## 5 Discussion  
This study evaluated the performance of several LLMs for GEC in OCR-scanned German children's literature, including both encoder-based models (GBERT and GELECTRA) and state-of-the-art decoder-based models (GPT-4o, Llama-3.2-1B, Llama3.2-3B, Llama-3.1-8B, and Llama-3.1-70B). Our results indicate that zero-shot evaluation on advanced decoder-based models, particularly GPT-4o and Llama models, outperformed fine-tuned encoder-based models. This finding suggests that state-of-the-art decoder-based models possess robust language processing capabilities that generalize well to specific tasks, such as GEC, even without task-specific fine-tuning. One of the primary challenges at the initial stage of this study was the high level of noise in the original OCR-scanned documents. The OCR scans of German children's literature often contain a mix of headers, footers, and text extracted from images, which introduces substantial irregularities in the scanned text. Unlike traditional books with continuous paragraphs, children's books frequently feature short texts embedded in illustrations, highly stylized fonts, and fragmented text structures. Due to these layout complexities, no model, whether encoder-based or decoder-based, was fully capable of cleaning all or most of the noise from the text. As a result, a pattern-matching preprocessing step was essential to remove redundant or misrecognized text elements before feeding the data into the models for GEC. However, the pattern-matching steps used in this study rely heavily on the characteristics of the documents and they might not work efficiently for every OCR-scanned document. Furthermore, since certain text elements originate from images and are closely tied to the visual content, the models struggled to fully comprehend and correct such text in a meaningful way. These words often lacked sufficient standalone context, making their interpretation particularly difficult. For instance, if a book features an image of a bear with the text \"grosser Bar\" (big bear) underneath, the models might struggle to correct any OCR-induced errors without visual context. Given this limitation, a multimodal approach, where text-based LLMs are integrated with vision models, could significantly enhance the ability to process and correct text derived from images. Future research could explore the potential of multimodal models in German children's literature, particularly in addressing OCR errors that arise from the interplay between visual and textual elements. Figure 4: Example of Llama-70B’s GEC performance. The left side shows the text before processing, the middle section presents the model’s output, and the right side displays the ground truth. In this “Leuchtnase”, but failed to correct another detected error (“cs”) and it missed one error (“Sich”). Translation: “[PERSON]”s son was different from the other reindeer and now everyone knew it. The dwarves made jokes about the strange glowing nose and the reindeer shook their heads.”  
Figure 5: Example of GPT-4o’s GEC performance. The left side shows the text before processing, the middle section presents the model’s output, and the right side displays the ground truth. In this example, GPT-4o successfully corrected 2 grammatical errors (e.g., changing “cs” to “es” and “u” to “zur. ), but failed to correct another detected error (“Rentieronkcl”). Translation: “Enthralled by the white splendor, he folicked through the snow, sending up dusty clouds. Three reindeer also came to greet [PERSON].”  
Among the models tested, Llama-3.1-70B exhibited the best performance in error detection, accurately identifying grammatical mistakes across various texts. However, GPT-4o demonstrated superior performance in error correction, successfully fixing detected errors with high accuracy and minimal overcorrection--an issue previously observed in older models like GPT-3.5.