ProdigyExplosion
|
PyMuPDFArtifex
|
|||||
Related Products
|
||||||
About
Radically efficient machine teaching. An annotation tool powered by active learning. Prodigy is a scriptable annotation tool so efficient that data scientists can do the annotation themselves, enabling a new level of rapid iteration. Today’s transfer learning technologies mean you can train production-quality models with very few examples. With Prodigy you can take full advantage of modern machine learning by adopting a more agile approach to data collection. You'll move faster, be more independent and ship far more successful projects. Prodigy brings together state-of-the-art insights from machine learning and user experience. With its continuous active learning system, you're only asked to annotate examples the model does not already know the answer to. The web application is powerful, extensible and follows modern UX principles. The secret is very simple: it's designed to help you focus on one decision at a time and keep you clicking – like Tinder for data.
|
About
PyMuPDF is a high-performance, Python-centric library for reading, extracting, and manipulating PDFs with ease and precision. It enables developers to access text, images, fonts, annotations, metadata, and structural layout of PDF documents, and to perform tasks such as extracting content, editing objects, rendering pages, searching text, modifying page content, and manipulating PDF components like links and annotations. PyMuPDF also supports advanced operations like splitting, merging, inserting, or deleting pages; drawing and filling shapes; handling color spaces; and converting between formats. The library is lightweight but robust, optimized for speed and low memory overhead. On top of the base PyMuPDF, PyMuPDF Pro adds support for reading and writing Microsoft Office-format documents and enhanced functionality for integrating Large Language Model (LLM) pipelines and Retrieval Augmented Generation (RAG).
|
|||||
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
Platforms Supported
Windows
Mac
Linux
Cloud
On-Premises
iPhone
iPad
Android
Chromebook
|
|||||
Audience
Data scientists, AI developers, data labelers
|
Audience
Developers, engineers, or automation teams interested in a solution to extract, render, edit, or convert PDFs in Python-based or cross-platform workflows
|
|||||
Support
Phone Support
24/7 Live Support
Online
|
Support
Phone Support
24/7 Live Support
Online
|
|||||
API
Offers API
|
API
Offers API
|
|||||
Screenshots and Videos |
Screenshots and Videos |
|||||
Pricing
$490 one-time fee
Free Version
Free Trial
|
Pricing
No information available.
Free Version
Free Trial
|
|||||
Reviews/
|
Reviews/
|
|||||
Training
Documentation
Webinars
Live Online
In Person
|
Training
Documentation
Webinars
Live Online
In Person
|
|||||
Company InformationExplosion
Founded: 2016
Germany
prodi.gy/
|
Company InformationArtifex
Founded: 1993
United States
artifex.com/products#pymupdf
|
|||||
Alternatives |
Alternatives |
|||||
|
|
||||||
|
|
||||||
|
|
||||||
|
|
||||||
Categories |
Categories |
|||||
Data Labeling Features
Human-in-the-loop
Labeling Automation
Labeling Quality
Performance Tracking
Polygon, Rectangle, Line, Point
SDK
Supports Audio Files
Task Management
Team Collaboration
Training Data Management
|
||||||
Integrations
.NET
Hugging Face
JavaScript
LangChain
Llama
Make
Microsoft Excel
Microsoft Office 2024
Microsoft PowerPoint
Microsoft Word
|
Integrations
.NET
Hugging Face
JavaScript
LangChain
Llama
Make
Microsoft Excel
Microsoft Office 2024
Microsoft PowerPoint
Microsoft Word
|
|||||
|
|
|