Table Detection and Extraction Model
| Property | Value |
|---|---|
| Model Type | Object Detection (YOLOv8) |
| Accuracy | 96.2% mAP@0.5 |
| Training Hardware | NVIDIA GeForce RTX 3060 |
| Framework | PyTorch / Ultralytics |
| Developer | FODUU AI |
What is table-detection-and-extraction?
This model is a specialized implementation of YOLOv8s designed for detecting and extracting tables from documents. It can identify both bordered and borderless tables with high accuracy, making it particularly valuable for document analysis and data extraction workflows. The model utilizes advanced object detection techniques combined with OCR capabilities to not only locate tables but also extract their contents.
Implementation Details
Built on the YOLOv8s architecture, the model employs a modified CSPDarknet53 backbone enhanced with self-attention mechanisms and feature pyramid networks. It achieves impressive accuracy scores of 96.1% for bordered tables and 96.3% for borderless tables, demonstrating balanced performance across table types.
- Customizable confidence threshold and IoU settings
- Support for batch processing of multiple documents
- Integrated OCR capabilities for text extraction
- Optimized for both structured and unstructured documents
Core Capabilities
- High-precision table detection (96.2% mAP@0.5)
- Distinction between bordered and borderless tables
- Bounding box delineation for table isolation
- OCR integration for data extraction
- Support for unstructured document analysis
Frequently Asked Questions
Q: What makes this model unique?
The model's ability to handle both bordered and borderless tables with equal precision, combined with its OCR integration capabilities, makes it stand out. Its high accuracy and versatility in processing unstructured documents provide a comprehensive solution for document analysis tasks.
Q: What are the recommended use cases?
The model is ideal for document digitization projects, automated data extraction from reports and forms, digital archiving systems, and any application requiring table detection and data extraction from both structured and unstructured documents.
