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Co-founder of doctly.ai here (OCR tool) 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…
Would love to see the test file.
Related, does anyone know of an app that can read gauges from an image and log the number to influx? I have a solar power meter in my crawlspace, it is inconvenient to go down there. I want to point an old phone at it and log it so I can check it easily. The gauge is digital and looks like this: https://www.pvh2o.com/solarShed/firstPower.jpg
https://www.unix-ag.uni-kl.de/~auerswal/ssocr/
https://github.com/tesseract-ocr/tesseract
https://community.home-assistant.io/t/ocr-on-camera-image-fo...
https://www.google.com/search?q=home+assistant+ocr+integrati...
https://www.google.com/search?q=esphome+ocr+sensor
https://hackaday.com/2021/02/07/an-esp-will-read-your-meter-...
...start digging around and you'll likely find something. HA has integrations which can support writing to InfluxDB (local for sure, and you can probably configure it for a remote influxdb).
You're looking at 1xRaspberry PI, 1xUSB Webcam, 1x"Power Management / humidity management / waterproof electrical box" to stuff it into, and then either YOLO and DIY to shoot over to your influxdb, or set up a Home Assistant and "attach" your frankenbox as some sort of "sensor" or "integration" which spits out metrics and yadayada...
I would like to see how it performs with massively warped and skewed scanned text images, basically a scanned image where the text lines are wavy as opposed as straight horizontal, where the letters are elongated. One where the line widths are different depending on the position on the scanned image. I once had to deal with such a task that somebody gave me with OCR software, Acrobat, and other tools could not decode…
Dang. Super fast and significantly more accurate than google, Claude and others. Pricing : $1/1000 pages, or per 2k pages if “batched”. I’m not sure what batching means in this case: multiple pdfs? Why not split them to halve the cost? Anyway this looks great at pdf to markdown.
"World's best OCR model" - that is quite a statement. Are there any well-known benchmarks for OCR software?
We published this benchmark the other week. We'll can update and run with Mistral today! https://github.com/getomni-ai/benchmark
The new Mistral OCR release looks impressive - 94.89% overall accuracy and significantly better multilingual support than competitors. As someone who's built document processing systems at scale, I'm curious about the real-world implications. Has anyone tried this on specialized domains like medical or legal documents? The benchmarks are promising, but OCR has always faced challenges with domain-specific terminology…
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
[2] https://huggingface.co/datasets/getomni-ai/ocr-benchmark
I will give this a shot
Earlier quoted context omitted.
You'll be happier finding a replacement meter that has an interface to monitor it directly or a second meter. An old phone and OCR will be very brittle.
Not OP, but it sounds like the kind of project I’d undertake. Happiness for me is about exploring the problem within constraints and the satisfaction of building the solution. Brittleness is often of less concern than the fun factor. And some kinds of brittleness can be managed/solved, which adds to the fun.
LLM based OCR is a disaster, great potential for hallucinations and no estimate of confidence. Results might seem promising but you’ll always be wondering.
I would like to see how it performs with massively warped and skewed scanned text images, basically a scanned image where the text lines are wavy as opposed as straight horizontal, where the letters are elongated. One where the line widths are different depending on the position on the scanned image. I once had to deal with such a task that somebody gave me with OCR software, Acrobat, and other tools could not decode…
Garbage in, garbage out?