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Show HN: Documind – Open-source AI tool to turn documents into structured data

(github.com)
168 points Tammilore | 2 comments | 18 Nov 24 10:51 UTC | HN request time: 0.499s | source

Documind is an open-source tool that turns documents into structured data using AI.

What it does:

- Extracts specific data from PDFs based on your custom schema - Returns clean, structured JSON that's ready to use - Works with just a PDF link + your schema definition

Just run npm install documind to get started.

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danbruc ◴[18 Nov 24 13:02 UTC] No.42171959[source]▶
With such a system, how do you ensure that the extracted data matches the data in the source document? Run the process several times and check that the results are identical? Can it reject inputs for manual processing? Or is it intended to be always checked manually? How good is it, how many errors does it make, say per million extracted values?
replies(1): >>42172472 #
1. glorpsicle ◴[18 Nov 24 14:18 UTC] No.42172472[source]▶
Perhaps there's still value in the documents being transformed by this tool and someone reviewing them manually, but obviously the real value would be in reducing manual review. I don't think there's a world–for now–in which this manual review can be completely eliminated.

However, if you process, say, 1 million documents, you could sample and review a small percentage of them manually (a power calculation would help here). Assuming your random sample models the "distribution" (which may be tough to define/summarize) of the 1 million documents, you could then extrapolate your accuracy onto the larger set of documents without having to review each and every one.

replies(1): >>42174980 #
2. danbruc ◴[18 Nov 24 17:58 UTC] No.42174980[source]▶
You can sample the result to determine the error rate, but if you find an unacceptable level of errors, then you still have to review everything manually. On the other hand, if you use traditional techniques, pattern matching with regular expressions and things like that, then you can probably get pretty close to perfection for those cases where your patterns match and you can just reject the rest for manual processing. Maybe you could ask a language model to compare the source document and the extracted data and to indicate whether there are errors, but I am not sure if that would help, maybe what tripped up the extraction would also trip up the result evaluation.