Rapid AI
Open siteResearch & Data Analysis
Introduction
Rapid AI transforms businesses with smarter data analysis and workflow optimization.
Rapid AI Product Information
RapidAI Overview
RapidAI is an AI model platform aimed at bridging the gap between academic research and real-world engineering. It focuses on practical engineering deployments, emphasizes ease of use and out-of-the-box solutions, and can operate across computer vision, natural language processing, and speech, among other AI domains. The platform stresses simplicity, rapid integration, and minimal barriers to adoption, with capabilities for deploying AI models in real-world workflows without requiring extensive in-house training (though training capabilities exist).
What RapidAI Does
- Provides engineering-ready AI components and deployment strategies for CV, NLP, and speech tasks.
- Emphasizes ease of use, plug-and-play deployment, and minimal usage barriers.
- Bridges academia and industry by offering research-grade models and production-ready tooling.
- Maintains a strong focus on open, practical tooling that can be integrated into existing systems.
Star Projects
- 🤖 LLM
- LLM-DOC: Large model research institute document repository
- LLM-EXAM: Large model Chinese exam question bank (community version)
- 🔍💬 RAG相关 (RAG-related)
- Knowledge-QA-LLM: QA based on local knowledge and LLM
- ⚙ OCR推理部署 (OCR Inference & Deployment)
- RapidOCR family: OCR toolkits across multiple languages and backends (ONNXRuntime, OpenVINO, PaddlePaddle)
- RapidOcrOnnx, RapidOcrNcnn, RapidOcrJava, RapidOcrOnnxJvm, RapidOcrNcnnJvm, RapidOCRCSharp, RapidOcrAndroidOnnx, RapidOcrAndroidOnnxCompose, RapidOCRServer, etc.
- 📄 智能文档 (Intelligent Documents)
- RapidTableDetection: table detection and perspective/rotation correction
- RapidUnWrap: document distortion correction (ONNX-based)
- RapidDoc: document image content extraction, with plans to output Word/Txt/JSON/Markdown
- RapidOrientation: document direction classification
- RapidLaTexOCR: formula recognition using LaTeX-OCR and ONNXRuntime
- RapidTable: table recognition (ONNX) with deployment stability
- TableStructureRec: open-source table recognition models consolidation
- RapidLayout: layout analysis for Chinese/English documents
- RapidOCRPDF: PDF extraction
- RapidLaTexOCR: formula recognition
- 🎶 语音相关 (Speech & Audio)
- RapidASR: commercial-grade open-source ASR library (cross-platform, ONNXRuntime, FunASR-based)
- RapidTTS: cross-platform TTS using ONNXRuntime
- RapidVoice, RapidPunc, paraformer_simple, RapidAudioKit, RapidVAD, RapidTP
- 👷♂️🛠️ Builder
- OnnxruntimeBuilder, OpenCVBuilder, Custom Builder
- 🔄 转换工具 (Conversion Tools)
- PaddleOCRModelConvert, LabelConvert
- 🏷 评测工具 (Evaluation Tools)
- TextDetMetric, TableRecognitionMetric
- 📱 场景应用 (Scenario Applications)
- RapidVideOCR: video hard-subtitle extraction
- 🔢 测评集 (Test Collections)
- text_det_test_dataset, text_rec_test_dataset, table_rec_test_dataset
Architecture & Approach
- Engineering-focused, deployment-ready tooling rather than solely training pipelines.
- Leverages ONNXRuntime and related accelerators for cross-platform, efficient inference.
- Provides a suite of tools spanning vision, language, and audio to facilitate end-to-end AI workflows.
- Emphasizes easy integration into existing systems with modular builders and conversion tools.
How to Use RapidAI (Overview)
- Identify the domain (OCR, Intelligent Documents, Speech, LLM, etc.).
- Pick the appropriate project or tool (e.g., RapidOCR, RapidASR, RapidDoc).
- Use the provided builders or deployment scripts to integrate into your application.
- Utilize evaluation metrics to validate performance on your data.
- Deploy to production with ONNXRuntime-backed inference for speed and portability.
Safety and Best Practices
- Align usage with licensing and data governance appropriate to each model and component.
- Validate model outputs, especially for critical document processing and privacy-sensitive data.
- Monitor performance and resource usage in production environments.
Core Features
- Engineering-first AI tooling; ready-to-deploy components across CV, NLP, and speech domains
- Large collection of star projects for LLMs, RAG, OCR, intelligent documents, and speech
- Cross-platform inference powered by ONNXRuntime and related acceleration backends
- Comprehensive document-focused capabilities: table recognition, layout analysis, PDF/Word/Markdown output support
- Rich OCR ecosystem with multi-language support and language-specific optimizations
- Speech processing stack including ASR, TTS, punctuation, VAD, and related components
- Builders and conversion tools to simplify integration and data format handling
- Evaluation tools for quick benchmarking and metric reporting on custom datasets
- Practical scenario applications (e.g., video OCR, document understanding) for real-world use cases
- Active community and ongoing updates with industry-relevant achievements
About RapidAI
- Purpose: Build a bridge between academic AI research and practical engineering deployment.
- Focus: Simplicity, out-of-the-box usability, deployment efficiency, and broad applicability across AI domains.
- Mission: Lower the barriers to applying advanced AI in production and accelerate the path from research to real-world impact.
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