Very interesting subject. I did some smallish and isolated experiments with seeking coding help from LLMs, so I was curious to see what your "full picture" chat was able to reveal. I have not read every word in the full chat-log but most of them. My thoughts:

• The analysis shows some general understanding of the problem space in general and this project in particular. I'm still in the stage of being able to be impressed by state of the art LLM's abilities in that respect.
• The code the LLM was taking as input is pretty outdated: e.g. there is no LuminanceSource class anymore (for a couple years now)
• The proposed improvements are pretty high level and feel rather "generic".
• The mapping of suggested improvements to some source files or specific C++ classes feels rather useless and superficial.

Are these improvements aligned with the project’s goals?

In general, sure but there are two things that I care about quite a bit: 1) runtime performance, so adding some cpu heavy preprocessing step that helps with e.g. 1% of cases while increasing the runtime 20% for all is probably not going to happen and 2) keeping the dependencies of the library itself to a minimum.

Would maintainers accept PRs for OpenCV-dependent preprocessing?

No, I'm not going to add a mandatory dependency to OpenCV. What might be on the table is an optional / configurable dependency. But even for that I'd need to be convinced that this actually improves the detection rate significantly. And if some specific algorithm is able to achieve that, I might be inclined to simply implement that from scratch.

That said, it is rather trivial to do any OpenCV based preprocessing before calling ReadBarcodes. Please see also this example code. If you make any experiments with OpenCV and find something helpful, please let me know.

Are there existing efforts to address these issues?

• I did some experiments with Otsu in the past, that did not result in a substantial improvement.
• I also spent and unpleasant amount of time on the linear codes with low resolution / lack of sharpness case that did not result in something robust, unfortunately. There a CNN-based segmentation algorithm could have really helped, I guess. The core problem I ended up trying to solve with some manual heuristics was just that. That said, I would question how important that kind of improvement really is nowadays with increasingly high resolution input.
• There is some early experimental code, trying to improve the resilience regarding the deformation of DataMatrix symbols.

I would suggest you pick one specific issue that limits your application the most, describe it here and maybe I have some pointers that would allow you to go back to your LLM of choice and maybe come up with some specific and measurable improvement. And then we can see how we can move on from there.