{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"github-paddlepaddle--paddleocr","slug":"paddlepaddle--paddleocr","name":"PaddleOCR","type":"repo","url":"https://www.paddleocr.com","page_url":"https://unfragile.ai/paddlepaddle--paddleocr","categories":["data-pipelines","documentation"],"tags":["ai4science","chineseocr","document-parsing","document-translation","kie","ocr","paddleocr-vl","pdf-extractor-rag","pdf-parser","pdf2markdown","pp-ocr","pp-structure","rag"],"pricing":{"model":"open_source","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"github-paddlepaddle--paddleocr__cap_0","uri":"capability://image.visual.multilingual.text.detection.and.recognition.via.pp.ocrv5.pipeline","name":"multilingual text detection and recognition via pp-ocrv5 pipeline","description":"Detects and recognizes text across 100+ languages using a two-stage deep learning pipeline: a text detection model (EAST-based) identifies text regions and bounding boxes in images, then a text recognition model (CRNN-based) decodes characters within those regions. Outputs structured JSON with character-level confidence scores and spatial coordinates. Supports both CPU and GPU inference with automatic model selection based on language and hardware availability.","intents":["Extract text from scanned documents or photos with precise bounding box coordinates for downstream processing","Build OCR pipelines that work offline without cloud API dependencies","Support non-Latin scripts (Chinese, Arabic, Devanagari, etc.) with high accuracy in production systems","Integrate OCR into RAG systems to convert image documents into searchable text"],"best_for":["Teams building document processing pipelines for multilingual content","Developers requiring on-premise OCR without cloud API costs or latency","AI/ML engineers integrating OCR into LLM-based document understanding systems"],"limitations":["Detection accuracy degrades on rotated text (>45°) without preprocessing","Recognition models optimized for document text; handwriting recognition requires specialized models","Inference latency ~200-500ms per image on CPU (varies by image size and language)","Memory footprint ~500MB-1GB for full model suite; requires quantization for mobile deployment"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","4GB+ RAM for CPU inference, 2GB+ VRAM for GPU acceleration","Pre-trained model weights (auto-downloaded on first use, ~200MB per language)"],"input_types":["image (JPEG, PNG, BMP, TIFF)","PDF (via preprocessing to image extraction)","numpy arrays (in-memory images)"],"output_types":["JSON with text, bounding boxes, confidence scores","structured text with spatial metadata"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_1","uri":"capability://image.visual.document.structure.parsing.and.layout.analysis.via.pp.structurev3","name":"document structure parsing and layout analysis via pp-structurev3","description":"Parses document layouts (tables, text blocks, figures, headers) using a hierarchical detection and recognition pipeline that identifies semantic regions beyond raw text. Combines object detection (YOLOv3-based) to locate structural elements with specialized recognition models for tables (cell extraction, row/column parsing) and text blocks (reading order inference). Outputs structured Markdown or JSON preserving document hierarchy and spatial relationships.","intents":["Convert complex PDFs with tables and multi-column layouts into structured Markdown for LLM consumption","Extract table data with preserved row/column structure for database ingestion","Reconstruct document reading order for accessibility and downstream NLP tasks","Build document understanding pipelines that preserve semantic structure for RAG systems"],"best_for":["Document processing teams handling mixed-format PDFs (text, tables, figures)","Organizations converting legacy documents to machine-readable formats","RAG system builders requiring structured document decomposition"],"limitations":["Table recognition accuracy depends on clear cell boundaries; handdrawn tables may fail","Figure detection identifies regions but does not extract figure captions or content","Reading order inference uses heuristics; complex multi-column layouts may require post-processing","Inference latency ~500ms-2s per page on CPU (scales with document complexity)"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","8GB+ RAM for full pipeline (detection + recognition models)","PDF preprocessing library (pypdf or similar) for multi-page document handling"],"input_types":["image (single page)","PDF (processed page-by-page)","numpy arrays"],"output_types":["Markdown with preserved structure","JSON with semantic region annotations","table data in CSV or structured format"],"categories":["image-visual","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_10","uri":"capability://automation.workflow.model.quantization.and.compression.for.edge.deployment","name":"model quantization and compression for edge deployment","description":"Compresses trained OCR models for edge/mobile deployment using quantization (INT8, FP16), pruning, and knowledge distillation. Reduces model size by 50-90% while maintaining accuracy within acceptable thresholds. Supports post-training quantization (no retraining) and quantization-aware training (QAT) for better accuracy. Outputs optimized models compatible with edge inference engines (ONNX, TensorRT, CoreML).","intents":["Deploy OCR models on mobile devices or edge devices with limited memory (< 500MB)","Reduce model download size for faster deployment and updates","Optimize inference latency on edge hardware (mobile CPUs, embedded processors)","Maintain model accuracy while reducing computational requirements"],"best_for":["Teams deploying OCR on mobile or edge devices","Developers optimizing model size/accuracy trade-offs for constrained environments","Organizations reducing deployment bandwidth and storage costs"],"limitations":["Quantization introduces accuracy loss (typically 1-5% depending on quantization level)","Quantization-aware training requires retraining; adds training time and complexity","Edge inference engines have limited operator support; some model architectures may not be compatible","Debugging quantized models is complex; accuracy issues may be hard to diagnose"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","Pre-trained model weights","Quantization calibration dataset (representative images for post-training quantization)"],"input_types":["trained model weights (.pdparams)","model configuration (.yml)","calibration dataset (for post-training quantization)"],"output_types":["quantized model weights (INT8, FP16)","ONNX model (for cross-platform compatibility)","TensorRT engine (for NVIDIA edge devices)","CoreML model (for iOS deployment)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_11","uri":"capability://automation.workflow.configuration.driven.model.selection.and.language.support","name":"configuration-driven model selection and language support","description":"Provides configuration system (YAML-based) for selecting pre-trained models, languages, and inference backends without code changes. Maintains model registry with metadata (language, accuracy, model size, inference speed) enabling automatic model selection based on input language and hardware constraints. Supports fallback models if primary model unavailable. Integrates with PaddleX for unified model management.","intents":["Select appropriate OCR models for different languages and use cases via configuration","Automatically choose models based on hardware constraints (GPU memory, CPU cores)","Switch between inference backends (PaddlePaddle, ONNX, TensorRT) via configuration","Manage model versions and enable A/B testing of different model variants"],"best_for":["Teams managing multiple OCR models for different languages/use cases","Developers deploying OCR across heterogeneous hardware environments","Organizations requiring model versioning and A/B testing capabilities"],"limitations":["Configuration complexity increases with number of models and languages","Model registry requires manual maintenance; outdated models may be selected","Automatic model selection heuristics may not match user preferences","Configuration validation is limited; invalid configurations may fail at runtime"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","YAML configuration file","Pre-trained model weights (auto-downloaded or pre-cached)"],"input_types":["YAML configuration file","image with language metadata","hardware specification (GPU type, memory)"],"output_types":["selected model configuration","inference backend specification","model metadata (accuracy, speed, size)"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_12","uri":"capability://automation.workflow.command.line.interface.for.batch.document.processing","name":"command-line interface for batch document processing","description":"Provides CLI subcommands for invoking OCR pipelines on document batches without writing Python code. Supports input/output specification (file paths, directories, S3 buckets), format conversion (PDF to images, images to JSON/Markdown), and pipeline chaining (OCR → structure parsing → translation). Includes progress reporting, error handling, and result aggregation for batch jobs.","intents":["Process document batches from command line without writing Python code","Integrate OCR into shell scripts and CI/CD pipelines","Convert document formats (PDF to Markdown, images to JSON) via CLI","Monitor batch processing progress and handle errors gracefully"],"best_for":["DevOps teams integrating OCR into CI/CD pipelines","Data engineers processing document batches in data pipelines","Non-developers using OCR via command-line interface"],"limitations":["CLI is less flexible than Python API; complex workflows require custom scripts","Error handling is basic; detailed error diagnostics require log file inspection","Progress reporting is text-based; no real-time visualization for large batches","Result aggregation is limited; complex post-processing requires additional tools"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","paddleocr package installed and in PATH","Input documents (images or PDFs)"],"input_types":["image file path","directory of images","PDF file path","S3 bucket path (with credentials)"],"output_types":["JSON with OCR results","Markdown with structured content","CSV with extracted data","translated documents"],"categories":["automation-workflow","tool-use-integration"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_2","uri":"capability://image.visual.vision.language.model.based.document.understanding.via.paddleocr.vl","name":"vision-language model-based document understanding via paddleocr-vl","description":"Integrates a vision-language model (VLM) backbone that jointly processes image and text embeddings to understand document semantics beyond character recognition. Uses a transformer-based architecture that fuses visual features (from document images) with language understanding to answer questions about document content, extract key information, and generate structured summaries. Supports multiple inference backends (PaddlePaddle native, ONNX, TensorRT) for deployment flexibility.","intents":["Answer natural language questions about document content without explicit OCR-then-parse workflows","Extract structured information (invoice amounts, dates, entity names) via semantic understanding rather than pattern matching","Generate document summaries that capture semantic meaning, not just concatenated text","Build intelligent document triage systems that classify and route documents based on content understanding"],"best_for":["Teams building document Q&A systems or intelligent document processing workflows","Organizations requiring semantic understanding beyond text extraction","Developers integrating document understanding into LLM-based agents"],"limitations":["VLM inference is computationally expensive (~1-3s per document on GPU); requires GPU for production use","Model performance depends on document quality and clarity; degraded on low-resolution or heavily distorted images","Context window limited to single-page documents; multi-page understanding requires chunking and aggregation","Requires fine-tuning on domain-specific documents for optimal accuracy (generic model may hallucinate)"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","NVIDIA GPU with 6GB+ VRAM (or CPU with 16GB+ RAM for inference, slower)","Vision-language model weights (~2-4GB)"],"input_types":["image (document page)","natural language query (for Q&A mode)","PDF (processed page-by-page)"],"output_types":["JSON with extracted key-value pairs","Markdown with semantic structure","natural language answers to queries","structured summaries"],"categories":["image-visual","text-generation-language"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_3","uri":"capability://text.generation.language.intelligent.document.understanding.via.pp.chatocrv4.with.llm.integration","name":"intelligent document understanding via pp-chatocrv4 with llm integration","description":"Combines OCR output with large language models to perform semantic document understanding tasks: key-value extraction, entity recognition, document classification, and question-answering. Routes OCR results through a configurable LLM backend (supports OpenAI, Anthropic, local models via Ollama) with prompt engineering optimized for document understanding. Implements chain-of-thought reasoning for complex extraction tasks and handles multi-page document aggregation.","intents":["Extract structured data (invoice fields, contract terms, form responses) from documents using LLM reasoning","Classify documents by type, urgency, or content category using semantic understanding","Answer complex questions about document content that require reasoning across multiple sections","Build document processing workflows that combine OCR accuracy with LLM semantic understanding"],"best_for":["Teams building intelligent document processing pipelines with semantic understanding requirements","Organizations automating document triage, classification, and data extraction workflows","Developers integrating document understanding into LLM-based agents or RAG systems"],"limitations":["LLM inference adds latency (1-5s per document depending on model and provider); cloud APIs incur per-request costs","Hallucination risk: LLM may invent information not present in document; requires validation layer","Prompt engineering required for domain-specific extraction; generic prompts may miss domain-specific entities","Multi-page document handling requires chunking strategy; context window limits prevent processing entire documents"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","LLM API key (OpenAI, Anthropic) OR local LLM (Ollama, vLLM) with 8GB+ VRAM","OCR pipeline (PP-OCRv5 or PP-StructureV3) for document text extraction"],"input_types":["image (document page)","PDF (processed page-by-page)","OCR output (JSON from PP-OCRv5)"],"output_types":["JSON with extracted key-value pairs","structured data matching schema","document classification labels","natural language answers"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_4","uri":"capability://text.generation.language.cross.lingual.document.translation.via.pp.doctranslation.pipeline","name":"cross-lingual document translation via pp-doctranslation pipeline","description":"Translates document content across languages while preserving layout and structure using a specialized translation pipeline that combines OCR, layout-aware translation, and document reconstruction. Uses machine translation models (supports multiple backends) with document-level context awareness to maintain consistency across pages. Outputs translated documents in original format (PDF, Markdown) with spatial layout preserved.","intents":["Translate scanned documents or PDFs while maintaining original layout and formatting","Build multilingual document processing pipelines that preserve document structure across languages","Enable global document distribution by translating content while preserving visual hierarchy","Support document understanding workflows that require translation before semantic analysis"],"best_for":["Organizations processing multilingual document collections","Teams building global document workflows requiring translation with layout preservation","Developers integrating translation into document processing pipelines"],"limitations":["Translation quality depends on source language and domain; technical documents may require post-editing","Layout reconstruction is heuristic-based; complex multi-column layouts may not preserve perfectly","Inference latency ~1-3s per page on CPU; scales with document length and language pair","Requires separate translation models for each language pair; not all language combinations supported"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","Translation model weights (auto-downloaded, ~200-500MB per language pair)","OCR pipeline (PP-OCRv5) for text extraction"],"input_types":["image (document page)","PDF (processed page-by-page)","OCR output (JSON from PP-OCRv5)"],"output_types":["translated PDF with preserved layout","translated Markdown","translated JSON with spatial metadata"],"categories":["text-generation-language","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_5","uri":"capability://automation.workflow.parallel.and.multi.device.inference.orchestration","name":"parallel and multi-device inference orchestration","description":"Distributes OCR inference across multiple GPUs, CPUs, or heterogeneous devices (NVIDIA GPU, Kunlun XPU, Ascend NPU) using PaddlePaddle's distributed inference framework. Implements batch processing, dynamic batching, and device-aware scheduling to maximize throughput. Supports both data parallelism (multiple images processed in parallel) and pipeline parallelism (detection and recognition stages on different devices). Includes automatic load balancing and fallback to CPU if GPU memory exhausted.","intents":["Process large document batches (1000s of images) efficiently using available hardware resources","Deploy OCR services that scale horizontally across multiple GPUs or machines","Optimize inference latency for high-throughput document processing pipelines","Support heterogeneous hardware environments (mixed CPU/GPU/XPU) without code changes"],"best_for":["Teams building high-throughput document processing services","Organizations with heterogeneous hardware (multiple GPU types, accelerators)","Developers optimizing inference latency for production document pipelines"],"limitations":["Batch processing introduces latency trade-off: larger batches improve throughput but increase per-image latency","Dynamic batching adds scheduling overhead (~10-50ms per batch); not suitable for ultra-low-latency requirements","Multi-device setup requires careful memory management; OOM errors may occur with large batches","Distributed inference across machines requires network overhead; local multi-GPU is more efficient"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","Multiple GPUs (NVIDIA, Kunlun, Ascend) OR multi-core CPU","Sufficient GPU memory (2GB per GPU minimum for single model, scales with batch size)"],"input_types":["batch of images (list of numpy arrays or file paths)","image queue (for streaming processing)"],"output_types":["batch of OCR results (JSON with bounding boxes and confidence scores)","streaming results (for real-time processing)"],"categories":["automation-workflow","data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_6","uri":"capability://code.generation.editing.model.training.and.fine.tuning.infrastructure","name":"model training and fine-tuning infrastructure","description":"Provides end-to-end training pipeline for custom OCR models using PaddlePaddle's training framework. Includes data preprocessing (image augmentation, normalization), model architecture building (configurable detection and recognition backbones), loss functions optimized for OCR tasks, and distributed training across multiple GPUs. Supports knowledge distillation to compress models for edge deployment, and includes checkpoint management, learning rate scheduling, and metric tracking.","intents":["Train custom OCR models on domain-specific datasets (handwriting, specialized fonts, non-standard layouts)","Fine-tune pre-trained models on new languages or scripts with limited labeled data","Compress models for edge/mobile deployment using knowledge distillation","Optimize model accuracy for specific use cases (e.g., invoice recognition, medical document OCR)"],"best_for":["ML teams building domain-specific OCR models","Organizations with proprietary datasets requiring custom model training","Developers optimizing model size/accuracy trade-offs for edge deployment"],"limitations":["Requires large labeled datasets (10k+ images minimum); limited data leads to overfitting","Training is computationally expensive (8-24 hours on single GPU for detection models)","Hyperparameter tuning requires experimentation; no automated hyperparameter search included","Knowledge distillation adds training complexity; requires both teacher and student model training"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","NVIDIA GPU with 8GB+ VRAM (or multi-GPU setup for faster training)","Labeled training dataset in PaddleOCR format (images + annotations)"],"input_types":["image dataset (JPEG, PNG)","annotation files (JSON or custom format)","pre-trained model weights (for fine-tuning)"],"output_types":["trained model weights (.pdparams)","model configuration files (.yml)","training logs and metrics","quantized/distilled models for deployment"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_7","uri":"capability://code.generation.editing.c.inference.engine.for.production.deployment","name":"c++ inference engine for production deployment","description":"Provides high-performance C++ inference runtime that loads PaddlePaddle models and executes inference without Python overhead. Supports model optimization (quantization, pruning, operator fusion) and hardware acceleration (TensorRT for NVIDIA, OpenVINO for Intel). Includes batch inference, multi-threaded execution, and memory pooling for efficient resource utilization. Deployable as standalone binary or embedded in C++ applications.","intents":["Deploy OCR models in production services with minimal latency and memory footprint","Integrate OCR into C++ applications (document processing servers, embedded systems)","Optimize inference performance for high-throughput document processing","Reduce deployment complexity by eliminating Python runtime dependency"],"best_for":["Teams deploying OCR in production services requiring low latency","Developers integrating OCR into C++ applications or microservices","Organizations optimizing inference performance and resource utilization"],"limitations":["C++ API is lower-level than Python; requires more boilerplate code","Model updates require recompilation or dynamic loading; less flexible than Python for experimentation","Debugging C++ inference is more complex than Python; requires C++ profiling tools","Cross-platform compilation requires platform-specific build configurations"],"requires":["C++11 or later compiler (GCC, Clang, MSVC)","PaddlePaddle C++ inference library (pre-built or compiled from source)","CMake 3.10+ for building","CUDA toolkit (for GPU acceleration) or TensorRT (for NVIDIA optimization)"],"input_types":["image file path (JPEG, PNG, BMP)","raw image buffer (uint8 array)","image tensor (pre-processed)"],"output_types":["OCR results (text, bounding boxes, confidence scores)","JSON serialized results"],"categories":["code-generation-editing","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_8","uri":"capability://tool.use.integration.mcp.server.integration.for.llm.based.document.processing","name":"mcp server integration for llm-based document processing","description":"Exposes PaddleOCR capabilities as an MCP (Model Context Protocol) server, enabling LLM agents and applications to invoke OCR operations as tools. Implements standardized MCP tool schemas for text detection, recognition, document parsing, and translation. Handles asynchronous request processing, result caching, and error handling. Integrates with LLM frameworks (Claude, OpenAI) for seamless document understanding workflows.","intents":["Enable LLM agents to process documents autonomously using OCR as a tool","Build document understanding workflows where LLMs can request OCR on demand","Integrate OCR into multi-step LLM reasoning chains for complex document tasks","Create standardized interfaces for document processing in LLM-based applications"],"best_for":["Teams building LLM agents that need to process documents","Developers integrating document understanding into Claude or OpenAI applications","Organizations standardizing document processing interfaces across LLM applications"],"limitations":["MCP server adds network latency; local inference is faster for single-machine deployments","Tool invocation overhead (~100-200ms per request) for LLM agent coordination","Result caching requires careful cache invalidation; stale results may occur with dynamic documents","Error handling in LLM agents may be brittle; OCR failures need graceful degradation"],"requires":["Python 3.8+","PaddlePaddle >= 3.0","MCP server library (stdio or HTTP transport)","LLM client supporting MCP (Claude API, OpenAI with MCP adapter)"],"input_types":["image URL or base64-encoded image","document file path","natural language query (for document Q&A)"],"output_types":["JSON with OCR results","structured data matching LLM-requested schema","natural language responses"],"categories":["tool-use-integration","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__cap_9","uri":"capability://data.processing.analysis.pdf.preprocessing.and.multi.page.document.handling","name":"pdf preprocessing and multi-page document handling","description":"Handles PDF parsing, page extraction, and preprocessing for multi-page document workflows. Extracts individual pages as images, applies document-specific preprocessing (deskewing, denoising, contrast enhancement), and manages page ordering and metadata. Supports batch processing of large PDFs and includes memory-efficient streaming for documents exceeding available RAM. Integrates with OCR pipelines for seamless end-to-end PDF processing.","intents":["Convert multi-page PDFs to images for OCR processing with automatic page extraction","Apply document preprocessing (deskew, denoise, enhance contrast) before OCR for improved accuracy","Process large PDFs (100+ pages) efficiently without loading entire document into memory","Preserve document metadata and page ordering for downstream processing"],"best_for":["Teams processing large PDF document collections","Developers building end-to-end PDF-to-structured-data pipelines","Organizations requiring robust PDF handling for production document processing"],"limitations":["PDF parsing is complex; some PDFs with non-standard encoding may fail to extract correctly","Preprocessing heuristics (deskew, denoise) may degrade image quality for already-clean documents","Large PDFs (1000+ pages) require streaming to avoid memory exhaustion; adds complexity","Metadata extraction depends on PDF structure; some PDFs lack embedded metadata"],"requires":["Python 3.8+","PDF parsing library (pypdf, pdfplumber, or similar)","Image processing library (OpenCV, Pillow)","Sufficient disk space for temporary image files (1-2MB per page)"],"input_types":["PDF file path","PDF binary stream","multi-page TIFF"],"output_types":["extracted images (JPEG, PNG)","image metadata (page number, dimensions, DPI)","preprocessed images (deskewed, denoised)"],"categories":["data-processing-analysis","automation-workflow"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"github-paddlepaddle--paddleocr__headline","uri":"capability://data.processing.analysis.ocr.and.document.ai.toolkit","name":"ocr and document ai toolkit","description":"PaddleOCR is a powerful, lightweight OCR toolkit that transforms images and PDF documents into structured data, supporting over 100 languages for document parsing and intelligent understanding.","intents":["best OCR toolkit","OCR for document parsing","OCR solutions for multilingual support","best document AI tools","OCR software for structured data extraction"],"best_for":["multilingual document processing","structured data extraction from images"],"limitations":[],"requires":[],"input_types":["images","PDFs"],"output_types":["JSON","Markdown","DOCX"],"categories":["data-processing-analysis"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":58,"verified":false,"data_access_risk":"high","permissions":["Python 3.8+","PaddlePaddle >= 3.0","4GB+ RAM for CPU inference, 2GB+ VRAM for GPU acceleration","Pre-trained model weights (auto-downloaded on first use, ~200MB per language)","8GB+ RAM for full pipeline (detection + recognition models)","PDF preprocessing library (pypdf or similar) for multi-page document handling","Pre-trained model weights","Quantization calibration dataset (representative images for post-training quantization)","YAML configuration file","Pre-trained model weights (auto-downloaded or pre-cached)"],"failure_modes":["Detection accuracy degrades on rotated text (>45°) without preprocessing","Recognition models optimized for document text; handwriting recognition requires specialized models","Inference latency ~200-500ms per image on CPU (varies by image size and language)","Memory footprint ~500MB-1GB for full model suite; requires quantization for mobile deployment","Table recognition accuracy depends on clear cell boundaries; handdrawn tables may fail","Figure detection identifies regions but does not extract figure captions or content","Reading order inference uses heuristics; complex multi-column layouts may require post-processing","Inference latency ~500ms-2s per page on CPU (scales with document complexity)","Quantization introduces accuracy loss (typically 1-5% depending on quantization level)","Quantization-aware training requires retraining; adds training time and complexity","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.8812465309865755,"quality":0.5,"ecosystem":0.7000000000000001,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.3,"quality":0.2,"ecosystem":0.15,"match_graph":0.3,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:22.063Z","last_scraped_at":"2026-05-03T13:58:26.976Z","last_commit":"2026-04-28T03:51:17Z"},"community":{"stars":77000,"forks":10355,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=paddlepaddle--paddleocr","compare_url":"https://unfragile.ai/compare?artifact=paddlepaddle--paddleocr"}},"signature":"UZmatEPdZq1g6r8h0VxY/B4Be26rJbl5QaCC+IoLtaLHYOkvrRiJEEEp+dbJ56DHeO5VuRXOzx1hQ1k5/6qqBw==","signedAt":"2026-06-21T05:01:36.083Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/paddlepaddle--paddleocr","artifact":"https://unfragile.ai/paddlepaddle--paddleocr","verify":"https://unfragile.ai/api/v1/verify?slug=paddlepaddle--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"}}