Taxonomy, a scientific and systematic categorization of elements, has been extensively applied in various domains, including data grids, data mining tasks, and network systems. However, until now, there has been a notable absence of research exploring the taxonomy of Character Recognition (CR) Systems. CR, the process of identifying characters in image format and associating them with their respective ASCII or Unicode, presents varied mechanisms for different phases of the recognition process. Our study centers around the development of a taxonomy for CR, exploring both contemporary trends and obstacles within the domain. We pivot to CR, systematically examining pivotal technologies, application scenarios leveraging state-of-the-art machine learning models, and associated services within diverse contexts. The narrative encompasses an exploration of the challenges and constraints inherent in CR systems. Through a systematic literature review, we scrutinize the fundamental technologies, practical applications, and research trajectories in CR, pinpointing burgeoning developments and avenues for further investigation. This diversity enables us to classify character recognition systems into groups and subgroups, paving the way for an intensive taxonomy of these systems by delveing into existing character recognition systems, identifying similarities and differences. Ultimately, we propose a taxonomy of character recognition systems, offering a novel perspective on this domain. A rigorous selection process was undertaken, identifying and categorizing 96 papers published between 2018 and 2024 according to predefined criteria. The findings are meticulously analyzed, offering a panoramic taxonomy of character recognition system in various contexts.