tesserocr VS python-mss

Did you read the instructions for windows? https://github.com/sirfz/tesserocr

If you’re using tesserocr then you can use OpenCV images directly, so you can just extract the relevant image rows (e.g. query_image = main_image[prev_line:this_line]) and process then without needing to save each image.

This tutorial is a good start towards getting the data from an image of a form with a known structure. I’d personally recommend using tesserocr (actual library binding, more efficient, more functionality) instead of pytesseract (requires images to be saved before processing, uses command-line options in a subprocess instead of binding to the library), but both should work (that tutorial uses pytesseract, which is also what u/Iceberg_Bart_Simpson linked to).

Side note, but I’d suggest using tesserocr over pytesseract. It’s an actual binding to the tesseract library, so comes with numerous efficiency and interface benefits, and can operate on OpenCV images directly (whereas pytesseract saves them to disk first).

If you’re after speed I’d recommend mss for screenshots/recording, and tesserocr instead of pytesseract (note in particular the OpenCV option.

tesserocr is an actual binding to the tesseract library, and is better in practically every way than pytesseract (more efficient, more options for usage, doesn’t require saving images to disk before they can be processed, and more).

If you have an electronically created pdf (not scanned) and you’re just wanting to run OCR on embedded images then you’ll want a pdf library that can extract the figure images for you, and then you can use tesserocr to run OCR on those images.

If you want a suggestion, use tesserocr instead of Pytesseract. It’s an actual binding to the tesseract library (Python talks to it directly, instead of calling a program as a subprocess), which means it runs more efficiently, you can process multiple images sequentially with the same OCR engine (pytesseract has to start a process and a new engine for every image that gets processed), you get access to more functionality options, and a bunch of other beneficial stuff. If you’re doing preprocessing with OpenCV it’s even possible to pass those arrays directly to tesseract in memory, whereas Pytesseract requires that you save each image to a file before it can process it.

As a somewhat unrelated side note, I’d strongly suggest using tesserocr instead of pytesseract, and even more so if you’re working with opencv as well. It’s a true library binding which means it’s more efficient, you have more functionality available to you, you can process multiple images with the same Tesseract engine, and you can process opencv images directly (compared to pytesseract which saves them as a file first and then calls the tesseract CLI as a subprocess).

In Python the library PyTesseract constructs a command to run and calls Tesseract via the command-line as a subprocess, which is inefficient if you have more than one image to process, because it has to reinitialize the OCR engine for every image. tesserocr is a different library which came around a bit later, which is a direct binding to the Tesseract library, so you can initialise the engine once and process several images with it, and for images that are stored in memory (e.g. OpenCV arrays that you’ve done some processing on) you can process them directly instead of having to save them as individual files (which PyTesseract requires).

VidGear provides an easy-to-use, highly extensible, Multi-Threaded + Asyncio Framework on top of many state-of-the-art specialized libraries like OpenCV, FFmpeg, ZeroMQ, picamera, starlette, streamlink, pafy, pyscreenshot, aiortc and python-mss at its backend, and enable us to flexibly exploit their internal parameters and methods, while silently delivering robust error-handling and real-time performance.

I'm using a python library called mss https://github.com/BoboTiG/python-mss

You can use PyGetWindow to get the info of a window based on its name, and then use your screenshot library of choice to get that part of the screen (personally I use mss because it’s fast and cross-platform).

mss can do this efficiently and cross-platform. What’s the use-case?

That was one of the main reasons I made my library pythonic-cv - I had solved the problem once (not super well), and realised I was going to have to do it again repeatedly, and thought that was a waste of time. While I was at it I added some additional nice features like iterating over a camera/video stream, using a context manager to handle cleaning it up/releasing it afterwards, and convenient processing and threaded recording loops. I've recently also added mss integration so you can use your screen as a video input in the same way as a camera or video file.

You may also want to consider using mss for taking screenshots efficiently, which allows specifying the screen region you want when taking the screenshot. It has OpenCV/numpy support.

That error shouldn't be happening, and is most likely some kind of error in mss. If you haven't already, I'd suggest trying to update your mss (e.g. python3 -m pip install --upgrade mss, and if that doesn't help then you should create an Issue on the GitHub page, mentioning your OS and what you're doing that's causing that error.

If you’re after speed I’d recommend mss for screenshots/recording, and tesserocr instead of pytesseract (note in particular the OpenCV option.

Your blog post claims to do image preprocessing with pyautogui, but I couldn’t find any image processing steps? Also, if you’re only using pyautogui to take screenshots, you might want to look at a library like mss, which is more efficient and also has support for multiple monitors :-)