3 ms·
Excited to test this our on our side as well. We recently built an OCR benchmarking framework specifically for VLMs[1][2], so we'll do a test run today. From o
by themanmaran 2y ago
Excited to test this our on our side as well. We recently built an OCR benchmarking framework specifically for VLMs[1][2], so we'll do a test run today.
From our last benchmark run, some of these numbers from Mistral seem a little bit optimistic. Side by side of a few models:
model | omni | mistral |
gemini | 86% | 89% |
azure | 85% | 89% |
gpt-4o | 75% | 89% |
google | 68% | 83% |
Currently adding the Mistral API and we'll get results out today!
[1] https://github.com/getomni-ai/benchmark https://github.com/getomni-ai/benchmark
[2] https://huggingface.co/datasets/getomni-ai/ocr-benchmark https://huggingface.co/datasets/getomni-ai/ocr-benchmark
- jaggs 2y agoBy optimistic, do you mean 'tweaked'? :)
- themanmaran 2y agoUpdate: Just ran our benchmark on the Mistral model and results are.. surprisingly bad? Mistral OCR: - 72.2% accuracy - $1/1000 pages - 5.42s / page Which is pretty far cry from the 95% accuracy they were advertising from their private benchmark. The biggest thing I noticed is how it skips anything it classifies as an image/figure. So charts, infographics, some tables, etc. all get lifted out and returned as [image](image_002). Compared to the other VLMs that are able to interpret those images into a text representation. https://github.com/getomni-ai/benchmark https://github.com/getomni-ai/benchmark https://huggingface.co/datasets/getomni-ai/ocr-benchmark https://huggingface.co/datasets/getomni-ai/ocr-benchmark https://getomni.ai/ocr-benchmark https://getomni.ai/ocr-benchmark
- deleted 2y ago[deleted]