Since this is for the mac you really should be using apple's vision framework for OCR. It smokes tesseract in both speed and accuracy.
Edit: I'm curious which LLM was used to generate the code. I fed the title of your post to claude/deepseek/qwen/codex asking to recommend a stack for this project, expecting to frown thinking that they still recommend tesseract. However, I found that they all recommend apple's vision framework. In fact the latest model to recommend Tesseract is gpt-4.1.
nullsanity got downvoted into oblivion, but they are correct. This is one of the many reasons why vibe coding produces worse software. The code that is generated and the best practice recommendations are completely separate. They both come from a distribution of "most common", and best practice is rarely common. Especially when a practice is first established, or in a specific niche.
Can you copyright things like this now that LLMs exist? I mean, up until now if a small startup has a great idea they will get bought out by big tech which will integrate (or kill) their tech. But now with LLMs can the likes of OpenAI just tell their model to make something that works similar to X (such as this project) and then get round copying laws and negate being behind the curve?
EDIT: switched to the correct spelling of copyright.
I don't see why LLMs should make the legal part different. It's like saying if you can copyright a book considering LLMs now can copy / write a new one in just one hour. Making it faster doesn't change the legal aspect / ownership of something.
Having built something similar with CLIP on an M1, frame sampling rate is the whole ballgame. One frame a second on 12k videos is days, keyframes only got me to an overnight run.
How well do you think this would work on stock photography on m1 mac with 32GB ram? For example I'd like to be able to search a folder of ~2k photos for houses with palm trees. Or find photos of kitchens, or find photos of desert southwest landscapes.
I like the entire premise, the one thing stopping me from trying this is not knowing the time scales that I will need to set my computer aside for the processing of large folders of video frames, or my photos library's videos, some 12,000 videos
Would require you to be locked in to the one embedding model in the camera though and cameras would need to use the same or be incompatible. Would be fine with a standard model like CLIP but would leave a lot of potential on the table compared to a good way to do your own embedding for everything.
Because you haven't rereleased your own fork in rust yet! The "LLMs can do it" cuts both ways. If that doesn't sound worth your time then it's silly to rudely suggest it is worth someone else's.
Edit: I'm curious which LLM was used to generate the code. I fed the title of your post to claude/deepseek/qwen/codex asking to recommend a stack for this project, expecting to frown thinking that they still recommend tesseract. However, I found that they all recommend apple's vision framework. In fact the latest model to recommend Tesseract is gpt-4.1.
Can you copyright things like this now that LLMs exist? I mean, up until now if a small startup has a great idea they will get bought out by big tech which will integrate (or kill) their tech. But now with LLMs can the likes of OpenAI just tell their model to make something that works similar to X (such as this project) and then get round copying laws and negate being behind the curve?
EDIT: switched to the correct spelling of copyright.
1. https://news.ycombinator.com/item?id=34080326
[0]https://en.wikipedia.org/wiki/Copyright
Based on my experience, sampling rate can be tricky if what you are looking for lasted less than interval period.
you are pirating first-release movies for commercial purposes?
(& by "this" I mean an approximate AI search for photos & videos - I can't account for the "every frame", nor for the comparative search quality)