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How do I run PaddleOCR on a PDF?
Use PaddleOCR’s local inference pipeline and provide the PDF itself as the input—not a directory that contains it. The PaddleOCR 3.0.1 usage documentation says the pipeline accepts a local image or PDF path, a URL, or a directory of images, but PDF files inside a directory are not currently supported. See PaddleOCR OCR pipeline usage.
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Set save_path to the destination where you want inference result files written. The documented behavior is that results are not saved locally if this parameter is omitted. Choose an output location that remains available after the process finishes; for a container, that means planning separately for how results will be retained beyond the container’s lifetime.
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PaddleOCR’s official installation guidance lists pip and official PaddlePaddle Docker images as setup routes. It also says the general OCR pipeline does not require an additional dependency group. These are supported routes, not a guarantee that every host, PaddlePaddle build, or container-image combination will work together. Pin compatible versions and validate the selected environment. Use the installation guidance that matches your chosen release: PaddleOCR installation.
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Keep the model choice version-aware
In the PaddleOCR 3.0 documentation, PP-OCRv5 is the general OCR pipeline’s default. That default is version-specific; it should not be assumed for later or different releases. Check the CLI help or documentation matching the version installed in your environment before relying on a default or model option.
Make a one-shot command reliable
For a repeatable job, expose the PDF input path and output destination as explicit parameters. As an implementation recommendation—not a behavior promised by the OCR documentation—have the wrapper log progress and useful errors and return a nonzero exit status if inference fails. This makes failures easier to detect locally and through a job runner.
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How do I run a one-shot container job on Azure Container Apps?
Azure Container Apps Jobs are intended for containerized work that runs to completion, including tasks started on demand. Azure describes the model as: “A job runs containerized work to completion.” A manually triggered job fits a workflow where a person or automation decides when an individual OCR run begins. See Azure Container Apps jobs.
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Use the Azure CLI command shapes documented for manual jobs, replacing the example values with your subscription’s resource group, Container Apps environment, and image. Your image must contain the pinned PaddleOCR/PaddlePaddle runtime and a command or entry point that runs the one-PDF workflow.
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Create the job with
az containerapp job createand--trigger-type Manual. Supply the required environment, image, resource, and job configuration for your deployment. -
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Check execution status and history using the Azure CLI operations documented in Create and manage Azure Container Apps jobs with the CLI.
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Configure completion, parallelism, retries, and timeout
A job definition can set replica completion count, parallelism, retry limit, and replica timeout. For one PDF processed by one worker, set completion and parallelism to match that single-run intent. Select retries and timeout based on whether rerunning the same input is safe and on observed processing duration. Azure’s documentation describes these controls but does not provide PaddleOCR-specific values.
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Choose how the job receives and returns files
The container needs an explicit way to access the PDF, and its output needs a destination that persists after execution. The cited Azure job guide does not prescribe either mechanism for PaddleOCR. Select an input and output handoff appropriate to your environment, then configure the container to pass the exact PDF path to PaddleOCR and set its result save path.
Azure also documents starting an execution with configuration overrides. If you override the job template to supply a per-run PDF path, note that the override replaces the whole template configuration: preserve the required container and settings rather than sending only the changed field. A stable job definition with an explicit input handoff is another operational approach; the storage or transport choice depends on your deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Local process or Azure manual job?
| Decision point | Local one-shot process or container | Azure Container Apps manual Job |
|---|---|---|
| Where inference runs | On the local runtime using the chosen PaddlePaddle and PaddleOCR environment. PaddleOCR installation | In a container execution managed by Azure Container Apps. Azure job concepts |
| How it starts | Run the process or command directly. | Create a Manual job, then start an execution on demand. Azure CLI job guide |
| PDF input | Pass the exact PDF path; the cited PaddleOCR documentation does not support PDFs inside directory prediction. PaddleOCR usage | Provide the PDF using an input mechanism you design; the cited Azure guide does not select one. |
| Output | Set save_path to save result files locally. PaddleOCR usage |
Arrange durable output handling for the container; the Azure job guide does not specify PaddleOCR output storage. |
| Operational record | Process exit status and local logs depend on your implementation. | Azure provides execution status and history, along with configurable completion, retry, and timeout behavior. Azure job concepts Azure CLI job guide |
| Sizing | Depends on the model, PDF, and machine; a workload-specific recommendation is not stated in the cited documentation. | Choose CPU, memory, and timeout for the selected model and measured document; PaddleOCR-specific sizing is not stated in the cited Azure sources. |
What the documentation does—and does not—establish
The instructions support the architecture and command patterns: local PaddleOCR inference for a specified PDF path, plus an on-demand Azure Container Apps Job for finite container work. They do not establish an end-to-end tested configuration for an unspecified PDF workload, a complete Dockerfile, universal CPU or memory values, cost, processing time, or accuracy. Treat those as implementation-specific and validate them with the model, document, and environment you plan to use.
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