In natural history museums, handwritten specimen data cards serve as repositories of field note-derived biological information. Digitizing these records is essential to preserve their content and facilitate worldwide research access. However, this process remains a bottleneck due to the complexities of historical handwriting. I evaluate the potential of Multimodal Large Language Models (MLLMs) to automate the transcription of historical oology cards from the Robert B. Lyle Collection at the Cornell University Museum of Vertebrates. Moving beyond traditional Optical Character Recognition (OCR) pipelines, experimentation tested whether GPT-4o could transcribe and interpret card data using two prompting strategies: a literal "strict transcription" directive and a structured "thinking archivist" directive. Analysis of 262 handwritten and typed cards reveals that the structured directive significantly improved semantic accuracy and reduced median human review time, yielding error-free transcripts for 74% of the dataset. Furthermore, the development of a "Hesitation Score" derived from token-level log-probabilities identified the cards most likely to contain errors. These results demonstrate that MLLMs can accelerate digitization by acting as intelligent transcribers, enabling a scalable, triage-based workflow that optimizes human effort for large-scale historical transcription.