hms-ml-demo VS grobid

GitHub - HMS-Core/hms-ml-demo

For more details, you can go to: ML Kit official website ML Kit Development Documentation page, to find the documents you need Reddit to join our developer discussion GitHub to download ML Kit sample codes Stack Overflow to solve any integration problems

Download Demo: https://github.com/HMS-Core/hms-ml-demo/tree/master/MLKit-Sample/module-vision/src/main/java/com/huawei/mlkit/sample/activity/imageclassfication

Get the code from GitHub:https://github.com/HMS-Core/hms-ml-demo/tree/master/ApplicationCases/CrazyChristmas

Interesting concept. Grobid tries to do the same https://github.com/kermitt2/grobid

I would suggest using grobid - a pipeline for extracting scientific PDFs into a common XML format which can be easily parsed. Grobid has quite a nice mature REST API that I've used in some of my own projects. It parses references and matches them to their DOI using the CrossRef API with a reported 95% F1 score. This should make your job pretty simple as far as I can tell - all you'd need to do is run your papers through grobid and then build a citation graph by comparing document DOIs.

Just use science-parse or GROBID. They have been designed for that exact reason.

- I ended up using Grobid, which converts the PDF to a very detailed XML format. The format is not a word processing format though, but a format specifically for representing scientific documents. I don't know, if it would, for example, contain tags about bold or italicized text. The tool is working really well, but since you probably cannot use the output XML format directly, it will need some postprocessing, which would be relatively simple with XML parsing libraries.

A few years ago I evaluated a few open source tools. In the end focused on GROBID. As usual, it depends on the type of document whether it works well for your use-case. There is some focus on it being "fast" (if that is a concern).