rbx-proofreader 1.0.1

Visual trade detection and OCR engine

Proofreader 🔍

High-speed Roblox trade analyzer using YOLOv11, CLIP, and EasyOCR for instant item detection and structured JSON output.

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Features

• Detect outgoing and incoming trade items from screenshots
• Outputs structured JSON for automation or analytics
• Supports custom backgrounds and HTML templates
• Trained with YOLOv11 and enhanced with CLIP embeddings
• Easy installation and integration

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Example

Input Image	Detected UI Elements

Sample Output JSON:

{
    "outgoing": {
        "item_count": 4,
        "robux_value": 0,
        "items": [
            {"id": 1031429, "name": "Domino Crown"},
            {"id": 72082328, "name": "Red Sparkle Time Fedora"},
            {"id": 124730194, "name": "Blackvalk"},
            {"id": 16652251, "name": "Red Tango"}
        ]
    },
    "incoming": {
        "item_count": 2,
        "robux_value": 1048576,
        "items": [
            {"id": 21070012, "name": "Dominus Empyreus"},
            {"id": 22850569, "name": "Red Bandana of SQL Injection"}
        ]
    }
}

💻 Quick Start

import proofreader

# Analyze the image
data = proofreader.get_trade_data("test.png")

# Print the result
print(data)

Installation

Quick Install (Recommended)

pip install rbx-proofreader

From Source (Advanced / Custom Training)

1. Clone the repository.

2. Run python scripts/setup_items.py to initialize cache, download thumbnails, and create CLIP embeddings.

3. Place background images in: src/proofreader/train/emulator/backgrounds. Use continuous numbering: background_0.jpg, background_1.jpg, ...

4. Place HTML templates in src/proofreader/train/emulator/templates. Include both light and dark theme templates.

5. Configure synthetic data generation and YOLO training settings in src/proofreader/core/config.py

6. Run python scripts/train_model.py

Note: GPU recommended for training. Final model will be saved under runs/trainX/weights/best.pt. Rename to yolo.pt and move to src/assets/weights.

Tech Stack

• Python 3.12

• YOLOv11 for fast UI detection

• CLIP for visual embedding matching

• EasyOCR for text extraction

• NumPy / OpenCV for image processing

Contributing

Contributions are welcome! Please open an issue or submit a pull request.

License

This project is licensed under the MIT License.