大型语言模型在手写文本识别中的基准测试
Benchmarking Large Language Models for Handwritten Text Recognition
AI总结：
该研究基准测试了多种大型语言模型在手写文本识别中的表现，发现专有模型（如Claude 3.5 Sonnet）在零样本下优于开源模型，且模型自主纠错能力有限。
摘要：
传统的手写文本识别（HTR）机器学习模型依赖监督训练，需要大量人工标注，并且由于版面处理与文本处理相分离，常常产生错误。相比之下，多模态大型语言模型（MLLMs）提供了一种无需针对特定模型训练即可识别多种手写风格的通用方法。本研究将多种专有和开源的大型语言模型（LLMs）与Transkribus模型进行基准对比，评估它们在英语、法语、德语和意大利语书写的现代及历史数据集上的性能。此外，重点测试了模型自主纠正先前生成输出的能力。研究结果表明，在零样本设置下，专有模型（尤其是Claude 3.5 Sonnet）优于开源替代模型。MLLMs在识别现代手写体方面取得了优异结果，并且由于其预训练数据集的构成，表现出对英语的偏好。与Transkribus的比较显示，两种方法均无一致的优势。此外，LLMs在零样本转录中自主纠正错误的能力有限。
英文摘要：
Traditional machine learning models for Handwritten Text Recognition (HTR) rely on supervised training, requiring extensive manual annotations, and often produce errors due to the separation between layout and text processing. In contrast, Multimodal Large Language Models (MLLMs) offer a general approach to recognizing diverse handwriting styles without the need for model-specific training. The study benchmarks various proprietary and open-source LLMs against Transkribus models, evaluating their performance on both modern and historical datasets written in English, French, German, and Italian. In addition, emphasis is placed on testing the models' ability to autonomously correct previously generated outputs. Findings indicate that proprietary models, especially Claude 3.5 Sonnet, outperform open-source alternatives in zero-shot settings. MLLMs achieve excellent results in recognizing modern handwriting and exhibit a preference for the English language due to their pre-training dataset composition. Comparisons with Transkribus show no consistent advantage for either approach. Moreover, LLMs demonstrate limited ability to autonomously correct errors in zero-shot transcriptions.
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。
