神经纸莎草纸:用于手写纸莎草纸检索的深度注意力嵌入网络

Giuseppe De Gregorio, Simon Perrin, Rodrigo C. G. Pena, Isabelle Marthot-Santaniello, Harold Mouchère
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引用次数: 0

摘要

计算机视觉与机器学习的交汇已成为推动历史研究的一个重要途径,有助于对我们的过去进行更深入的探索。然而,由于其 "黑箱 "性质,机器学习方法在历史古文字学中的应用常常受到批评。为了应对这一挑战,我们推出了神经纸莎草纸,这是一种基于深度学习的创新模型,专门用于分析包含古希腊纸莎草纸的图像。为了解决与透明度和可解释性相关的问题,该模型采用了注意力机制。这种注意力机制不仅增强了模型的性能,还为对决策过程有重要贡献的图像区域提供了可视化表示。该模型专门针对处理带有手写文字行的纸莎草纸文档图像进行了校准,利用单个注意力图来告知输入图像中特定字符的存在与否。本文介绍了 NeuroPapyri 模型,包括其架构和训练方法。评估结果证明了 NeuroPapyri 在文档检索方面的功效,并展示了其在推进历史手稿分析方面的潜力。
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NeuroPapyri: A Deep Attention Embedding Network for Handwritten Papyri Retrieval
The intersection of computer vision and machine learning has emerged as a promising avenue for advancing historical research, facilitating a more profound exploration of our past. However, the application of machine learning approaches in historical palaeography is often met with criticism due to their perceived ``black box'' nature. In response to this challenge, we introduce NeuroPapyri, an innovative deep learning-based model specifically designed for the analysis of images containing ancient Greek papyri. To address concerns related to transparency and interpretability, the model incorporates an attention mechanism. This attention mechanism not only enhances the model's performance but also provides a visual representation of the image regions that significantly contribute to the decision-making process. Specifically calibrated for processing images of papyrus documents with lines of handwritten text, the model utilizes individual attention maps to inform the presence or absence of specific characters in the input image. This paper presents the NeuroPapyri model, including its architecture and training methodology. Results from the evaluation demonstrate NeuroPapyri's efficacy in document retrieval, showcasing its potential to advance the analysis of historical manuscripts.
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