[constantinum]

The primary issue with LLMs is hallucination, which can lead to incorrect data and flawed business decisions.

For example, Llamaparse(https://docs.llamaindex.ai/en/stable/llama_cloud/llama_parse...) uses LLMs for PDF text extraction but faces hallucination problems. See this issue for more details: https://github.com/run-llama/llama_parse/issues/420.

For those interested, try LLMWhisperer(https://unstract.com/llmwhisperer/) for OCR. It avoids LLMs, eliminates hallucination issues, and preserves the input document layout for better context.

Examples of extracting complex layout:

https://imgur.com/a/YQMkLpA

https://imgur.com/a/NlZOrtX

https://imgur.com/a/htIm6cf

[Hackbraten]

> try LLMWhisperer(https://unstract.com/llmwhisperer/) for OCR. It avoids LLMs

The website you linked says it uses LLMs?

[constantinum]

The tool doesn't use any LLMs for processing/parsing the data. It parses and converts into raw text.

The final output(raw text) of the parsing is then fed to LLMs for data extraction. e.g. Extracting data from insurance, banking, and invoice documents.

[ungerik]

Those images look exactly like what you get from every OCR tool out there if you use the XY information.