This paper presents the concept and technical justification of a software product for automatic detection and correction of spelling errors in German-language texts. The relevance of the topic is due to the complexity of the German language in terms of grammar, spelling and word formation, which creates significant difficulties for speakers of other languages. This is especially true for those who study the language or use it in professional activities, where the accuracy of speech is crucial. The developed concept is focused on identifying typical spelling errors, including omissions, letter permutations and incorrectly used words. Instead of the classic use of large dictionary databases and search methods by editing distance, the project involves working with machine learning data on typical errors, which allows reducing resource requirements and ensuring faster text processing. This approach makes the system more mobile, autonomous and suitable for integration into educational or professional environments. During the development process, a comprehensive system analysis was carried out: a goal tree was built, a context diagram was formed to describe information connections, the main stages of the system life cycle were determined, and the logic of the work was modeled using a UML activity diagram. Particular attention was paid to the choice of methods and means of implementation, including the analysis of software tools and Python libraries that best meet the tasks of automatic correction of spelling errors. The possibilities of integrating machine learning algorithms to improve the system's adaptation to new types of errors were considered. The results of the work form the basis for creating an effective, compact and adaptive tool that can be expanded in the future - in particular, to detect grammatical errors, which will significantly increase the functionality of the system and its practical value.
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