List of manual image annotation tools

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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.

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

Software Description Platform License References
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, Django MIT License [1][2][3]
dLabel dLabel is a free and online image and video annotation tool to label your datasets for computer vision and AI projects. Python, JavaScript, HTML, CSS Custom License
ImageTagger An 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, CSS MIT License [4][5][6] [7][8][9][10]
LabelMe Online annotation tool to build image databases for computer vision research. Perl, JavaScript, HTML, CSS[11] MIT License [12]
RectLabel An image annotation tool to label images for bounding box object detection and segmentation.[13] macOS Custom License [12][14]
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.[15] JavaScript, HTML, CSS[16] BSD-2 clause license [15][17][18]
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 [19][20][21][22][23][24]

References[edit]

  1. ^ "Intel open-sources CVAT, a toolkit for data labeling". VentureBeat. 2019-03-05. Retrieved 2019-03-09.
  2. ^ "Computer Vision Annotation Tool: A Universal Approach to Data Annotation". software.intel.com. 2019-03-01. Retrieved 2019-03-09.
  3. ^ "Computer Vision Annotation Tool (CVAT) source code on github". GitHub. Retrieved 3 March 2019.
  4. ^ "ImageTagger source code on github". GitHub. Retrieved 25 July 2020.
  5. ^ Marzahl, C.; Aubreville, M.; Bertram, C. (2021), "EXACT: A collaboration toolset for algorithm-aided annotation of images with annotation version control", Scientific Reports, 11 (1): 4343, arXiv:2004.14595, Bibcode:2021NatSR..11.4343M, doi:10.1038/s41598-021-83827-4, PMC 7902667, PMID 33623058
  6. ^ Fiedler, N.; Bestmann, M.; Hendrich, N. (2018), ImageTagger: Open Source Online Platform for Image Labeling
  7. ^ WF Wolves – Humanoid KidSizeTeam Description for RoboCup 2020 (PDF), retrieved 26 July 2020
  8. ^ 24 Best Image Annotation Tools for Computer Vision, 17 July 2019, retrieved 26 July 2020
  9. ^ Scheunemann, M.; van Dijk, S.; Miko, R. (2019), Bold HeartsTeam Description for RoboCup 2019, arXiv:1904.10066
  10. ^ Bator, M.; Maciej, P. (2019). "Image Annotating Tools for Agricultural Purpose: a Requirements Study" (PDF). Machine Graphics and Vision. 28: 69–77. doi:10.22630/MGV.2019.28.1.7. S2CID 239007207.
  11. ^ "LabelMe Source". GitHub. Retrieved 26 January 2017.
  12. ^ a b "Reducing the Pain: A Novel Tool for Efficient Ground-Truth Labelling in Images" (PDF). Auckland University of Technology. Retrieved 2018-10-13.
  13. ^ "RectLabel support page". GitHub. Retrieved 29 March 2017.
  14. ^ "Faster R-CNN-Based Glomerular Detection in Multistained Human Whole Slide Images". The University of Tokyo Hospital. Retrieved 2018-07-04.
  15. ^ a b Dutta, 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. ISBN 9781450368896. S2CID 173188066.
  16. ^ "Visual Geometry Group / via". GitLab. Retrieved 2019-02-05.
  17. ^ "Easy Image Bounding Box Annotation with a Simple Mod to VGG Image Annotator". Puget Systems. Retrieved 2019-02-05.
  18. ^ 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.
  19. ^ Tung, Liam. "Free AI developer app: IBM's new tool can label objects in videos for you". ZDNet.
  20. ^ Bornstein, Aaron (Ari) (February 4, 2019). "Using Object Detection for Complex Image Classification Scenarios Part 4". Medium.
  21. ^ Solawetz, Jacob (July 27, 2020). "Getting Started with VoTT Annotation Tool for Computer Vision". Roboflow Blog.
  22. ^ "Best Open Source Annotation Tools for Computer Vision". www.sicara.ai.
  23. ^ "Beyond Sentiment Analysis: Object Detection with ML.NET". September 20, 2020.
  24. ^ "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.