DeepSeek OCR Dashboard: An Out-of-the-Box Local OCR Visualization Platform

DeepSeek OCR Dashboard is a local OCR visualization platform built on FastAPI and Vue.js, designed to lower the technical barrier for ordinary users to use the DeepSeek-OCR model. The platform supports PDF/image uploads, progress tracking, bounding box visualization, history management, and other features, making the use of top-tier OCR models simple and intuitive, while local data processing ensures privacy and security.

Although Optical Character Recognition (OCR) technology has been developed for many years, there are still barriers to its application: command-line tools are not user-friendly for ordinary users, and commercial API services involve data privacy and cost issues. As a high-performance model, DeepSeek-OCR excels in tasks such as document understanding, table recognition, and mathematical formula extraction, but its native interface requires technical background to use. This open-source project addresses this pain point by providing an out-of-the-box local web interface.

The project adopts a front-end and back-end separation architecture:

• Back-end: FastAPI, an asynchronous framework based on Python 3.10+, which automatically generates API documentation and ensures type safety.
• Front-end: Vue.js + Vite, providing a modern development experience, componentized UI, and responsive layout.
• OCR Engine: DeepSeek-OCR, supporting local deployment (data never leaves your device), GPU acceleration (e.g., RTX 3090), and multi-scenario (document, table, formula) recognition.

The platform's core features include:

1. Multi-format Upload: Supports PDF (automatic pagination and batch processing) and images (PNG/JPG), with drag-and-drop upload and real-time status display.
2. Progress Visualization: Displays upload progress, processing progress, and step tracking to reduce waiting anxiety.
3. Bounding Box Visualization: Overlays detection boxes on the original image, with different content types (paragraph/table/formula) colored by category and confidence displayed.
4. Annotation Details: Click on a region to view extracted text, position coordinates, region type, and confidence.
5. History Records: Saves processing history, supporting viewing past results, comparing versions, and exporting structured data.
6. Modular UI: Includes upload area, prompt area, mode area, operation area, visualization area, details area, and log area.

Applicable scenarios for the platform:

• Mathematical Formula Recognition: Accurately recognizes complex expressions while preserving structure, suitable for educators and researchers.
• Table Data Processing: Extracts text while understanding row and column structures, facilitating analysis of financial reports, experimental data, etc.
• Document Digitization: Converts paper archives/scanned documents into searchable and editable electronic documents, with local deployment ensuring sensitive data security.

Environment Requirements

• Python 3.10 (conda management recommended), PyTorch 2.6.0+ (CUDA 11.8 support), NVIDIA graphics card (e.g., RTX3090), Node.js.

Installation Steps

1. Create a conda environment: conda create -n ds-ocr python=3.10 -y && conda activate ds-ocr
2. Install back-end dependencies: cd web_project/backend && pip install --upgrade pip && pip install -r requirements.txt
3. Install front-end dependencies: cd ../frontend && npm install
4. Start the service: ./start.sh (starts both FastAPI back-end at localhost:8000 and Vite front-end at localhost:5173)

Environment Variable Configuration

Supports configuration of variables such as OCR_BACKEND_PORT, DEEPSEEK_OCR_MODEL_PATH, DEEPSEEK_ATTN_IMPL.

Technical Highlights

• Local First: Local data processing ensures privacy, no network dependency, no API costs, and low latency.
• Engineering Practices: Clear directory structure, explicit dependency management, externalized configuration, one-click startup script.
• User Experience Optimization: Real-time progress feedback, visual verification, history management.

Expansion Possibilities

Can be extended to support batch processing of folders, multiple export formats (Word/Excel/Markdown), custom model fine-tuning, Docker cloud deployment, REST API encapsulation, etc.

DeepSeek OCR Dashboard does not reinvent OCR technology; instead, it packages DeepSeek-OCR into a user-friendly interface, enabling more people to easily access top-tier OCR capabilities. It is suitable for individuals, small teams handling large volumes of documents, or privacy-focused enterprises. Its success lies in being user-centric, addressing the core pain point of 'convenient, visual, and manageable text recognition'—a valuable reference for AI tool developers.