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Tatr Tab Struct V2

Developed by deepdoctection
DETR architecture model trained on PubTables1M and FinTabNet datasets, specialized for table structure recognition tasks
Downloads 99
Release Time : 9/4/2023

Model Overview

This model adopts the Transformer architecture, capable of identifying rows, columns, headers, and cross-cell structures in tables, suitable for document digitization scenarios

Model Features

Cross-cell Recognition
Accurately identifies merged cells and cross-row/column structures in tables
Multi-element Detection
Simultaneously detects various layout elements such as tables, rows, columns, and headers
Optimized Edge Processing
Recommended to use a 5-pixel padding margin for optimal recognition results

Model Capabilities

Table Region Detection
Row/Column Structure Recognition
Header Classification
Merged Cell Detection

Use Cases

Document Digitization
Financial Statement Parsing
Automatically identifies structured data in complex financial statements
Accurately extracts row/column relationships and cross-cell data
Academic Literature Processing
Extracts table content from research papers
Preserves the hierarchical relationships of original tables
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