This paper presents a crowd-sourcing platform designed to address the need for parallel corpora in the field of Automatic Text Simplification (ATS). ATS aims to automatically reduce the linguistic complexity of text to aid individuals with reading difficulties, such as those with cognitive disorders, dyslexia, children, and non-native speakers. ATS does not only facilitate improved reading comprehension among these groups but can also enhance the preprocessing stage for various NLP tasks through summarization, contextual simplification, and paraphrasing. Our work introduces a language independent, openly accessible platform that crowdsources training data for ATS models, potentially benefiting low-resource languages where parallel data is scarce. The platform can efficiently aid in the collection of parallel corpora by providing a user-friendly data-collection environment. Furthermore, using human crowd-workers for the data collection process offers a potential resource for linguistic research on text simplification practices. The paper discusses the platform’s architecture, built with modern web technologies, and its user-friendly interface designed to encourage widespread participation. Through gamification and a robust admin panel, the platform incentivizes high-quality data collection and engagement from crowdworkers.