ripl VS grobid

Compare ripl vs grobid and see what are their differences.

ripl

Ripl provides a unified API for 2D graphics rendering in the browser with a focus towards high performance and interactive data visualization. (by andrewcourtice)
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ripl grobid
3 12
308 3,157
- -
8.1 9.2
about 1 month ago 5 days ago
TypeScript Java
MIT License Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

ripl

Posts with mentions or reviews of ripl. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-06.

grobid

Posts with mentions or reviews of grobid. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-06.

What are some alternatives?

When comparing ripl and grobid you can also consider the following projects:

Parsr - Transforms PDF, Documents and Images into Enriched Structured Data

CERMINE - Content ExtRactor and MINEr

Smile - Statistical Machine Intelligence & Learning Engine

science-parse - Science Parse parses scientific papers (in PDF form) and returns them in structured form.

datahub - The Metadata Platform for your Data Stack

Deep Java Library (DJL) - An Engine-Agnostic Deep Learning Framework in Java

Tribuo - Tribuo - A Java machine learning library

s2orc-doc2json - Parsers for scientific papers (PDF2JSON, TEX2JSON, JATS2JSON)

paperai - 📄 🤖 Semantic search and workflows for medical/scientific papers

pdfextract

pandoc - Universal markup converter

hms-ml-demo - HMS ML Demo provides an example of integrating Huawei ML Kit service into applications. This example demonstrates how to integrate services provided by ML Kit, such as face detection, text recognition, image segmentation, asr, and tts.