## 2 Literature Review  
GEC is a NLP task that aims to improve text quality by detecting and correcting grammatical, syntactic, and stylistic errors. Effective GEC enhances text clarity, making it particularly useful in educational applications, automated proofreading, and document digitization [2]. Early approaches to GEC relied on rule-based methods [1; 6], which were effective in structured contexts but lacked adaptability. These were later replaced by statistical models that utilized large corpora to improve correction accuracy [3]. The field has since evolved with the advent of deep learning, particularly transformer-based architectures [7; 8; 9], which have demonstrated superior performance in grammatical correction through advanced contextual understanding. OCR is a technique utilized to convert printed text into digital format, yet it often introduces errors that compromise text integrity. These errors stem from character misrecognition, incorrect word segmentation, and inconsistencies in formatting, particularly when dealing with complex layouts and degraded print quality [10]. Correcting OCR-generated text requires robust GEC models capable of handling both grammatical and OCR-specific errors. Traditional approaches, such as rule-based spell-checkers, have shown limited success due to their inability to capture contextual meaning. More recent research has explored the application of neural-based GEC models to address OCR-induced errors, improving overall text quality and usability [7]. However, these challenges are particularly pronounced in children's literature, which presents additional OCR-specific difficulties. Unlike conventional texts, children's books often feature short, fragmented text interwoven with illustrations, highly stylized fonts, and playful layouts, making OCR misrecognition more prevalent. The presence of decorative elements and non-standard typography can lead to misclassified characters, missing words, and misplaced text blocks, further complicating grammatical error correction. While neural-based models have improved text restoration for general OCR tasks, limited research has investigated their effectiveness in the unique context of digitized children's literature. The heavy reliance on visual context in these books also presents a challenge, as current text-only GEC models struggle to infer meaning when words are extracted from image-based elements. However, there remains a need to assess the performance of different model architectures in correcting OCR-generated errors, particularly in complex domains such as digitized literature. German presents unique challenges for GEC due to its morphologically complex nature and syntactic variability. Inflectional variations, case-based word changes, and flexible word order introduce additional difficulties in identifying and correcting errors. Unlike English, German exhibits a verb-second (V2) structure in main clauses and verb-final positioning in subordinate clauses, requiring models to account for long-distance dependencies in sentence structure [11]. Another key challenge is German's extensive use of compound words, which are often misrecognized by OCR systems and incorrectly segmented in text-processing tasks. For example, OCR may incorrectly split \"Donaudampfschiffahrtgesellschaftskapitan\" into multiple words, leading to syntactic errors. As a result, effective GEC in German must incorporate advanced contextual understanding to distinguish between correct compound structures and OCR-induced errors. While previous work has demonstrated that fine-tuned encoder-based models, such as GBERT and GELECTRA, offer improvements in handling German syntax [12], recent research suggests that decoder-based models, such as GPT-4o and Llama, provide competitive performance without fine-tuning [13; 14]. Recent advancements in deep learning have led to the development of two dominant approaches for GEC: encoder-based and decoder-based models.