Wouldn't "Serverless OCR" mean something like running tesseract locally on your computer, rather than creating an AI framework and running it on a server?
Question for the crowd -- with autoscaling, when a new pod is created it will still download the model right from huggingface?
I like to push everything into the image as much as I can. So in the image modal, I would run a command to trigger downloading the model. Then in the app just point to the locally downloaded model. So bigger image, but do not need to redownload on start up.
I am working on a client project, originally built using Google Vision APIs, and then I realized Tesseract is so good. Like really good. Also, if PDF text is available, then pdftotext tools are awesome.
My client's usecase was specific to scanning medical reports but since there are thousands of labs in India which have slightly different formats, I built an LLM agent which works only after the pdf/image to text process - to double check the medical terminology. That too, only if our code cannot already process each text line through simple string/regex matches.
There are perhaps extremely efficient tools to do many of the work where we throw the problem at LLMs.
Tried adding a receipt itemization feature into an app using OpenAI. It does 95% right but the remaining 5% are a mess. Mostly it mixes prices between items (Olive oil 0.99 while Banana 7.99). Is there some lightweight open source lib that can do this better?
hi. i run "ocr" with dmenu on linux, that triggers maim where i make a visual selection. a push notification shows the body (nice indicator of a whiff), but also it's on my clipboard
So I'm trying to OCR 1000s of pages of old french dictionaries from the 1700s, has anything popped up that doesn't cost an arm and a leg, and works pretty decently?
Not sure what “your own” in the title is supposed to mean if you are running a model that you didn’t train using a framework that you didn’t write on a server that you don’t own.
Not sure what "baking your own bread" means if you are using wheat grown by someone else in an oven that you didn't build that is run with electricity you didn't created from your muscles' force. You haven't even contributed to the nuclear fusion which created the oxygen for the water molecules you've been using! How dare you, standing of the shoulders of giants!
Consider the source of the idiom: rolling your own cigarettes.
Which involves taking some rolling papers, a pouch of loose tobacco (or whatever), and perhaps a little device if you're rich. As opposed to manufactured cigarettes, you're just doing some manual assembly for the end-product.
You don't need to cultivate the plants or pulp any trees to roll your own.
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[ 3.2 ms ] story [ 46.2 ms ] thread> In production, DeepSeek-OCR can generate training data for LLMs/VLMs at a scale of 200k+ pages per day (a single A100-40G).
That... doesn't sound legal
I like to push everything into the image as much as I can. So in the image modal, I would run a command to trigger downloading the model. Then in the app just point to the locally downloaded model. So bigger image, but do not need to redownload on start up.
ocrarena.ai maintains a leaderboard, and a number of other open source options like dots [1] or olmOCR [2] rank higher.
[1] https://www.ocrarena.ai/compare/dots-ocr/deepseek-ocr
[2] https://www.ocrarena.ai/compare/olmocr-2/deepseek-ocr
My client's usecase was specific to scanning medical reports but since there are thousands of labs in India which have slightly different formats, I built an LLM agent which works only after the pdf/image to text process - to double check the medical terminology. That too, only if our code cannot already process each text line through simple string/regex matches.
There are perhaps extremely efficient tools to do many of the work where we throw the problem at LLMs.
Which involves taking some rolling papers, a pouch of loose tobacco (or whatever), and perhaps a little device if you're rich. As opposed to manufactured cigarettes, you're just doing some manual assembly for the end-product.
You don't need to cultivate the plants or pulp any trees to roll your own.
I have 4 of these now, some are better than others. But all worked great.