> Mistral OCR capabilities are free to try on le Chat
but when asked, Le Chat responds:
> can you do ocr?
> I don't have the capability to perform Optical Character Recognition (OCR) directly. However, if you have an image with text that you need to extract, you can describe the text or provide details, and I can help you with any information or analysis related to that text. If you need OCR functionality, you might need to use a specialized tool or service designed for that purpose.
Edit: Tried anyway by attaching an image; it said it could do OCR and then output... completely random text that had absolutely nothing to do with the text in the image!... Concerning.
Tried again with a better definition image, output only the first twenty words or so of the page.
Did you try using the API?
https://docs.mistral.ai/capabilities/document/
I used base64 encoding of the image of the pdf page. The output was an object that has the markdown, and coordinates for the images:
[OCRPageObject(index=0, markdown='', images=[OCRImageObject(id='img-0.jpeg', top_left_x=140, top_left_y=65, bottom_right_x=2136, bottom_right_y=1635, image_base64=None)], dimensions=OCRPageDimensions(dpi=200, height=1778, width=2300))] model='mistral-ocr-2503-completion' usage_info=OCRUsageInfo(pages_processed=1, doc_size_bytes=634209)
Feels like something is missing in the docs, or the API itself.
```  ```
I’m assuming this is partially because your use case is targeting RAG under various assumptions bur also partially because multimodal models aren’t near what I would need to be successful with?
The difference with this is that it took the entire page as an image tag (it's just a table of text in my document). rather than being more selective.
I do like that they give you coordinates for the images though, we need to do something like that.
Give the actual tool a try. Would love to get your feedback for that use case. It gives you 100 free credits initially but if you email me ([email protected]), I can give you an extra 500 (goes for anyone else here also)
In our current setup Gemini wins most often. We enter multiple generations from each model into the 'tournament', sometimes one generation from gemini could be at the top while another in the bottom, for the same tournament.
I have a lot of "This document filed and registered in the county of ______ on ______ of _____ 2023" sort of thing.
Give it a try, no credit cards needed to try it. If you email me ([email protected]) i can give you extra free credits for testing.
Now to figure out how many millions of pages I have.
Doctly runs a tournament style judge. It will run multiple generations across LLMs and pick the best one. Outperforming single generation and single model.
I love mistral and what they do. I got really excited about this, but a little disappointed after my first few tests.
I tried a complex table that we use as a first test of any new model, and Mistral OCR decided the entire table should just be extracted as an 'image' and returned this markdown:
```  ```
I'll keep testing, but so far, very disappointing :(
This document I try is the entire reason we created Doctly to begin with. We needed an OCR tool for regulatory documents we use and nothing could really give us the right data.
Doctly uses a judge, OCRs a document against multiple LLMs and decides which one to pick. It will continue to run the page until the judge scores above a certain score.
I would have loved to add this into the judge list, but might have to skip it.