation, in scientific and academic documents.
About Model
Table Transformer (TATR) - PubTables1M Table Structure Recognition is a Transformer-based object detection model trained on the PubTables1M dataset. It is designed for detecting and identifying table structures, such as rows, columns, and table boundaries, in unstructured scientific and academic documents. Built on the DETR (DEtection TRansformer) framework, the model applies a "normalize before" approach, where layer normalization occurs before self- and cross-attention mechanisms. This model is particularly useful for document digitization, research paper analysis, and structured data extraction from complex table layouts in PDFs and scanned images.
Table Transformer (TATR) - PubTables1M Table Structure Recognition
Metadata
MIT
Microsoft
object detection
N.A.
Open
Sector Agnostic
12/03/25 06:34:59
0
Activity Overview
0
4 
0 
620
Tags
- object detection
- Transformers
- PyTorch
- safetensors
- table-transformers
- inference endpoints
License Control
MIT
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