Abstract
Image segmentation and image recognition are well established computational techniques in the broader discipline of image processing. Segmentation allows to locate areas in an image, while recognition identifies specific objects within an image. These techniques have shown remarkable accuracy with modern images, mainly because the amount of training data is vast. Achieving similar accuracy in digitized images of centuries-old documents is more challenging. This difficulty is due to two main reasons: first, the lack of sufficient training data, and second, because the degree of specialization in a given domain. Despite these limitations, the ability to segment and recognize objects in these collections is important for automating the curation, cataloging, and dissemination of knowledge, making the contents of priceless collections accessible to scholars and the general public. In this paper, we report on our ongoing work in segmenting and labeling images pertaining to shipbuilding treatises from the XVI and XVII centuries, a historical period known as the Age of Exploration. To this end, we leverage SAM2 for image segmentation; Florence2 and ChatGPT for labeling; and a specialized ontology ontoShip and glossary glosShip of nautical architecture for enhancing the labeling process. Preliminary results demonstrate the potential of marrying these technologies for improving curation and retrieval of priceless historical documents. We also discuss the challenges and limitations encountered in this approach and ideas on how to overcome them in the future.
Abstract (translated)
图像分割和图像识别是更广泛的图像处理学科中的成熟计算技术。分割允许定位图像中的区域，而识别则用于确定图像中特定的对象。这些技术在现代图像上已经展现了极高的准确度，主要是因为训练数据量庞大。然而，在对几世纪前的文档进行数字化后的图像中达到类似的准确度更具挑战性。这种困难主要由两个原因造成：首先是缺乏足够的训练数据，其次是特定领域的专业化程度高。尽管存在这些限制，但在这些收藏品中分割和识别对象的能力对于自动化管理和传播知识至关重要，从而使珍贵的历史文献对学者和公众开放。 本文报告了我们在处理涉及16世纪和17世纪造船论著图像的分割与标注方面的正在进行的工作。这一时期被称为探险时代。为此，我们利用SAM2进行图像分割；使用Florence2和ChatGPT进行标注，并采用专门化的海洋建筑本体ontoShip及术语表glosShip来增强标注过程。初步结果显示了将这些技术结合起来以改进珍贵历史文献的管理和检索潜力的前景。此外，本文还讨论了在此方法中遇到的挑战与限制以及未来克服这些问题的想法。
URL
https://arxiv.org/abs/2603.00147
