OpenCV VS tesseract-ocr

- [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection

I came across a very interesting [project]( (4) Mckay Wrigley on Twitter: "My goal is to (hopefully!) add my house to the dataset over time so that I have an indoor assistant with knowledge of my surroundings. It’s basically just a slow process of building a good enough dataset. I hacked this together for 2 reasons: 1) It was fun, and I wanted to…" / X ) made by Mckay Wrigley and I was wondering what's the easiest way to implement it using free, open-source software. Here's what he used originally, followed by some open source candidates I'm considering but would love feedback and advice before starting: Original Tools: - YoloV8 does the heavy lifting with the object detection - OpenAI Whisper handles voice - GPT-4 handles the “AI” - Google Custom Search Engine handles web browsing - MacOS/iOS handles streaming the video from my iPhone to my Mac - Python for the rest Open Source Alternatives: - [ OpenCV](https://opencv.org/) instead of YoloV8 for computer vision and object detection - Replacing GPT-4 is still a challenge as I know there are some good open-source LLms like Llama 2, but I don't know how to apply this in the code perhaps in the form of api - [DeepSpeech](https://github.com/mozilla/DeepSpeech) rather than Whisper for offline speech-to-text - [Coqui TTS](https://github.com/coqui-ai/TTS) instead of Whisper for text-to-speech - Browser automation with [Selenium](https://www.selenium.dev/) instead of Google Custom Search - Stream video from phone via RTSP instead of iOS integration - Python for rest of code I'm new to working with tools like OpenCV, DeepSpeech, etc so would love any advice on the best way to replicate the original project in an open source way before I dive in. Are there any good guides or better resources out there? What are some pitfalls to avoid? Any help is much appreciated!

Glancing at the code, I think it's detectMultiScaleNoGrouping and then the operator() of CascadeClassifierInvoker gets called. It will probably help you to put a breakpoint and step through that bit of the code.

On GitHub https://github.com/opencv/opencv

OpenCV

You also need C++ if you're going to do things which aren't built in as part of the engine. As an example if you're looking at using compute shaders, inbuilt native APIs such as a mobile phone's location services, or a third-party library such as OpenCV, then you're going to need C++.

wget -O opencv.zip https://github.com/opencv/opencv/archive/4.1.0.zip

Install Google Tesseract OCR (additional info how to install the engine on Linux, Mac OSX and Windows). You must be able to invoke the tesseract command as tesseract. If this isn’t the case, for example because tesseract isn’t in your PATH, you will have to change the “tesseract_cmd” variable pytesseract.pytesseract.tesseract_cmd. Under Debian/Ubuntu you can use the package tesseract-ocr. For Mac OS users. please install homebrew package tesseract.

I’ve used Tesseract for this. It seems to work well with tabular data. https://github.com/tesseract-ocr/tesseract

If you go this route, then using an app that can convert your handwritten notes to a digital format (indexed text), will give you a good balance between cognitive processing and efficient data storage/management; you can likely find many such apps on the App Store or Google Play. If you're interested in something more hands-on, on Arch you can probably experiment with Tesseract OCR in an interesting way (Example).

[1] https://github.com/tesseract-ocr/tesseract/issues/3447

Less that 20 years ago. Not a small project.

I have recently discovered a few very helpful github packages which help me make notes while listening to lectures. These would be 1. pix2tex (allows you to scan an equation and convert it to latex) 2. pix2text (allows you to scan an equation with words in it and converts it to latex and text) 3. Tesseract (not really a physics related package, but it does allow me to copy notes from transcripts easily) 4. Mathpix an app that performs all the above mentioned operations better than the packages above, but one which ain't free.

I suggest manually creating a dataset using scribd.com. It offers a free trial period of 30 days, but I am uncertain whether it covers unlimited documents or not. Nevertheless, there are over one million statements of purpose (SOPs) available on the site. You could also use the Scribd downloader. Some documents may be composed of a bunch of images, so you will have to use something like Tesseract OCR.

The ocr filter in ffmpeg is powered by the Tesseract library. As you will often find in ffmpeg, the build within ffmpeg has only a subset of the functionality of the original library - at least, for the moment. There's always the possibility of APIs being expanded in later ffmpeg releases. And it is open source of course, so there's the option of instigating those changes yourself - or using the original library in conjunction with ffmpeg if that suits your needs better.

After that you would use Tesseract-OCR to OCR the pages. Tesseract is a open source multiplatform OCR software. If the typeface is something non standard you would have to train the recognition engine on your data.

Decent OCR: https://github.com/tesseract-ocr/tesseract

tesseract-ocr/tesseract: Tesseract Open Source OCR Engine (main repository)