rbx-proofreader 1.1.2

Visual trade detection and OCR engine

Proofreader 🔍

A high-speed vision pipeline for reading Roblox trade screenshots.

Proofreader transforms unstructured screenshots of Roblox trades ("proofs", hence "proofreader") into structured Python dictionaries. By combining YOLOv11 for object detection, CLIP for visual similarity, and EasyOCR, it achieves high accuracy across diverse UI themes, resolutions, and extensions.

Why Proofreader?

Roblox trade screenshots are commonly used as proof in marketplaces, moderation workflows, and value analysis, yet they are manually verified and error-prone. Proofreader automates this process by converting screenshots into structured, verifiable data in milliseconds.

Example

⚡ Performance

Tested on an RTX 5070 using $n=500$ real-world "worst-case" user screenshots (compressed, cropped, and varied UI).

Metric	Result (E2E)
Exact Match Accuracy	97.2% (95% CI: 95.4–98.5%)
Median latency	36.8 ms
95th percentile latency	73.4 ms

[!NOTE] End-to-End (E2E) latency includes image loading, YOLO detection, spatial organization, CLIP similarity matching, and OCR fallback.

✨ Key Features

• Sub-40ms Latency: Optimized with "Fast-Path" logic that skips OCR for high-confidence visual matches, ensuring near-instant processing.

• Multi-modal decision engine: Weighs visual embeddings against OCR text to resolve identities across 2,500+ distinct item classes.

• Fuzzy Logic Recovery: Built-in string distance matching corrects OCR typos and text obscurations against a local asset database.

• Theme & Scale Agnostic: Robust performance across various UI themes (Dark/Light), resolutions, and custom display scales.

💻 Quick Start

Installation

pip install rbx-proofreader

[!IMPORTANT] Hardware Acceleration: Proofreader automatically detects NVIDIA GPUs. For sub-40ms performance, ensure you have the CUDA-enabled version of PyTorch installed. If a CPU-only environment is detected on a GPU-capable machine, the engine will provide the exact pip command to fix your environment.

Usage

import proofreader

# Extract metadata from a screenshot
data = proofreader.get_trade_data("trade_proof.png")

print(f"Items Out: {data['outgoing']['item_count']}")
print(f"Robux In: {data['incoming']['robux_value']}")

[!TIP] First Run: On your first execution, Proofreader will automatically download the model weights and item database (~360MB). Subsequent runs will use the local cache for maximum speed.

🧩 How it Works

The model handles the inconsistencies of user-generated screenshots (varied crops, UI themes, and extensions) through a multi-stage process:

1. Detection: YOLOv11 localizes item cards, thumbnails, and robux containers.

2. Spatial Organization: Assigns child elements (names/values) to parents and determines trade side.

3. Identification: CLIP performs similarity matching. High-confidence results become Resolved Items immediately.

4. Heuristic Judge: Low-confidence visual matches trigger OCR and fuzzy-logic reconciliation.

📊 Data Schema

The get_trade_data() function returns a structured dictionary containing incoming and outgoing trade sides.

Key	Type	Description
item_count	int	Number of distinct item boxes detected.
robux_value	int	Total Robux parsed from the trade.
items	list	List of ResolvedItem objects containing id and name.

ResolvedItem Schema:

Property	Type	Description
id	int	The official Roblox Asset ID.
name	str	Canonical item name from the database.

🏗️ Development & Training

To set up a custom training environment for the YOLO and CLIP models:

# 1. Clone and Install
git clone https://github.com/lucacrose/proofreader.git
cd proofreader
pip install -e ".[train]"

# 2. Initialize Database
python scripts/setup_items.py

# 3. Training
# Place backgrounds in src/proofreader/train/emulator/backgrounds
# Place HTML templates in src/proofreader/train/emulator/templates
python scripts/train_models.py

[!CAUTION] GPU Required: Training is not recommended on a CPU. Final models save to runs/train/weights/best.pt. Rename to yolo.pt and move to src/assets/weights.

🛠️ Tech Stack

• Vision: YOLOv11 (Detection), CLIP (Embeddings), OpenCV (Processing)
• OCR: EasyOCR
• Logic: RapidFuzz (Fuzzy String Matching)
• Core: Python 3.12, PyTorch, NumPy

🤝 Contributing

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

📜 License

This project is licensed under the MIT License.