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You can run PaddleOCR against one PDF locally by passing the exact PDF file path to its OCR pipeline and specifying a save path for results. For a cloud run, package the same finite task in an Azure Container Apps manual Job, arrange an explicit way for the container to receive the PDF and preserve its output, then start an execution on demand. These are two ways to run local PaddleOCR inference—not the separate PaddleOCR API CLI that submits work to a hosted service.
How do I run PaddleOCR on a PDF?
Pass the PDF itself as the input to PaddleOCR’s local OCR pipeline. The official PaddleOCR 3.0.1 OCR usage documentation accepts a local image or PDF file path, a URL, or a directory of images. It specifically notes that PDFs in a directory are not currently supported, so do not pass a containing directory when processing a PDF.
The pipeline’s save_path parameter controls where inference result files are written. If you omit it, the documented behavior is that results are not saved locally. Choose an output destination deliberately, particularly when the process runs in a container that may be removed after the job finishes.
Install PaddleOCR using a supported route
The official PaddleOCR 3.0.1 installation documentation describes installation through pip or official PaddlePaddle Docker images. Its general OCR pipeline does not require an additional dependency group. Treat the documentation’s image tags and wheel versions as version-specific examples, not universal current pins: select compatible versions for your host or image and validate the environment you intend to run.
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In the PaddleOCR 3.0 documentation, PP-OCRv5 is the general OCR pipeline’s default. Defaults can change across releases; check the CLI help or documentation matching the version installed in your environment before relying on a default.
Make a reliable one-shot command
The OCR documentation describes the input and save-path behavior, but it does not prescribe a production job wrapper. As an implementation recommendation, make your command accept an input PDF path and output destination explicitly, log useful progress and errors, and exit nonzero if processing fails. These choices make failures visible to a calling shell or job controller; they are not guarantees of PaddleOCR’s documented CLI behavior.
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 launched manually for a one-off run. That makes a Manual job a natural fit when a person or automation decides when an individual OCR task begins. It is distinct from a continuously available app that serves requests.
Create a Manual job and start it on demand
Azure’s Container Apps Jobs CLI guide documents creating a manual job with az containerapp job create and --trigger-type Manual, then starting an execution with az containerapp job start --name ... --resource-group .... The following is a command outline, not a complete deployable configuration; replace the bracketed values and provide the container, environment, resources, and input/output setup required by your environment.
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Create the job with
az containerapp job create --name <job-name> --resource-group <resource-group> --environment <environment-name> --trigger-type Manual, adding the image, container configuration, and resource settings for your deployment.Rank #2
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Make the PDF available to the container using an input mechanism you have chosen, and define where results go so they remain available after the execution ends. The Azure job guide does not prescribe a PaddleOCR-specific file-transfer or output-storage design.
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Start an execution with
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Azure also documents execution configuration overrides. If you use an override to pass a per-run PDF path, account for the guide’s warning that overriding the template replaces the whole template configuration: preserve the required container and settings. A stable job definition with a separate, explicit input handoff may be simpler operationally, but the right handoff depends on your storage and deployment design.
Set completion, parallelism, retries, and timeout deliberately
Container Apps job definitions expose replica completion count, parallelism, retry limit, and replica timeout. For a single PDF processed by one worker, configure completion and parallelism to match that one-worker intent. Choose retries based on whether repeating the task is safe, and choose a timeout from measured processing duration for your PDF and environment. Azure’s job documentation describes these controls but does not provide PaddleOCR-specific values.
What changes between local and Azure runs?
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Consideration |
Local one-shot process or container |
Azure Container Apps Manual Job |
|---|---|---|
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Where inference runs |
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|
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How it starts |
Run the process or command directly. |
Create a Manual job, then start an execution on demand. |
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PDF input |
Pass the exact PDF path; the cited PaddleOCR documentation does not support PDFs inside directory prediction. |
Provide the PDF through an explicit input mechanism. The cited Azure job guidance does not choose one. |
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Result persistence |
Set |
Choose durable output handling for the container; the Azure job guide does not specify PaddleOCR output storage. The Tool Desk Outbyte PC Repair FREEClear out junk files and repair common Windows errorsFree Scan →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →Special offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.
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Run record and controls |
Exit status and local logs depend on the process or wrapper you implement. |
Azure provides execution status/history and controls for completion, retries, and timeout. |
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Resource sizing |
Depends on the model, PDF, and machine; no workload-specific sizing is established by the cited documentation. |
Choose CPU, memory, and timeout for the selected model and measured document; no PaddleOCR-specific sizing is established by the cited sources. Do these 3 things before closing this tab: 1Fix the driver behind crashes, sound loss and screen glitches2Clear out junk files and repair common Windows errors3Scan for outdated or missing drivers - takes under a minuteSpecial offer. See more information about Outbyte and uninstall instructions. Please review EULA and Privacy policy.
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What should you validate before relying on the job?
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Confirm that the installed PaddleOCR version, PaddlePaddle environment, and model selection work together on the target host or container image.
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Run a representative PDF through the exact-file input path and confirm that results are written to the intended
save_path. -
For Azure, verify that the execution can read its input and that output survives the container’s lifetime.
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Measure processing duration and resource use with the documents and model you expect to handle, then set the job’s timeout and resources accordingly.
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Test how the wrapper reports errors and whether retrying a failed or interrupted run is safe for your output design.
The cited documentation establishes the command and job model, not a complete tested deployment for an unspecified PDF workload. It does not establish universal CPU or memory sizing, runtime, cost, accuracy, a Dockerfile, or a particular cloud storage design.
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