Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics, specialization in Data ScienceLarge volumes of labeled data are required to train Machine Learning models in order to solve today’s computer vision challenges. The recent exacerbated hype and investment in Data Labeling tools and services has led to many ad-hoc labeling tools. In this review, a detailed comparison between a selection of data labeling tools is framed to ensure the best software choice to holistically optimize the data labeling process in a Computer Vision problem. This analysis is built on multiple domains of features and functionalities related to Computer Vision, Natural Language Processing, Automation, and Quality Assurance, enabling its application to the most prevalent data labeling use cases across the scientific community and global market
