Imagine a world where the secrets hidden within centuries-old handwritten documents are instantly revealed. No more painstakingly deciphering faded script or relying on scarce expert transcribers. Thanks to the latest advancements in artificial intelligence, this world is becoming a reality. Researchers are now leveraging the power of multimodal Large Language Models (LLMs) like Gemini to unlock the historical treasures hidden in handwritten archives. These powerful AI models can not only recognize and transcribe handwritten text, but also understand the context, correct spelling errors, and even adapt to different writing styles and languages. This research compared Gemini's performance to state-of-the-art transcription methods like TrOCR and CNN-BiLSTM models. The findings revealed that while specialized, fine-tuned models still hold an edge, especially for non-English languages, Gemini demonstrated surprisingly comparable accuracy for English texts with minimal training data. The implications are huge. For historians, this means easier access to vast troves of primary source material, potentially rewriting our understanding of the past. For cultural institutions, it opens up new possibilities for preserving and sharing historical collections with a wider audience. However, challenges remain. The research highlighted the impact of training data biases on LLM performance, with Gemini showing weaker results for languages other than English. Furthermore, the occasional “hallucinations” of LLMs – generating text unrelated to the image – pose a hurdle. Future research will focus on mitigating these issues, further refining LLM capabilities and paving the way for a future where historical documents are as accessible and searchable as today's digital texts.