pdfjam VS tesseract-ocr

Some time ago I had success with https://github.com/rrthomas/pdfjam for that purpose.

It used to be included in the standard Debian repositories but that no longer seems to be the case which might mean it is an unmaintained project that has build issues. Worth trying though, if it builds on a modern system.

There is pdjam which might be a part of what you are looking for.

If you have a standard package manager, look up pdfjam for pretty good CLI editing.

I use https://github.com/rrthomas/pdfjam/ for that since ages in case you're interested in alternatives.

Custom Integration: Developers and businesses needing flexibility for custom integration into applications and projects should consider open-source solutions like Tesseract OCR or API-based services like API4AI OCR. These options provide APIs for seamless integration into existing software systems.

Tesseract OCR is an open-source OCR engine created by Google, known for its accuracy and wide language support. It is particularly favored by developers for its flexibility and the absence of licensing fees, allowing it to be integrated into various applications. However, it demands more effort to set up and utilize compared to cloud-based OCR services.

If you want to learn more visit the complete tesseract documentation.

You can use tesseract

https://tesseract-ocr.github.io/

AI copilots: Copilots powered by various LLMs like Pieces Copilot can leverage computer vision technologies for inputs beyond text and code. For example, optical character recognition software at Pieces uses Tesseract as its main OCR code engine, extended with bicubic upsampling. Pieces then uses edge-ML models to auto-correct any potential defects in the resulting code/text, which users can input as prompts to the AI copilot. Pieces Copilot in its current iteration also comes with a unique tool called the Workstream Pattern Engine which gathers real-time context from any application through computer vision, enabling Pieces to understand everything on your screen and pass it through to the LLM so you can talk to the AI about it.

i used open source solutions to built it, like libreoffice, ghostscript, google's tesseract and a bunch of other tools, Google's Tesseract: https://github.com/tesseract-ocr/tesseract

We are going to be using an OCR (Optical Character Recognition) engine called Tesseract for the image-to-text recognition part. It is free software, released under the Apache License. Install the engine for your desired OS from their official website. I'm using Windows for this. Add the installation path to your environment variables.

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