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Scikit Image VS KeptPDF

Compare Scikit Image VS KeptPDF and see what are their differences

Scikit Image

scikit-image is a collection of algorithms for image processing.

Rating
0 reviews
Pricing
Open source
Promote on VibeVoted
Alternatives Is it good?
KeptPDF

Redact, edit, OCR, and sign PDFs entirely in your browser. The file never leaves your device. Free, no account needed.

Rating
0 reviews
Pricing
Freemium $29 / Monthly (Pro, 1 user)
1 VibeVoted session
Alternatives Is it good?
boldrouter
A unified, OpenAI-compatible gateway to every major LLM provider. One endpoint, one key, one bill - with automatic routing and failover. sponsored

Which is more popular?

Based on our record, Scikit Image seems to be more popular. It has been mentioned 7 times since March 2021.

social mentions
7 vs 0
Data Science And Machine Learning popularity
100% vs 0%
alternatives listed
46 vs 38

Base details

Website, pricing, platforms and company facts side by side.

Scikit Image
KeptPDF
Website scikit-image.org keptpdf.com
Pricing
Open source
Freemium $29 / Monthly (Pro, 1 user) Official pricing
Company — Startup from the United States · 2026
Listed in
Image Analysis OCR Machine Learning Data Science And Machine Learning +1
PDF Tools Pdf Redaction OCR PDF Editor PDF Security & Privacy PDF Readers And Editors AI Tools +5

About Scikit Image and KeptPDF

In their own words, as submitted to SaaSHub.

Scikit Image
KeptPDF

No description of Scikit Image yet.

KeptPDF is a PDF toolkit that runs entirely in your browser. Nothing uploads: every tool runs on your own device, so the document never touches a server. What it does: Redaction that auto-detects names, SSNs, phone numbers, addresses, and dates, then removes the underlying text instead of drawing...

Read more about KeptPDF

Features and specs

What each product offers, as listed by its team.

Scikit Image 5 features
KeptPDF 5 features
  • Open Source
    Scikit-Image is open-source and free to use, making it accessible for individuals and organizations without licensing costs.
  • Integration with NumPy
    Scikit-Image is built on top of NumPy, allowing it to seamlessly integrate with a wide range of scientific Python libraries for efficient data processing.
  • Comprehensive Documentation
    The library offers extensive and well-documented resources, tutorials, and examples that help users to understand and implement various image processing tasks.
  • Wide Range of Algorithms
    It provides a large set of optimized algorithms for common image processing tasks like filtering, segmentation, and edge detection.
  • Active Community
    Scikit-Image has a supportive and active community, contributing to its constant growth and the addition of new features and improvements.

Possible disadvantages

  • Performance Limitations
    For very large images or performance-intensive tasks, Scikit-Image may not match the performance of specialized image processing libraries written in lower-level languages.
  • Steep Learning Curve for Beginners
    While well-documented, the wide range of options and flexibility can be overwhelming for beginners starting with image processing.
  • Limited Real-Time Processing
    Scikit-Image is not designed for real-time image processing applications, which can be a drawback for tasks requiring quick processing times.
  • Dependency on Python
    Being a Python library, it's limited to Python's ecosystem, which means users who are not familiar with Python might face a learning barrier.
  • Simple PDF Editing
    KeptPDF offers straightforward tools for editing, merging, splitting, and converting PDF files, making it accessible for users who need quick document management without a steep learning curve.
  • Web-Based Accessibility
    Being a web-based tool, KeptPDF can be accessed from any device with an internet connection, eliminating the need for software installation and allowing use across different operating systems.
  • Multiple File Format Support
    The platform supports conversion between PDF and various other file formats, offering flexibility for users who work with documents in different formats.
  • No Installation Required
    Since it operates in a browser, users can save storage space on their devices and avoid compatibility issues that sometimes arise with desktop software installations.
  • Quick Processing
    KeptPDF is designed to process PDF tasks efficiently, allowing users to complete common document tasks like compression or conversion in a relatively short amount of time.

Videos

Walkthroughs and reviews on video.

Scikit Image 1 video + Add
KeptPDF 0 videos + Add

Image analysis in Python with scipy and scikit image 1 | SciPy 2014 | Juan Nunez Iglesias, Tony Yu

No KeptPDF videos yet. You could help us improve this page by suggesting one.

Category popularity

How often each product is chosen within a category, 0–100% relative to the other.

Score bands 0–20 21–40 41–50 51–60 61–100
Scikit Image
KeptPDF
100% 100%
Data Science And Machine Learning
0% 0%
0% 0%
PDF Tools
100% 100%
100% 100%
Image Processing And Management
0% 0%
0% 0%
PDF
100% 100%

Questions & Answers

As answered by people managing Scikit Image and KeptPDF.

What makes your product unique?

KeptPDF's answer:

KeptPDF runs entirely in your browser. Your file, and the text inside it, never leaves your device. Not to an AI, not even to us. You can open your browser's network tab and watch: the document bytes never go out.

Most online PDF tools upload your file to a server first, and some "AI redaction" services quietly send your document text to a third-party model. That is the exact risk people are trying to avoid when they redact something.

KeptPDF also does true redaction. The text is removed from the file, not covered with a black box you can copy out later. Every redaction produces a verification certificate you can share with the file.

Why should a person choose your product over its competitors?

KeptPDF's answer:

Three reasons.

Privacy you can verify, not just a promise. Processing happens locally in the browser, so there is no upload step to trust. We do send anonymous usage counts for quota, and we say so plainly, but never your document.

Real redaction with proof. Removed text is permanently gone, and you get a certificate showing the file was checked for leftover extractable text. Automated detection cannot catch everything, so a final human review is still your job, and the tool says that too.

It works on a phone. Most PDF suites assume a desktop. KeptPDF was built and used on a phone first, so redacting a document while you are standing in a hallway actually works.

How would you describe the primary audience of your product?

KeptPDF's answer:

Anyone who has to hand a document to someone else and needs the sensitive parts gone first.

In practice that is solo attorneys and small law firms, accountants and tax preparers, healthcare and records staff handling requests, HR teams, and individuals dealing with their own medical, legal, or financial paperwork.

The common thread is not an industry. It is a person who cannot upload a confidential file to a random website, and who does not have an enterprise IT budget to solve it.

What's the story behind your product?

KeptPDF's answer:

A family member got seriously ill. We spent most days at the hospital, and straight answers were hard to come by, so we leaned on AI tools to make sense of the records, notes, and lab results.

But you cannot paste a medical record into an AI chat. You have to strip the names, the ID numbers, the diagnoses first. And almost every tool we found either wanted to upload the whole file to a server, or "auto-redacted" by sending the document text to an online AI. That was the exact thing we were trying to avoid.

Most of this was happening on a phone, at a bedside. So I built the tool I needed: redaction that runs on the device, works on mobile, and never sends the file anywhere. That turned into KeptPDF, which is now a full PDF suite with over 20 tools, all local-first.

Which are the primary technologies used for building your product?

KeptPDF's answer:

The app is plain JavaScript with no front-end framework, which keeps it fast and keeps the code auditable.

PDF work happens in the browser using pdf.js for rendering and pdf-lib for writing. Text recognition uses Tesseract running as WebAssembly. Password and encryption handling uses a WebAssembly build of qpdf. It is a Progressive Web App, so it installs and works offline.

The thin server side is Node on Vercel, with Postgres for accounts, Stripe for billing, and Resend for email. None of those ever see a document.

Who are some of the biggest customers of your product?

KeptPDF's answer:

KeptPDF is early and independent, and we do not publish customer names. The product is privacy-first by design: we never see your documents, and we do not track who our users are or what they work on. Publishing a client list would sit badly next to that.

The user base today is mostly solo attorneys, small firms, accountants, and individuals handling their own records.

User comments

Share your experience with using Scikit Image and KeptPDF. For example, how are they different and which one is better?

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Reviews and articles

External articles and on-site reviews we used to compare the two products.

Scikit Image no reviews yet
KeptPDF no reviews yet

We have no reviews of KeptPDF yet. Be the first one to post

Social recommendations and mentions

Recommendations tracked on public social media and blogs since March 2021.

Scikit Image 7 mentions
KeptPDF 0 mentions
  • How to Estimate Depth from a Single Image
    We will use the Hugging Face transformers and diffusers libraries for inference, FiftyOne for data management and visualization, and scikit-image for evaluation metrics. - Source: dev.to / over 2 years ago
  • Exploring Open-Source Alternatives to Landing AI for Robust MLOps
    Data analysis involves scrutinizing datasets for class imbalances or protected features and understanding their correlations and representations. A classical tool like pandas would be my obvious choice for most of the analysis, and I... - Source: dev.to / almost 3 years ago
  • Is it possible to add a noise to an image in python?
    This is a good cv deep learning book with python examples https://www.manning.com/books/deep-learning-for-vision-systems. If you're pretty comfortable with the concepts of traditional image processing this is a good companion to cv2 (so... Source: almost 4 years ago

View more

Tracking KeptPDF since Aug 2026.

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