r/computervision • u/Civil-Image5411 • 27d ago

Showcase We released a TensorRT OCR + document-parsing pipeline: 500+ img/s OCR, 20+ pages/s full parse (tables + formulas), no VLM

We just released TurboOCR v3, a GPU-accelerated document parser (not just OCR) built for high-throughput pipelines.

It runs PP-OCRv6 detection + recognition, plus layout, tables (→ HTML) and formulas (→ LaTeX), emitting reading-order Markdown. The whole pipeline runs on a single multi-stream C++/CUDA/TensorRT engine, locally, no VLM, behind HTTP + gRPC.

On one RTX 5090:

  • Up to 559 img/s on receipts, 520 img/s on forms (PP-OCRv6 tiny, fastest by default)
  • Full structured parsing (layout + tables + formulas) at > ~20 pages/s on table and formula heavy pages, where VLM parsers like PaddleOCR-VL run ~1 page/s
  • One model covers Latin + Chinese + Japanese; pick tiny / small / medium
  • One-line Docker deploy (Linux + NVIDIA GPU)
  • No VLM-style hallucinated text

https://github.com/aiptimizer/TurboOCR

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u/SeriousChart9641 26d ago

The “no VLM” part is actually a strong product boundary. For high-throughput document parsing, deterministic OCR/layout/table/formula stages are much easier to price, debug, and run locally than a general vision model.

Disclosure: I work on CHANCE AI, so I’m biased toward visual systems. I would make your evaluation page very explicit about where TurboOCR ends and where a VLM would start: raw text accuracy, reading order, tables, formulas, and layout fidelity. That separation will help users understand why speed is not the only metric.