blackmaria VS pix2struct

I have also seen several tools that try to use LLM to do web scraping. I didn't look into the details. https://www.reddit.com/r/MachineLearning/comments/12v0vda/p_i_built_a_tool_that_autogenerates_scrapers_for/ https://github.com/Smyja/blackmaria

Here are a few useful resources you could start with: [Pix2Struct by Google Research](https://github.com/google-research/pix2struct) might be a valuable tool, although it will most likely need some fine-tuning to fit your specifics. You can also find some fine-tuned models on HuggingFace by searching 'pix2struct'. Another option worth considering is [DonutI](https://github.com/clovaai/donut). Like Pix2Struct, fine-tuning likely needed to meet your requirements. Tesseract OCR is another alternative, particularly for handling text. It's primarily designed for pages of text, think books, but with some tweaking and specific flags, it can process tables as well as text chunks in regions of a screenshot. Bit too much tweaking for my taste. As I'm also in search of OCR tools for UI and chart screenshots, so share if you find something else.

For images with text, if you want to do visual qa, document classification, table/key information extraction, checkout https://huggingface.co/blog/document-ai https://github.com/philschmid/document-ai-transformers https://github.com/google-research/pix2struct https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppstructure/README.md

https://huggingface.co/blog/document-ai https://github.com/microsoft/table-transformer https://github.com/google-research/pix2struct https://github.com/PaddlePaddle/PaddleOCR/blob/release/2.6/ppstructure/table/README.md

It is related to document AI. Recently google has released a model pix2struct. Some of the tasks they considered and datasets they used include:

Google has a new model pix2struct that can accept image input(charts, documents, book covers, UI screenshos) and answer questions. https://github.com/google-research/pix2struct