Desktop Annotation Software



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(Redirected from List of Manual Image Annotation Tools)

Desktop Annotation Tool

Manual image annotation is the process of manually defining regions in an image and creating a textual description of those regions. Such annotations can for instance be used to train machine learning algorithms for computer vision applications.

Desktop SuperAnnotate Desktop is the fastest image and video annotation software. It was built based on SuperAnnotate's web platform which is designed based on feedback from thousands of annotators that have spent hundreds of thousands of hours on labeling. In Windows 7, you can use Paint to annotate images. Open your image Windows Paint and select the Text Tool (The icon with the capital A) This will reveal the toolbar with all of the options. Let us know if you have more questions. Whiteboard software can be classified into many categories, such as simple drawing software, interactive whiteboard software, collaboration whiteboard software, desktop whiteboard or annotation software, virtual whiteboard software, etc. PDF Annotator is a powerful program that allows users to make a variety of different kinds of notes on PDF.

This is a list of computer software which can be used for manual annotation of images.

Desktop annotation tool

The Worlds leading desktop marker software. Epic Pen is an easy to use yet powerful desktop annotation tool for windows. You can draw, write and highlight directly over most Windows desktop applications, including presentation software, webpages, videos, universal annotation. The Worlds leading desktop marker software. Epic Pen is an easy to use yet powerful desktop annotation tool for windows. You can draw, write and highlight directly over most Windows desktop applications, including presentation software, webpages, videos, universal annotation tool, creative studios and even games.

Desktop Annotation Software
SoftwareDescriptionPlatformLicenseReferences
OCLAVIObject Classification and Annotation for Computer Vision Models(OCLAVI) is an AI driven cloud annotation platform. OCLAVI provides free forever accounts along with on-premise or hybrid installation for enterprise customers. The app supports bounding box, polygon, cuboid, landmark annotation for images and videos. OCLAVI is a free to use collaborative annotation tool.Javascript, HTML, CSS, Node.jsMIT License[1][2][3]
SuperAnnotateSuperAnnotate is an AI-powered annotation platform for computer vision. As part of their web solution, SuperAnnotate provides a free-to-use desktop app which can be installed on Mac, Linux and Windows. The desktop app supports not only box and polygon annotations, but also less mainstream annotation tools such as points, polylines, ellipses and cuboids. It can also be used for any types of video tagging and object tracking applications by using the video slidebar together with copy/paste functionality. Allowing users to filter the classes they are interested in, the desktop app makes the quality assurance process to be at least 3x faster. The tool was released as part of their strategic partnership with OpenCV.ElectronJS, MacOS, Linux and WindowsMIT License[4][5][6][7][8]
Computer Vision Annotation Tool (CVAT)Computer Vision Annotation Tool (CVAT) is a free, open source, web-based annotation tool which helps to label video and images for computer vision algorithms. CVAT has many powerful features: interpolation of bounding boxes between key frames, automatic annotation using TensorFlow OD API and deep learning models in Intel OpenVINO IR format, shortcuts for most of critical actions, dashboard with a list of annotation tasks, LDAP and basic authorizations, etc. It was created for and used by a professional data annotation team. UX and UI were optimized especially for computer vision annotation tasks.JavaScript, HTML, CSS, Python, DjangoMIT License[9][10][11]
ImageTaggerAn online platform for collaborative image labeling. It allows bounding box, polygon, line and point annotations and includes user, image and annotation management, annotation verification and customizable export formats.Python (Django), JavaScript, HTML, CSSMIT License[12][13][14][15][16][17][18]
LabelMeOnline annotation tool to build image databases for computer vision research.Perl, JavaScript, HTML, CSS[19]MIT License[20]
RectLabelAn image annotation tool to label images for bounding box object detection and segmentation.[21]macOSCustom License[20][22]
VGG Image Annotator (VIA)VIA is a simple and standalone manual annotation tool for images, audio and video. This is a light weight, standalone and offline software package that does not require any installation or setup and runs solely in a web browser. The VIA software allows human annotators to define and describe spatial regions in images or video frames, and temporal segments in audio or video. These manual annotations can be exported to plain text data formats such as JSON and CSV and therefore are amenable to further processing by other software tools. VIA also supports collaborative annotation of a large dataset by a group of human annotators. The BSD open source license of this software allows it to be used in any academic project or commercial application.[23]JavaScript, HTML, CSS[24]BSD-2 clause license[23][25][26]
VoTT (Visual Object Tagging Tool)Free and open source electron app for image annotation and labeling developed by Microsoft.TypeScript/Electron (Windows, Linux, macOS)MIT License[27][28][29][30][31][32]

References[edit]

  1. ^'OCLAVI'. m14intelligence.com. Retrieved 2020-09-24.
  2. ^'Data Annotation in the Autonomous Vehicle Sector: Market Estimation and Forecasts to 2026'. globenewswire.com. Retrieved 2020-06-12.
  3. ^'Autonomous Vehicle Data Annotation Market Analysis 2020, Featuring Key Players CMORE Automotive, Cogito Tech, Samasource, Inc. and Dataloop'. yahoo.com. Retrieved 2020-06-10.
  4. ^'SuperAnnotate'. opencv.org. Retrieved 2020-11-17.
  5. ^'SuperAnnotate Desktop'. opencv.org. Retrieved 2020-11-17.
  6. ^opencv-ai/superannotate, OpenCV.AI, 2020-11-15, retrieved 2020-11-17
  7. ^'9 Best Data Labeling Tools for Machine Learning Projects'. Lionbridge AI. 2020-11-06. Retrieved 2020-11-17.
  8. ^'SuperAnnotate uses AI techniques to speed up data labeling'. VentureBeat. 2020-06-11. Retrieved 2020-11-17.
  9. ^'Intel open-sources CVAT, a toolkit for data labeling'. VentureBeat. 2019-03-05. Retrieved 2019-03-09.
  10. ^'Computer Vision Annotation Tool: A Universal Approach to Data Annotation'. software.intel.com. 2019-03-01. Retrieved 2019-03-09.
  11. ^'Computer Vision Annotation Tool (CVAT) source code on github'. Retrieved 3 March 2019.
  12. ^'ImageTagger source code on github'. Retrieved 25 July 2020.
  13. ^Marzahl, C.; Aubreville, M.; Bertram, C. (2020), EXACT: A collaboration toolset for algorithm-aided annotation of almost everything, arXiv:2004.14595
  14. ^Fiedler, N.; Bestmann, M.; Hendrich, N. (2018), ImageTagger: Open Source Online Platform for Image Labeling
  15. ^WF Wolves – Humanoid KidSizeTeam Description for RoboCup 2020(PDF), retrieved 26 July 2020
  16. ^24 Best Image Annotation Tools for Computer Vision, retrieved 26 July 2020
  17. ^Scheunemann, M.; van Dijk, S.; Miko, R. (2019), Bold HeartsTeam Description for RoboCup 2019, arXiv:1904.10066
  18. ^Bator, M.; Maciej, P. (2019). 'Image Annotating Tools for Agricultural Purpose: a Requirements Study'(PDF). Machine Graphics and Vision. 28.
  19. ^'LabelMe Source'. Retrieved 26 January 2017.
  20. ^ ab'Reducing the Pain: A Novel Tool for Efficient Ground-Truth Labelling in Images'(PDF). Auckland University of Technology. Retrieved 2018-10-13.
  21. ^'RectLabel support page'. Retrieved 29 March 2017.
  22. ^'Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images'. The University of Tokyo Hospital. Retrieved 2018-07-04.
  23. ^ abDutta, Abhishek; Zisserman, Andrew (2019). 'The VIA Annotation Software for Images, Audio and Video'. Proceedings of the 27th ACM International Conference on Multimedia: 2276–2279. arXiv:1904.10699. Bibcode:2019arXiv190410699D. doi:10.1145/3343031.3350535. ISBN9781450368896. S2CID173188066.
  24. ^'Visual Geometry Group / via'. GitLab. Retrieved 2019-02-05.
  25. ^'Easy Image Bounding Box Annotation with a Simple Mod to VGG Image Annotator'. Puget Systems. Retrieved 2019-02-05.
  26. ^Loop, Humans in the (2018-10-30). 'The best image annotation platforms for computer vision (+ an honest review of each)'. Hacker Noon. Retrieved 2019-02-05.
  27. ^Tung, Liam. 'Free AI developer app: IBM's new tool can label objects in videos for you'. ZDNet.
  28. ^Bornstein, Aaron (Ari) (February 4, 2019). 'Using Object Detection for Complex Image Classification Scenarios Part 4:'. Medium.
  29. ^Solawetz, Jacob (July 27, 2020). 'Getting Started with VoTT Annotation Tool for Computer Vision'. Roboflow Blog.
  30. ^'Best Open Source Annotation Tools for Computer Vision'. www.sicara.ai.
  31. ^'Beyond Sentiment Analysis: Object Detection with ML.NET'. September 20, 2020.
  32. ^'GitHub - microsoft/VoTT: Visual Object Tagging Tool: An electron app for building end to end Object Detection Models from Images and Videos'. November 15, 2020 – via GitHub.
Retrieved from 'https://en.wikipedia.org/w/index.php?title=List_of_manual_image_annotation_tools&oldid=1000313402'

OpenCV is happy to introduce a participant of our Silver Membership Program – SuperAnnotate! As a part of our partnership, they are launching a free annotation tool for the Computer Vision community and telling why this product is worth your attention.

About the author:

Vahan Petrosyan is the co-founder and CTO of SuperAnnotate. Before SuperAnnotate, Vahan was working on his Ph.D. research in image segmentation at KTH Royal Institute of Technology, Sweden. The algorithms he developed during his studies became the basis of SuperAnnotate’s platform. The growing demand for his technology in various computer vision applications eventually led him to drop out of his Ph.D. program to start the company.

In this post, I will be introducing SuperAnnotate’s new free-to-use desktop app, discuss some of the reasons why we built it, and share more about many of the features which we feel will dramatically increase the speed, accuracy, and efficiency of annotation projects. There is a massive functionality gap between free and commercial image annotation tools. SuperAnnotate Desktop is closing this gap by providing the fastest all-inclusive software tool for computer vision engineers to complete their annotation tasks.

The World of Free Image Annotation Tools

Instead of writing a rather long introduction on the universe of free image annotation tools, I will quickly summarize many of the well-written articles, blogs, and websites covering the topic. Probably the most informative website discussing free tools is Awesome Open Source, which ranks open source tools based on the number of GitHub stars each tool has received. The list for image annotation tools can be found here. According to the list, it becomes apparent that CVAT (managed by Intel) and VOTT (managed by Microsoft) are among the most popular free tools for image annotation. There are several other interesting articles that include CVAT and VOTT among the best annotation tools available for free. Here are a few examples: Bohemian.ai, Sicara.ai, Wikipedia.

These articles are wonderful resources, and I strongly recommend reading them to learn about the different tools available and even try some of them if you have the time. However, what you soon start to realize is that free tools are lacking in many areas resulting in slow speeds, disjointed project management, and overall non-intuitive user experience – especially when you consider what we’ve come to expect from software today.

Introducing SuperAnnotate Desktop

The founding team of SuperAnnotate (my brother and I) were PhD students in biomedical imaging and computer vision, respectively. During the course of our PhDs, we spent a considerable amount of time working with images, particularly with annotations. In 2018, free annotation tools were as incredibly inconvenient as they are today, and it was quite painful using them. They were not only extremely slow and clunky, but also lacked many key annotation functionalities. These pains led us to launch SuperAnnotate.

Since founding SuperAnntotate, we have always been focused on releasing software that is lightning-fast, easy to use, and extremely functional for all types of computer vision tasks. Over the last two years, we’ve worked hard to build what we think is the fastest and most efficient annotation platform for computer vision pipelines. And, as we came from academia, we also wanted to make a version of our platform easily installable and free for anyone, to help eliminate many of the pains my brother and I faced as PhD students.

Back in June we announced our partnership with OpenCV to bring a free annotation tool to the broader computer vision community that is a significant upgrade over the current free tools available.

A few days ago we released software for Mac, Windows and Linux users. Despite being the initial release, the software already provides multiple advanced features that will accelerate your labeling process by 3–5x. You can download it here.

We will keep updating our desktop app on a monthly basis and would love to get the community’s feedback on features you all like, as well as the ones that are missing. We’re excited to be a bigger part of the OpenCV community, and to help provide the best computer vision tools to its members.

Eight reasons why you should use SuperAnnotate Desktop

In this section, I will do a deeper dive into some of the features that make our app unique compared to some of the most popular alternatives. As I mentioned above, the paid version of our platform is focused on delivering lightning-fast speed, robust workflows, and a delightful user experience. We tried to bring that focus (and a few of the features) into our desktop app. Here we go:

Note: I strongly recommend watching the video below which summarizes all these components.

1. Born out of SuperAnnotate’s Core Platform

We’ve spent the last two years and we have invested hundreds of thousands of engineering hours and millions of dollars on the core web version of SuperAnnotate, building what we feel is the fastest and most efficient annotation platform for computer vision. It incorporates feedback from annotators working hundreds of thousands of hours in the web version of our platform as well. This has allowed us to deliver our desktop editor with some of the designs, features, and refinements from our core product offering. We hope the result is a 100% free product that is delightful, feature-rich, and professional grade.

Best Annotation Software

2. Advanced polygon tool

 Polygon annotation is often the most time-consuming annotation task. Anyone who has tried free annotation tooling knows how poor the experience can be. We made several additions to traditional polygon tools in order to make manual polygon creation and editing much faster. Some of these features include:

  • Pen-polygon tool— use the polygon as a pen making curved annotations much faster
  • Point addition/removal Addand remove polygon points with just a couple of clicks
  • Edit polygon — Substantially increase the speed of editing polygons with our pen polygon tool
  • Share polygon boundaries — draw polygons with shared boundaries 2x faster than traditional tools
  • Polygon move/group/delete — select, drag, drop or delete individual or groups of polygons wherever you want

These are just some of the features that allow us to reduce polygon annotation times by 20-60% while making polygon annotations significantly more accurate.

3. Filtering

Most annotation tools lack the ability to filter images. Yet we have found that class filtering has a dramatic impact on speeding up the annotation review process. Through SuperAnnotate’s filtering menu, users can display only images with certain classes they are interested in reviewing, avoiding the need to comb through all of the images and saving tremendous amounts of time.

4. Tracking multiple objects between frames

Tracking multiple objects between consecutive frames can dramatically improve the annotation experience while also making annotating much faster. Our desktop app allows users to select multiple objects and perform operations such as move, delete, group, copy, paste, and duplicate. Users can copy and duplicate annotations in successive frames while keeping the same attribute ID so that a particular attribute can be tracked through multiple frames easily.

Desktop Annotation Software Download

5. Huge list of shortcuts

Gamers and power users of tools like excel and photoshop know how a robust list of shortcuts can both improve the user experience and add considerable speed. That was why we made a huge list of shortcuts for actions like tool selection, on-screen navigation, copy/paste/group/ungroup objects, switching between frames, and others. All shortcuts take place on the left side of the keyboard (similar to gaming), so your right hand can stay focused on the mouse, and your left hand does not have to move while finding the right shortcut.

6. Labeling Flexibility

Current platforms (both free and paid) limit you to one labeling workflow: you set the attributes and then draw the shapes. Oftentimes, it can be significantly more efficient to have different workflows such as drawing shapes first, or copying classes between instances. With SuperAnnotate, we allow for a wide range of labeling workflows, giving users the flexibility they need to be most efficient.

7. Classes/attributes/point labels

Creating, adding, or deleting classes and attributes is made very simple in the SuperAnnotate desktop app. Users can easily import classes from previous projects saving the time needed to define projects. In addition, we allow users to annotate individual points with free text. This can have multiple uses such as, describing the object by a sentence, giving a tag to the object, or describing the specific point in the polygon (e.g. rear-right wheel).

8. Leveling up your annotations

Desktop Annotation Software Free

As your annotation needs increase, you will likely find yourself looking for things like increased automations, ML features, more robust project management, detailed quality assurance, team collaboration, and user roles. You might also find yourself needing outsourced annotation teams. At SuperAnnotate, we can satisfy all of these needs and much more via our core platform. Our core platform leverages ML and workflow-based features to help computer vision teams increase annotation speed by up to 10x, while dramatically improving the quality of training data and increasing the efficiency of managing annotation projects. We also have integrated services on the platform, giving customers the ability to access thousands of professionally managed outsourced annotators armed with our lightning-fast tooling. If you are interested in learning more about our core platform and services, please fill out this form.

Importing annotations from other platforms or open-source tools

Free Annotation Software

Migrating to SuperAnnotate from other software is something important to our customers. This was a common request from our users as many of them wanted to use our platform to quality check their previous work and transition over from other tools. We’ve made it super easy to import annotated data from other annotation tools using only a few lines of code, which I’ve described below. Then, once in our platform, users can leverage features described above like filtering and advanced polygon editing to easily perform QA on previous annotation work and check prediction accuracy.

To see how easy it is to migrate previous annotations, I have included the full code required.. (Note that you can transfer your annotations not only from other open-source tools but also from other paid platforms.) For the conversion, you can use our SDK where we provide all the conversion scripts to make an easy transition. Below is an example from Labelbox, but it can be applied to other platforms such as Amazon SageMaker, Google Cloud AutoML, Scale AI, VOTT, etc.

First, install the SDK and the supplementary repositories:

The use the script below to convert your data to SuperAnnotate format:

Once you receive the converted json file in SuperAnnotate format, you can simply upload annotations in our editor.

If you are interested in our premium features, you can bring the images and clean the annotations in our web-based platform, then automate the annotation of the next set of images using transfer learning. Please refer to this article and learn how to do it without writing a single line of code.

The Future of SuperAnnotate Desktop

Annotation Download

The goal of SuperAnnotate Desktop is to provide fast and intuitive tools for academic researchers or solo annotators and save precious time during the annotation process. In the future, we will update the software on a monthly basis, and provide features that address our community’s most important pain points. Therefore, we encourage our community to be more active and open such issues on our GitHub page.