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A GPU helps supported stages; switching OCR engines changes more than the execution device.
Compare CPU and GPU OCR across the entire document path: page preparation, recognition, structure reconstruction and result storage. An accelerator helps only stages using a supported GPU backend. Buying a card does not automatically speed up every OCR process, and changing the engine creates a different comparison from changing its device.
Split the pipeline into stages
Record PDF rendering or image loading, preprocessing, text detection, recognition, layout handling and output generation as applicable. PaddleOCR documents device and component choices, while Tesseract’s FAQ discusses CPU execution and thread behavior. These are different projects, not one program with two device labels.
Measure each stage and the complete job. A GPU can spend time waiting for page decoding or storage, so low overall improvement does not necessarily indicate faulty hardware. Preserve versions, models, languages and input resolution with the timing record.
Compare equivalent configurations
| Comparison | What to fix | Conclusion requiring further evidence |
|---|---|---|
| One engine on CPU and GPU | Models and preparation | Identical quality in every case |
| Tesseract against PaddleOCR | Versions, languages and pages | Difference caused only by GPU |
| One queue against several | Processes, threads and memory | Linear scaling |
| Full PDF against ready images | Measurement boundary | Equivalent user-visible work |
Use the same expected output format. A text file, structured table and searchable PDF are different deliverables. Report any change in the pipeline as part of the tested configuration rather than hiding it behind a hardware label.
Build a labeled page sample
Include clean text, dense tables, rotation, noise and multi-column layout reflecting the actual archive. Save reviewed reference text or fields and define what constitutes an acceptable result. Useful quality can depend on reading order and numbers, not just recognizable individual words.
Keep failures separate from inaccurate results. A page that the pipeline never processes should not disappear from an average over successful pages. Inspect differences manually before assuming a faster configuration performs the same task.
Find the limiting stage
Measure CPU threads, RAM, storage and GPU activity alongside job timestamps. When adding processes, account for each process’s internal threads to avoid oversubscription. Compare representative concurrency after the single-job control works, preserving total completion time and memory peaks.
Choose the configuration for the document workload and quality target, not a universal CPU-versus-GPU winner. An operations-funding decision can then address the measured bottleneck instead of assuming the accelerator changes every stage equally.
Official references: paddleocr.ai — OCR, tesseract-ocr.github.io — FAQ.
Related guides: Long audio with Whisper: segments, queues and durable results, PaddleOCR on GPU: validating installation and document quality.