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Abstract Text Spotting is the union of the tasks of detection and transcription of the text that is present in images. Due to the various problems often found when retrieving text, such as orientation, aspect ratio, vertical text or multiple languages in the same image, this can be a challenging task. In this paper, the most recent methods and publications in this field are analysed and compared. Apart from presenting features already seen in other surveys, such as their architectures and performance on different datasets, novel perspectives for comparison are also included, such as the hardware, software, backbone architectures, main problems to solve, or programming languages of the algorithms. The review highlights information often omitted in other studies, providing a better understanding of the current state of research in Text Spotting, from 2016 to 2022, current problems and future trends, as well as establishing a baseline for future methods development, comparison of results and serving as guideline for choosing the most appropriate method to solve a particular problem.