Document_Parser_using_AI

• Marker

  • Link: https://github.com/VikParuchuri/marker

• Docling by IBM

  • Links:
    • https://ds4sd.github.io/docling/v2/
    • https://github.com/DS4SD/docling

• LLMWhisperer

  • LLMs are powerful, but their output is as good as the input you provide. LLMWhisperer is a technology that presents data from complex documents (different designs and formats) to LLMs in a way that they can best understand.
  • Benefits: Process up to 100 pages a day completely free! No credit card required.
  • Link: https://unstract.com/llmwhisperer/

• Llamaparse

  • LlamaParse: Transform unstructured data into LLM optimized formats
    • The best genAI-native parsing platform built specifically to transform complex documents (with tables, charts, images, flow diagrams etc) into clean data for LLM applications.
  • Links:
    • https://github.com/run-llama/llama_parse
    • https://docs.llamaindex.ai/en/stable/llama_cloud/llama_parse/
    • https://www.llamaindex.ai/llamaparse

• PyPDF4LLM

  • PyMuPDF4LLM, competes popular tools like Unstructured and LlamaParse for PDF processing in LLMs.
  • Unique features, from handling multi-column layouts to seamless integration with LlamaIndex
  • Precise PDF data extraction
  • Build better and efficient RAG.

• Microsoft Florence-2

  • Link:
    • https://huggingface.co/microsoft/Florence-2-large

Other references:

• https://github.com/Nutlope/llama-ocr?tab=readme-ov-file
• https://github.com/getomni-ai/zerox
• https://www.analyticsvidhya.com/blog/2024/05/document-parsing-with-llamaparse/
• https://github.com/VikParuchuri/marker