tesseract-ocr VS Face Recognition

You will also need to install the Tesseract OCR engine, which can be downloaded and installed from the following link: https://github.com/tesseract-ocr/tesseract

PyTesseract Module [ Github ] EasyOCR Module [ Github ] PaddlePaddle OCR [ Github ]

It uses teseract for the OCR https://github.com/tesseract-ocr/tesseract

Last update was pretty recent, and the git mentions tesseract 5 as a dep. so it's likely moved on a bit from when you last tried it:

https://github.com/tesseract-ocr/tesseract/releases

I suppose it depends on your use-case. For personal tasks like this it should be more than sufficient, and won't need user details/cc or whatever to use it.

Tesseract is an open-source OCR engine developed by Google. It is highly accurate and supports multiple languages. This library will do all the heavy lifting for us. We'll use it in this tutorial to quickly read the text in some images.

> Does android even have native OCR?

Tesseract? https://github.com/tesseract-ocr/tesseract

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.

OCR detection will be done with 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).

Camera connected to a PI? Something like this could run locally: https://github.com/ageitgey/face_recognition

Have you tried this repo: github

One of the most common challenges is the black-box problem, when the pipeline becomes too complex to understand it would happen. This can make it difficult to identify issues with the system or to understand why it isn't working as we expected or make accurate predictions that saiwa company find out the solution for Face Recognition. Another challenge is the time required for organizations to deploy a machine learning model, which is increasing and make real-time computing difficult . To overcome these challenges, it's important to have an efficient and rigorous ML pipeline . ML level 0 involves a manual process with its own set of challenges, while ML level 1 involves ML pipeline automation and additional components . A well-defined machine learning pipeline can help to abstract the complex process into a series of steps, allowing each team to work independently on specific tasks such as data collection, data preparation, model training, model evaluation, and model deployment.

Second link is an easy to implement python library is you want to build it yourself https://github.com/ageitgey/face_recognition

It is similar to https://github.com/ageitgey/face_recognition, except that Ageitgey's cli only compares the first face found in the image to the first one found the the second.

You'd have alot more luck with something like DLIB or an open source implementation such as: https://github.com/ageitgey/face_recognition

To know more about face_recognition module https://github.com/ageitgey/face_recognition