挪威国家图书馆萨米语文本光学字符识别方法的比较分析
Comparative analysis of optical character recognition methods for Sámi texts from the National Library of Norway
AI总结：
针对挪威国家图书馆馆藏萨米语文档OCR准确率不足的问题，本研究微调并对比了三种OCR方法，验证了微调预训练模型结合多源标注补充可在中等人工数据量下实现萨米语高精度OCR。
摘要：
光学字符识别（OCR）是挪威国家图书馆（NLN）数字化流程的关键环节，可将扫描文档转换为机器可读文本。然而，NLN馆藏的萨米语文档OCR准确率不足。鉴于OCR质量会影响下游处理流程，有必要评估并改进萨米语文字的OCR效果，以实现这些资源的可访问性。为满足这一需求，本研究微调并评估了三种成熟的OCR方法——Transkribus、Tesseract和TrOCR，用于转录NLN馆藏的萨米语文本。结果显示，Transkribus和TrOCR在该任务上的表现优于Tesseract，而Tesseract在域外数据集上表现更优。此外，研究表明，微调预训练模型，并结合机器标注与合成文本图像补充人工标注，即使仅使用中等规模的人工标注数据，也能实现萨米语的高精度OCR。
英文摘要：
Optical Character Recognition (OCR) is crucial to the National Library of Norway's (NLN) digitisation process as it converts scanned documents into machine-readable text. However, for the Sámi documents in NLN's collection, the OCR accuracy is insufficient. Given that OCR quality affects downstream processes, evaluating and improving OCR for text written in Sámi languages is necessary to make these resources accessible. To address this need, this work fine-tunes and evaluates three established OCR approaches, Transkribus, Tesseract and TrOCR, for transcribing Sámi texts from NLN's collection. Our results show that Transkribus and TrOCR outperform Tesseract on this task, while Tesseract achieves superior performance on an out-of-domain dataset. Furthermore, we show that fine-tuning pre-trained models and supplementing manual annotations with machine annotations and synthetic text images can yield accurate OCR for Sámi languages, even with a moderate amount of manually annotated data.
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。
