EdgeData Vision Synthetic Engine
Edge-case synthetic data for VLMs and OCR systems
What it does
EdgeData Vision: Turn OCR failures into proprietary assets, not headaches. 🚀 Stop "Data Anxiety": Generate 100% synthetic, high-fidelity data to bypass GDPR/Privacy risks. No real customer data needed. Boost CX: Don’t make clients wait weeks for VLM fine-tuning. Deploy production-ready models in days, providing a fast-track fail-safe for complex tables & small text. Scaling for high-stakes Enterprise AI environments. Open for strategic partnerships and pilot programs.
Does the same job
all alternatives →- BVBenchmarking VLMs vs. Traditional OCR2025 · getomni.ai · ▲146
Vision models have been gaining popularity as a replacement for traditional OCR. Especially with Gemini 2.0 becoming cost competitive with the cloud platforms. We've been continuously evaluating different models since we released the Zerox package last year (https://github.com/getomni-ai/zerox). And we wanted to put some numbers behind it. So we’re open sourcing our internal OCR benchmark + evaluation datasets. Full writeup + data explorer here: https://getomni.ai/ocr-benchmark Github: https://github.com/getomni-ai/benchmark Huggingface:…
- VVVisiData – vi for data2017 · github.com · ▲221
- Q2Qwen-2.5-32B is now the best open source OCR model2025 · github.com · ▲211
Last week was big for open source LLMs. We got: - Qwen 2.5 VL (72b and 32b) - Gemma-3 (27b) - DeepSeek-v3-0324 And a couple weeks ago we got the new mistral-ocr model. We updated our OCR benchmark to include the new models. We evaluated 1,000 documents for JSON extraction accuracy. Major takeaways: - Qwen 2.5 VL (72b and 32b) are by far the most impressive. Both landed right around 75% accuracy (equivalent to GPT-4o’s performance). Qwen 72b was only 0.4% above 32b. Within the margin of error. - Both Qwen models passed mistral-ocr (72.2%), which is specifically trained for OCR. - Gemma-3…
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