Feature Proposal: Add VLM (Vision Language Model) as an Optional OCR Engine

[vegetablest]

💡 Core Concept

Coexistence, not replacement.
Offer multiple choices for different user preferences:

• 🖥️ Privacy-first users — continue using local OCR (RapidOCR), where all data stays on-device.
• ☁️ Lightweight or accuracy-focused users — opt for a cloud-based VLM API OCR, reducing local resource load and improving recognition quality.

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🔍 Current Limitations of Local OCR

• Memory usage: Resident model consumes 200–500 MB RAM.
• CPU usage: Local inference takes up CPU resources.
• Startup delay: Model loading slows down initialization.

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🌟 Advantages of the VLM-based OCR Option

1. Resource Optimization

• 🧩 Zero memory footprint – no local model required.
• ⚡ Zero CPU consumption – inference handled entirely in the cloud.
• 🚀 Faster startup – no model initialization delay.

2. Feature Enhancements

• ✨ Higher accuracy in complex or noisy scenarios.
• 🧠 Contextual understanding of image content and layout.
• 📊 Structured extraction (tables, lists, key-value pairs).
• 🌐 Improved multilingual support, especially for mixed-language content.

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🔒 Privacy and User Control

• Local OCR: Full data privacy — images and text are processed entirely offline.
• Cloud-based VLM OCR: Opt-in feature — users are clearly informed before any data upload.

This dual approach respects user choice, allowing them to decide between privacy-first and performance-first workflows without compromise.