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About this paper
Aryan Gupta, Anupam Purwar
This paper introduces Sprinklr-Edge-OCR, an optimized traditional OCR system, and benchmarks it against state-of-the-art Large Vision-Language Models (LVLMs) across 54 languages in real-world, noisy conditions. The research demonstrates that while LVLMs offer broad capabilities, specialized traditional OCR systems remain significantly more efficient and cost-effective for resource-constrained edge deployments, achieving superior performance on key accuracy and efficiency metrics.
Key insights
- Optimized traditional OCR systems like Sprinklr-Edge-OCR can significantly outperform LVLMs in efficiency (latency, memory, cost) for edge deployment, while maintaining competitive or superior accuracy in real-world scenarios.
- Specialized, task-specific optimizations and architectural choices remain highly valuable, enabling efficient solutions even as general-purpose LVLMs advance.
- A multi-faceted benchmarking approach, including an LLM judge for semantic consistency and comprehensive resource metrics, is essential for a realistic assessment of OCR system viability for practical, real-world applications.
Summary by alphaXiv.
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