Handwritten Text Recognition (HTR) remains a challenging task, particularly for child handwriting, which often exhibits irregular letter formation, letter crowding, mirrored letters, and phonological spelling errors. The presence of these characteristics is rare in adult handwriting, which forms the primary training data for most existing HTR systems and multimodal large language models (MLLMs). Consequently, current models often autocorrect or misinterpret these features, limiting their effectiveness in contexts where accurate handwritten text recognition is crucial. This is particularly problematic for identifying specific learning disabilities (SLDs) like dyslexia and dysgraphia, where features such as letter reversals, inversions, and spelling mistakes are key diagnostic indicators. To address this gap, we introduce Extended-TrOCR (E-TrOCR), an adaptation of the transformer-based optical character recognition (TrOCR) model specifically designed for child handwriting. E-TrOCR uses a two-stage training process, starting with the IAM dataset for general handwriting recognition, followed by fine-tuning on a dedicated child handwriting dataset. The model employs character-level tokenization to prevent autocorrection and introduces a novel 220-alphabet to represent letter reversals and inversions. Trained on over 1,800 text lines from elementary school students, E-TrOCR significantly outperforms state-of-the-art HTR models, underscoring the necessity of dedicated solutions for child handwriting recognition.