---

name: local-ocr-pipelines description: "Routes PaddleOCR users to local OCR, single-model predictors, and engine/device troubleshooting." metadata: disco-role: operating disable-model-invocation: true license: Apache 2.0

Local OCR and Model Pipelines

Use this route when the task is about PaddleOCR's local OCR pipeline or a standalone predictor class. If the user wants full document parsing, Office conversion, or hosted service usage, route them elsewhere.

Handle these tasks here

• General OCR with PaddleOCR.
• Standalone predictors such as TextDetection, TextRecognition, DocImgOrientationClassification, TextLineOrientationClassification, TextImageUnwarping, LayoutDetection, FormulaRecognition, ChartParsing, DocVLM, TableClassification, TableCellsDetection, TableStructureRecognition, and SealTextDetection.
• CLI subcommands like paddleocr ocr, paddleocr text_detection, paddleocr text_recognition, and the other single-model predictors.
• Model-name selection, language fallback, and engine/device configuration for local inference.

Route away from here

• PPStructureV3, PaddleOCRVL, PPChatOCRv4Doc, PPDocTranslation, and doc2md belong in document-parsing-and-conversion.
• Official hosted API, MCP, and LangChain integrations belong in cloud-api-and-integrations.
• Training, export, deployment, and TIPC evidence belong in training-export-and-deployment.

Read these references

• references/ocr-workflows.md for end-to-end OCR recipes and language/model selection.
• references/model-and-engine-reference.md for model families, default subcommands, and engine flags.
• references/troubleshooting.md for model download, backend, and language/version failures.

Use the bundled script

• scripts/run_ocr_pipeline.py wraps the public PaddleOCR API with safe defaults for local runs.

What future agents should know

• PaddleOCR is the end-to-end OCR pipeline; it returns a list of result objects that can be printed or saved.
• The common control surface comes from the shared PaddleX wrapper: device, engine, engine_config, enable_hpi, use_tensorrt, precision, enable_mkldnn, cpu_threads, and enable_cinn.
• The CLI and Python APIs share the same basic model-selection logic. Default OCR is PP-OCRv6; older versions and language-specific fallbacks are documented in the workflow reference.
• For standalone predictors, prefer the model-class names and CLI subcommands over ad hoc source checkout scripts.
• Keep CPU import/help checks separate from accelerated backend claims.

Common triggers

• "Run OCR on this image"
• "Why did PP-OCRv6 choose this model name?"
• "How do I use text detection / recognition directly?"
• "What engine should I use for this predictor?"
• "Why is the language or model combination unsupported?"