## 1 Introduction  
Advancements in machine learning and artificial intelligence (AI) have significantly enhanced the field of GEC, which is particularly important for improving the accuracy of texts processed through Optical Character Recognition (OCR). This is a crucial step in transforming digitized versions of historical and literary works into clean, usable formats. Large language models (LLMs) have emerged as powerful tools for this purpose, demonstrating a remarkable ability to understand and correct complex linguistic patterns. However, some types of texts present unique challenges, such as German children's literature, characterized by modified language, colloquial expressions, and narrative styles tailored to young readers. Additionally, children's books often contain a mix of images and text, which can make the OCR process more complex. The presence of illustrations, decorative fonts, and varied text layouts can also introduce additional noise and errors during scanning, leading to a higher rate of misrecognition. Therefore, cleaning texts digitized using OCR can be challenging due to the wide variety of errors that can be introduced including distorted characters, misplaced text, and other layout-related issues. The field of GEC in OCR-scanned documents has seen substantial advancements, driven by the need to improve the quality of digitized texts for analysis and reading. Early approaches relied on rule-based methods that utilized predefined grammar rules and dictionaries to correct errors, but these methods often lacked the flexibility to handle context-specific variations [1]. With the advent of machine learning, statistical models offered greater adaptability, and more recently, neural network-based models, including large language models, have become the standard for GEC tasks [2; 3]. German-specific GEC faces unique challenges due to the morphological complexity of the language. Evaluating the robustness of a model to OCR-induced noise is critical for ensuring accuracy in processed texts [4]. Despite the progress in this area, there is a notable gap in evaluating the performance of state-of-the-art LLMs on the task of GEC in German texts, specifically those found in children's literature. While LLMs have shown promise in general error correction tasks, their effectiveness in handling the particular linguistic features of German children's books has not been fully explored. Addressing this gap is crucial for leveraging the full potential of LLMs, allowing for a more automated and accurate approach to cleaning OCR-scanned documents. The goal of this study is to address this research gap by comparing the performance of various LLMs in correcting grammatical errors in OCR-scanned German children's literature. By evaluating different models, we aim to identify the one that performs best for this specific task, considering the unique linguistic aspects of the texts. The significance of this research lies in its potential to improve the accessibility and readability of OCR-scanned German children's literature. Given that OCR processes frequently introduce errors, effective grammatical correction is essential for preserving the original meaning and educational value of these works. By comparing different large language models, this study seeks to identify the strengths and weaknesses of these models, including the most suitable model for handling such corrections, contributing valuable insights into the practical applications of AI in language preservation. Furthermore, this work holds significance for corpus linguistics research by providing cleaner, more accurate textual data, which is crucial for analyzing linguistic patterns and studying language use in culturally significant genres. Ultimately, this research contributes to expanding the available sources of German literature, particularly children's literature, helping to addressthe scarcity of such resources.