In November 2024, OpenAI released GPT- 4V(ision), which includes Optical Character Recognition (OCR) capabilities. Given that much of the data curation, processing, and cleaning can be managed through user-friendly prompts (i.e., chat), we aim to conduct an initial assessment of GPT-4V’s effectiveness in transcribing hand-written documents from the urban renewal collection. If GPT-4V can accurately digitize hand-written documents through carefully crafted prompts, it could become a valuable tool for nonexperts in transcribing historical documents on a large scale. Alternatively, if it falls short, it is still crucial to understand and discuss the implications of using Large Language Models (LLMs) for digitizing archival documents.
This paper evaluates GPT-4V’s performance in transcribing the cover pages of selected urban renewal documents. These cover pages, all handwritten and generally more challenging to read (even for humans) compared to other parts of the documents, are valuable for researchers and practitioners focusing on urban renewal, as they succinctly provide key information about property acquisition processes. Figure 1 shows an example of an original document.
* Acknowledgments: We thank Ms. Priscilla Robinson and Dr. Richard Marciano for their continuing collaboration and the scanning of the archival documents.
