VLM vs OCR Benchmark Part 2: Self-Hosted Quantized Models - The Reality Check

Motivation & Scope

After the promising cloud-based VLM results, we focused on three key questions about what are performances on noisy scanned documents.

1. Quantization Impact: How do quantized versions of Qwen 2.5 VL (3B, 7B, 32B) perform compared to their full-precision counterparts? how does a finetuned version of QWEN for OCR tasks - namely RomlOCR- performs?
2. Small Model Viability: Can ultra-compact models like SmolVLM deliver acceptable OCR performance out-of-the-box? or shall they be fine-tuned to be viable?
3. Specialized Alternatives: Are there OCR-focused VLMs that outperform general-purpose models?

Hardware & Deployment

All models were deployed on a single RTX ADA 6000 (48GB VRAM) using my already existing LLM Self-Hosted Deployment Roadmap. This represents a realistic production setup for most organizations moving from cloud APIs to self-hosted solutions.

Model Selection & Methodology

Quantized Qwen 2.5 VL Variants

• Qwen 3B: Full precision and AWQ quantized
• Qwen 7B: Full precision, AWQ quantized, and W8A8 quantized
• Qwen 32B: AWQ quantized (memory constraints)

Additional Models Tested

• RolmOCR 8B: A specialized OCR-focused VLM model
• SmolVLM variants: 256M, 500M, and 2B parameters

We used the same evaluation metrics as Part 1: text similarity, WER, CER, word accuracy, and processing time.

Key Results

Quantization Performance Analysis

Critical Findings:

1. Qwen 7B AWQ Sweet Spot: Best balance with 0.812 similarity and 0.893 word accuracy at 7.7s per image
2. RolmOCR 8B Excellence: Highest similarity (0.874) thanks to OCR-specific training
3. Size Paradox: Qwen 32B AWQ (0.729 similarity) underperformed smaller quantized models
4. Quantization Efficiency: AWQ delivered 90%+ performance with 2-3x memory savings

Processing Time Trade-offs

• Qwen 3B AWQ: Fastest at 8.7s per image
• Qwen 3B Full: Slowest at 18.1s, proving quantization benefits
• RolmOCR 8B: Competitive at 9.2s with superior accuracy

The SmolVLM Reality Check

SmolVLM testing revealed a harsh reality: ultra-compact models are not production-ready for OCR. The 256M model produced unusable garbled output with massive hallucinations.

Sample SmolVLM 256M Output:

<fake_token_around_image> figured part of a larger structure is missing?
#iformale==Banner#ofAxes&XtrayChart{10:b, 45pt}P12C8AB3EKBEEEC9H...
[Extensive garbled symbols and nonsensical text]


OCR Grounding as Alternative Approach

Recent research, particularly LayTextLLM, suggests a more promising approach: combining traditional OCR with small VLMs through OCR grounding techniques. This method:

• Uses OCR engines for accurate text extraction
• Employs small VLMs for semantic understanding and layout interpretation
• Avoids the fundamental limitations of pure VLM-based OCR
• Achieves competitive performance with significantly lower computational requirements

This hybrid approach addresses the core limitations of using VLMs solely for OCR tasks. further investigation is needed to see if this approach can be used for production OCR systems. (If you have any ideas, please let me know)

Fundamental Limitations of VLM-based OCR

When using VLMs for OCR, there are some critical limitations, one has to keep in mind:

Technical Limitations

• Information Loss: VLMs use high-dimensional embeddings leading to more character-level inaccuracies compared to traditional OCR's precise character fitting.
• Text Alteration: VLMs tend to "correct" typos and modify original text, losing fidelity to source documents
• Layout Degradation: Original document layout and formatting are often lost or altered
• Self-Awareness Gap: These models cannot detect their own OCR inaccuracies. This is a big problem for production systems.

Performance Constraints

• Semantic Dependency: VLMs rely on semantic understanding rather than direct character recognition, failing on non-semantic text combinations
• Resolution Constraints: Limited input resolution restricts fine detail capture

Production Risks

• Unreliable Results: Solely relying on VLMs for OCR leads to unpredictable output quality
• Error Detection: Difficulty identifying and correcting OCR mistakes
• Consistency Issues: Variable performance across different document types and languages

Production Recommendations

Based on this comprehensive testing, I would recommend the following:

For High-Accuracy OCR Needs

• RolmOCR 8B: When production-ready VLM-only approach quick to deploy is required and accuracy is paramount

Avoid These Approaches

• Pure tiny VLM OCR: Too risky for production environments. Needs extensive finetuning on your tasks and testing before.
• Unquantized large models: Unnecessary resource consumption

Looking Forward

I'm deeply convinced that the future of document understanding lies not in replacing traditional OCR with VLMs, but in intelligent hybrid approaches that leverage the strengths of both technologies. In the near future, i will try to implement LayTextLLM to explore this approach.

For comprehensive deployment guides, infrastructure setup, and detailed implementation strategies, see my LLM Self-Hosted Deployment Roadmap.

Resources

• Complete benchmark toolkit: GitHub repository
• Deployment guide: Self-Hosted VLM Roadmap
• LayTextLLM paper: OCR Grounding Research

This research was conducted as part of my ongoing investigation into practical AI deployment strategies. The results highlight the importance of hybrid approaches over pure VLM solutions for production OCR systems.

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