Document Understanding Subnet - Whitepaper 1.0
  • What is the Document Understanding Subnet?
  • Core Functionalities
    • Current Capabilities
    • Future Capabilities
  • Supporting Infrastructure
  • Operational Overview
    • Reward Mechanism
  • Technical Architecture
    • Checkbox-Text Extraction: YOLO Checkbox Detector
    • OCR Engine: Tesseract OCR
    • Workflow of Checkbox-Text Extraction
  • Internal OCR Engine Development
    • Advanced Layout Analysis
  • Advantages of Document Understanding Subnet
  • Use Cases of Document Understanding Subnet
  • Economic Model
    • Key Participants and Roles
    • Integration with Bittensor’s Economic Framework
  • Comparative Analysis
    • GPT
    • Azure Document AI
    • Google Document AI
    • AWS Document Processing
  • Strategic Opportunities
  • Integration Options
  • Roadmap
  • Links
Powered by GitBook
On this page

Technical Architecture

PreviousReward MechanismNextCheckbox-Text Extraction: YOLO Checkbox Detector

Last updated 5 months ago

The Document Understanding Subnet's technical architecture is designed to provide precise and efficient checkbox-text extraction, leveraging a combination of advanced object detection and OCR technologies.

The architecture consists of two primary modules: the YOLO Checkbox Detector, based on the YOLOv8 object detection model, and the Tesseract OCR engine, both of which contribute to a robust, high-performance document processing pipeline.

Checkbox-Text Extraction: YOLO Checkbox Detector
OCR Engine: Tesseract OCR
Workflow of Checkbox-Text Extraction