解锁档案：利用大型语言模型转录手写历史文献
Unlocking the Archives: Using Large Language Models to Transcribe Handwritten Historical Documents
AI总结：
本研究展示大型语言模型在手写历史文档转录中准确性、速度和成本均优于专门HTR软件，并推出开源工具Transcription Pearl，实现接近人类水平的转录精度，大幅提升数字化效率。
摘要：
本研究证明，大型语言模型（LLMs）在手写历史文献转录方面的准确性显著高于专门的手写文本识别（HTR）软件，同时速度更快、成本效益更高。我们介绍了一款名为Transcription Pearl的开源软件工具，该工具利用这些能力，通过OpenAI、Anthropic和Google提供的商用多模态LLMs，自动转录和校正批量手写文档。在对18/19世纪英语手写文档的多样化语料库进行的测试中，LLMs实现了5.7%至7%的字符错误率（CER）和8.9%至15.9%的词错误率（WER），分别比Transkribus等最先进的专门HTR软件提高了14%和32%。最重要的是，当LLMs随后被用于校正这些转录文本以及由传统HTR软件生成的文本时，它们达到了接近人类的准确性水平，即CER低至1.8%，WER为3.5%。此外，LLMs完成这些任务的速度是专有HTR程序的50倍，成本约为其1/50。这些结果表明，当LLMs被整合到像Transcription Pearl这样的软件工具中时，它们为历史手写文档的大规模转录提供了一种可访问、快速且高度准确的方法，显著简化了数字化过程。
英文摘要：
This study demonstrates that Large Language Models (LLMs) can transcribe historical handwritten documents with significantly higher accuracy than specialized Handwritten Text Recognition (HTR) software, while being faster and more cost-effective. We introduce an open-source software tool called Transcription Pearl that leverages these capabilities to automatically transcribe and correct batches of handwritten documents using commercially available multimodal LLMs from OpenAI, Anthropic, and Google. In tests on a diverse corpus of 18th/19th century English language handwritten documents, LLMs achieved Character Error Rates (CER) of 5.7 to 7% and Word Error Rates (WER) of 8.9 to 15.9%, improvements of 14% and 32% respectively over specialized state-of-the-art HTR software like Transkribus. Most significantly, when LLMs were then used to correct those transcriptions as well as texts generated by conventional HTR software, they achieved near-human levels of accuracy, that is CERs as low as 1.8% and WERs of 3.5%. The LLMs also completed these tasks 50 times faster and at approximately 1/50th the cost of proprietary HTR programs. These results demonstrate that when LLMs are incorporated into software tools like Transcription Pearl, they provide an accessible, fast, and highly accurate method for mass transcription of historical handwritten documents, significantly streamlining the digitization process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
