{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"awesome-paddleocr","slug":"paddleocr","name":"PaddleOCR","type":"mcp","url":"https://paddlepaddle.github.io/PaddleOCR/latest/en/version3.x/deployment/mcp_server.html","page_url":"https://unfragile.ai/paddleocr","categories":["mcp-servers","documentation"],"tags":[],"pricing":{"model":"unknown","free":false,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"awesome-paddleocr__cap_0","uri":"capability://image.visual.document.image.text.extraction.with.layout.preservation","name":"document-image-text-extraction-with-layout-preservation","description":"Extracts text from document images while preserving spatial layout and structure using PaddleOCR's deep learning-based character recognition pipeline. The system processes images through a detection-recognition-classification workflow that identifies text regions, recognizes characters with language-specific models, and outputs bounding boxes with confidence scores. Supports multi-language document processing through language-specific model selection.","intents":["Extract text from scanned PDFs and photos while maintaining document structure","Build document digitization pipelines that preserve layout for downstream processing","Process multilingual documents in a single inference pass","Extract text with positional metadata for layout-aware reconstruction"],"best_for":["Document processing teams building enterprise digitization systems","Developers creating document management systems requiring layout preservation","Teams processing mixed-language documents at scale"],"limitations":["Accuracy varies by language and document quality; no confidence threshold filtering exposed in MCP interface","Processing latency unknown for large batch operations or high-resolution images","Language support matrix not documented in provided specifications","No built-in handling for rotated/skewed documents mentioned in available docs"],"requires":["Python 3.7+ runtime environment","PaddleOCR package installation","MCP server deployment (Claude for Desktop or uvx)","Image input in supported formats (JPEG, PNG, PDF assumed but not confirmed)"],"input_types":["image/jpeg","image/png","application/pdf (inferred)"],"output_types":["structured JSON with text, bounding boxes, and confidence scores","layout-aware text representation"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-paddleocr__cap_1","uri":"capability://image.visual.structured.document.parsing.with.table.extraction","name":"structured-document-parsing-with-table-extraction","description":"Parses complex document structures including tables, forms, and multi-column layouts using PP-StructureV3 model, which combines text detection, recognition, and table structure analysis in a unified pipeline. The system identifies table cells, rows, and columns, extracts cell content, and outputs structured representations (HTML tables, JSON schemas) that preserve document hierarchy and relationships between elements.","intents":["Extract tabular data from scanned documents and convert to structured formats","Parse form documents to identify field labels and values","Build document understanding systems that preserve semantic structure","Convert unstructured document images into machine-readable structured data"],"best_for":["Financial document processing teams handling invoices, statements, and reports","Legal document automation systems requiring form field extraction","Data extraction pipelines converting paper documents to databases","Enterprise content management systems needing semantic document understanding"],"limitations":["Table extraction accuracy depends on table regularity; complex nested tables or merged cells may have degraded performance","No documented support for handwritten form fields or signatures","Structure parsing output format not specified in available documentation","Performance on documents with mixed layouts (text + tables + images) unknown"],"requires":["Python 3.7+ runtime","PaddleOCR with PP-StructureV3 model weights","MCP server deployment","Document images in supported formats"],"input_types":["image/jpeg","image/png","application/pdf (inferred)"],"output_types":["JSON with table structure and cell content","HTML table markup","structured hierarchical document representation"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-paddleocr__cap_2","uri":"capability://image.visual.vision.language.document.understanding.with.qa","name":"vision-language-document-understanding-with-qa","description":"Enables question-answering and semantic understanding of document images using PaddleOCR-VL (vision-language) model, which combines OCR with language model reasoning to answer natural language queries about document content. The system processes document images and natural language questions through a unified multimodal pipeline that understands both visual layout and semantic meaning, outputting answers grounded in document content.","intents":["Ask natural language questions about document content and receive accurate answers","Build document search systems that understand semantic meaning beyond keyword matching","Extract specific information from documents using natural language queries","Create document understanding agents that can reason about document content"],"best_for":["AI agents requiring document understanding capabilities for reasoning tasks","Non-technical users querying documents through natural language interfaces","Document search and retrieval systems needing semantic understanding","Compliance and audit systems requiring document content verification"],"limitations":["Vision-language model performance on out-of-domain documents unknown","Query complexity limits not documented; unclear if model supports multi-hop reasoning","Hallucination risk not addressed in available documentation","Inference latency for vision-language processing likely higher than text-only OCR"],"requires":["Python 3.7+ runtime","PaddleOCR-VL model weights (larger than base OCR models)","MCP server deployment","Document image and natural language query as inputs"],"input_types":["image/jpeg","image/png","natural language text (questions)"],"output_types":["natural language text (answers)","confidence scores or relevance metrics (inferred)"],"categories":["image-visual","text-generation-language","planning-reasoning"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-paddleocr__cap_3","uri":"capability://tool.use.integration.mcp.server.integration.with.claude.desktop","name":"mcp-server-integration-with-claude-desktop","description":"Exposes PaddleOCR capabilities as an MCP (Model Context Protocol) server that integrates directly with Claude for Desktop and other MCP-compatible clients through a standardized tool interface. The server implements MCP resource and tool definitions that allow Claude to invoke OCR operations with proper schema validation, error handling, and streaming response support, enabling seamless integration into Claude's agentic workflows.","intents":["Integrate OCR capabilities into Claude conversations for document analysis","Build Claude agents that can process document images as part of reasoning workflows","Enable Claude to access OCR tools without external API calls or custom integrations","Create document processing agents that leverage Claude's reasoning with local OCR inference"],"best_for":["Claude users wanting to add document processing to conversations","Teams building Claude agents requiring document understanding","Developers integrating PaddleOCR into MCP-compatible applications","Organizations wanting local OCR without cloud API dependencies"],"limitations":["MCP server transport mechanism not specified (stdio vs SSE vs HTTP unknown)","Tool schema definitions not provided in available documentation","Integration with Claude for Desktop requires specific configuration steps not fully documented","No documented support for streaming large document processing results"],"requires":["Claude for Desktop application (latest version)","Python 3.7+ runtime for MCP server","PaddleOCR package installation","MCP server configuration in Claude settings"],"input_types":["image/jpeg","image/png","natural language instructions"],"output_types":["JSON tool responses","structured document data","natural language summaries"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-paddleocr__cap_4","uri":"capability://automation.workflow.batch.document.processing.with.pipeline.parallelization","name":"batch-document-processing-with-pipeline-parallelization","description":"Processes multiple documents in parallel using PaddleOCR's pipeline parallelization capabilities, which distribute inference across multiple devices or CPU cores to maximize throughput. The system queues document images and executes OCR operations in parallel batches, with configurable concurrency levels and device allocation (CPU/GPU), enabling efficient large-scale document digitization workflows.","intents":["Process large document collections efficiently without sequential bottlenecks","Maximize hardware utilization by parallelizing OCR inference across devices","Build scalable document processing pipelines for enterprise digitization","Reduce total processing time for document batches through parallel execution"],"best_for":["Enterprise document digitization teams processing thousands of documents","Data pipeline engineers building high-throughput document processing systems","Teams with multi-GPU or multi-CPU infrastructure wanting to maximize utilization","Organizations requiring SLA compliance for document processing turnaround"],"limitations":["Parallelization strategy not documented (thread-based vs process-based vs distributed unknown)","Device allocation configuration options not specified","Memory overhead for parallel processing not quantified","No documented support for distributed processing across multiple machines"],"requires":["Python 3.7+ with multiprocessing support","PaddleOCR package with parallel inference capabilities","MCP server deployment","Multi-core CPU or GPU hardware for meaningful parallelization"],"input_types":["batch of image/jpeg","batch of image/png","batch configuration parameters"],"output_types":["JSON array of OCR results","progress/status updates","error reports per document"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-paddleocr__cap_5","uri":"capability://image.visual.multi.language.document.processing.with.language.detection","name":"multi-language-document-processing-with-language-detection","description":"Automatically detects document language and applies appropriate language-specific OCR models from PaddleOCR's 80+ language support library, enabling seamless processing of multilingual documents without manual model selection. The system analyzes document content to identify language, selects the corresponding optimized model variant, and performs OCR with language-specific character sets and recognition patterns.","intents":["Process documents in unknown languages without manual model configuration","Build multilingual document processing systems that handle language diversity automatically","Extract text from mixed-language documents with appropriate models per language","Support global document digitization without language-specific setup overhead"],"best_for":["Global organizations processing documents in multiple languages","Document processing platforms serving international users","Teams building language-agnostic document automation systems","Enterprises with multilingual document archives requiring digitization"],"limitations":["Language detection accuracy not documented; unclear performance on mixed-language documents","Supported language list not provided in available documentation","Model switching overhead for mixed-language documents unknown","No documented support for rare or minority languages"],"requires":["Python 3.7+ runtime","PaddleOCR with multi-language model weights (larger download/storage)","MCP server deployment","Document images in supported formats"],"input_types":["image/jpeg","image/png","language preference (optional)"],"output_types":["JSON with detected language and extracted text","language confidence scores (inferred)"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-paddleocr__cap_6","uri":"capability://automation.workflow.c.plus.plus.local.deployment.for.edge.inference","name":"c-plus-plus-local-deployment-for-edge-inference","description":"Enables deployment of PaddleOCR on edge devices and resource-constrained environments through C++ inference engine with optimized model quantization and mobile-friendly runtime. The system compiles PaddleOCR models to C++ with INT8 quantization and model compression, reducing model size and inference latency for deployment on mobile devices, embedded systems, and edge servers without Python runtime overhead.","intents":["Deploy OCR on mobile devices and edge hardware with minimal resource footprint","Build offline document processing applications without cloud dependencies","Reduce inference latency for real-time document scanning applications","Enable document processing on devices with limited CPU/memory/storage"],"best_for":["Mobile app developers adding OCR to iOS/Android applications","Edge computing teams deploying document processing on IoT devices","Organizations requiring offline-first document processing","Teams needing sub-100ms OCR inference latency"],"limitations":["C++ deployment process and requirements not documented in provided specs","Model quantization impact on accuracy not specified","Supported edge platforms and devices not listed","Integration with MCP server unclear for C++ deployment variant"],"requires":["C++ compiler (GCC/Clang/MSVC)","PaddleOCR C++ inference engine","Model quantization tools","Target platform SDK (iOS/Android/embedded Linux)"],"input_types":["image/jpeg","image/png","raw image buffers"],"output_types":["C++ structured data (OCR results)","JSON serialization of results"],"categories":["automation-workflow","image-visual"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"awesome-paddleocr__cap_7","uri":"capability://automation.workflow.inference.engine.configuration.with.device.selection","name":"inference-engine-configuration-with-device-selection","description":"Provides configurable inference engine settings allowing selection of compute devices (CPU/GPU), batch size tuning, and model precision (FP32/FP16/INT8) to optimize for specific hardware and performance requirements. The system exposes parameters for inference optimization including thread count, memory allocation, and device affinity, enabling fine-tuned deployment across diverse hardware configurations from embedded systems to multi-GPU servers.","intents":["Optimize OCR inference performance for specific hardware configurations","Balance accuracy and speed through precision selection (FP32 vs FP16 vs INT8)","Maximize GPU utilization for high-throughput document processing","Deploy on resource-constrained devices with appropriate configuration"],"best_for":["DevOps engineers optimizing document processing infrastructure","ML engineers tuning inference performance for production deployments","Teams deploying across heterogeneous hardware (CPU/GPU/TPU)","Organizations with strict latency or throughput SLAs"],"limitations":["Parameter reference documentation not provided in available specs","Configuration options and valid ranges unknown","Impact of different precision modes on accuracy not documented","No guidance on optimal settings for different hardware configurations"],"requires":["Python 3.7+ runtime","PaddleOCR package","MCP server deployment","Hardware-specific drivers (CUDA for GPU, etc.)"],"input_types":["configuration parameters (JSON or environment variables)","device specification"],"output_types":["configuration confirmation","performance metrics/benchmarks"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":31,"verified":false,"data_access_risk":"moderate","permissions":["Python 3.7+ runtime environment","PaddleOCR package installation","MCP server deployment (Claude for Desktop or uvx)","Image input in supported formats (JPEG, PNG, PDF assumed but not confirmed)","Python 3.7+ runtime","PaddleOCR with PP-StructureV3 model weights","MCP server deployment","Document images in supported formats","PaddleOCR-VL model weights (larger than base OCR models)","Document image and natural language query as inputs"],"failure_modes":["Accuracy varies by language and document quality; no confidence threshold filtering exposed in MCP interface","Processing latency unknown for large batch operations or high-resolution images","Language support matrix not documented in provided specifications","No built-in handling for rotated/skewed documents mentioned in available docs","Table extraction accuracy depends on table regularity; complex nested tables or merged cells may have degraded performance","No documented support for handwritten form fields or signatures","Structure parsing output format not specified in available documentation","Performance on documents with mixed layouts (text + tables + images) unknown","Vision-language model performance on out-of-domain documents unknown","Query complexity limits not documented; unclear if model supports multi-hop reasoning","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.05,"quality":0.41,"ecosystem":0.35000000000000003,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.25,"ecosystem":0.15,"match_graph":0.23,"freshness":0.12}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-06-17T09:51:03.579Z","last_scraped_at":"2026-05-03T14:00:15.503Z","last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=paddleocr","compare_url":"https://unfragile.ai/compare?artifact=paddleocr"}},"signature":"cJv4SWG6ok/uOJbMdhAWZwXQo5IeNF0ZbJd5bC4qjhq6/Ju3W6dmltlA5OmbO289jaNrn3eCl4sQwOxH7W9ICQ==","signedAt":"2026-06-20T12:20:11.636Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/paddleocr","artifact":"https://unfragile.ai/paddleocr","verify":"https://unfragile.ai/api/v1/verify?slug=paddleocr","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}