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Package installation and a visible GPU are preliminary checks; confirm the selected pipeline actually uses the intended device.
Validate PaddleOCR with GPU in two stages: software compatibility and actual device use, then quality on your documents. An installed package and a GPU present in the server do not establish a working configuration for the selected pipeline. Keep the version, backend and one control page identifiable throughout the test.
Select the version and capabilities
PaddleOCR installation documentation separates the package, optional capability groups and execution engine. An old installation example may not match the current components. Record the exact package and backend versions rather than combining commands from several releases.
Choose the pipeline the task needs. Ordinary OCR and a more extensive document-understanding workflow can execute different work. Preserve enabled components and input preparation so later memory and latency measurements have a clear meaning. A package import alone is not the end of setup.
Prove device use in the process
The OCR pipeline documentation covers environment checks and device selection. Correlate library reports with GPU observation during a known control job. An unnoticed fallback to CPU may keep the program functioning while changing the interpretation of its speed.
| Check | Record | Condition for continuing |
|---|---|---|
| Package | Version and installation route | Environment identified |
| Engine | Backend name and version | Compatible with the model |
| Device | Actual process GPU | No unnoticed CPU switch |
| One page | Text and launch log | Basic path works |
| Complete sample | Errors and elapsed time | Task can be evaluated |
Keep host and container checks separate if containers are used. A card visible to the operating system is not proof of access inside the application environment.
Prepare a quality control
Use permitted documents reflecting the task: ordinary text, tables, noisy scans and layout variations. Save expected fields or reviewed text. Check numbers, reading order and missing regions, not merely whether some output exists.
Measure the complete processing path and distinguish incorrect recognition from skipped or failed pages. Fix a model and preparation method while comparing device behavior. If resolution or preprocessing changes, repeat quality evaluation because reduced resource use may accompany lost information.
Change one component at a time
After the control works, alter batch, model component or device setting separately. Preserve each tested recipe and the corresponding result. Then add representative concurrency and test output persistence after a restart. This avoids describing a single successful page as a fully prepared archive service.
For a hardware owner, the final profile identifies what passed and what remains constrained. For an operations partner, it links additional resources to a particular pipeline.
Official references: paddleocr.ai — installation, paddleocr.ai — OCR.
Related guides: OCR on CPU or GPU: where acceleration changes the pipeline, Tesseract and GPU acceleration: why installing CUDA is insufficient.