Abstract
There is an immense quantity of historical and cultural documentation that exists only as handwritten manuscripts. At the same time, performing OCR across scripts and different handwriting styles has proven to be an enormously difficult problem relative to the process of digitizing print. While recent Transformer based models have achieved relatively strong performance, they rely heavily on manually transcribed training data and have difficulty generalizing across writers. Multimodal LLM, such as GPT-4v and Gemini, have demonstrated effectiveness in performing OCR and computer vision tasks with few shot prompting. In this paper, I evaluate the accuracy of handwritten document transcriptions generated by Gemini against the current state of the art Transformer based methods. Keywords: Optical Character Recognition, Multimodal Language Models, Cultural Preservation, Mass digitization, Handwriting Recognitio
Abstract (translated)
有大量的历史和文化文档仅以手稿形式存在。同时，跨不同书写脚本和风格进行OCR（光学字符识别）证明比数字化印刷品的过程要困难得多。尽管最近基于Transformer的模型已经实现了相对较强的表现力，但它们高度依赖于手动转录的训练数据，并且在面对不同的写作者时难以泛化。多模态语言模型如GPT-4v和Gemini已经在使用少量样本提示的情况下展示了执行OCR和计算机视觉任务的有效性。在这篇论文中，我评估了由Gemini生成的手稿文档转录准确度与当前最先进的基于Transformer的方法之间的对比情况。关键词：光学字符识别，多模态语言模型，文化保存，大规模数字化，手写识别
URL
https://arxiv.org/abs/2410.24034
