Collaborative Research: RI: Small: Unsupervised Islamicate Manuscript Transcription via Lacunae Reconstruction

合作研究：RI：小型：通过缺口重建进行无监督伊斯兰手稿转录

• 批准号: 2200334
• 负责人: Matthew Miller
• 金额: $ 29.78万
• 依托单位: University of Maryland, College Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-07-01 至 2025-06-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2200334&HistoricalAwards=false
• 关键词: Collaborative; Research; RI; Small; Unsupervised

This award tackles handwritten text recognition (HTR, the task of automatically transcribing images of handwritten manuscripts into symbolic text) for Islamicate manuscripts, a domain that encompasses Persian and Arabic written traditions originating in the premodern Islamic world (7th-19th centuries). HTR for modern text is itself a challenging problem that has received substantial attention from the fields of machine learning (ML) and artificial intelligence (AI). However, the predominance of modern text in HTR research is, to some extent, waning: current techniques are relatively robust on modern data, and contemporary written media production is already almost entirely digital. In contrast, historical manuscripts have received comparatively less attention from ML and AI, and at the same time represent both an exceptional opportunity for impact and a set of unique challenges for ML techniques. Specifically, the written traditions of the Islamicate world together form one of the largest -- if not the largest -- archives of human cultural production of the premodern world. Scanning and digitization efforts over the last decade have made images of Islamicate manuscripts in a large number of collections available to the public. However, this data remains ‘locked’ for most scholarly uses because it has not been transcribed into symbolic text which is required for many types of analysis. In fact, the script styles used in Islamicate manuscripts -- 'scribal hands' -- vary so widely and differ so substantially from modern forms that even manual close reading of these texts requires expert training and is thus limited to a small subset of researchers. The primary outcome of this project will be new techniques that 'unlock' the Islamicate written tradition by accurately transcribing it. As a result, this project has the potential to be transformative for humanities disciplines such as Islamic and Near Eastern Studies by enabling libraries to accurately transcribe entire collections and, further, by allowing individual researchers to accurately transcribe manuscripts outside the western canon. Finally, this research will also support interdisciplinary training of a diverse set of graduate students at the University of California San Diego and the University of Maryland.Current techniques for HTR require large amounts of in-domain supervised training data in order to produce highly accurate transcriptions. The neural architectures behind these modern methods are capable of generalizing, to some degree, across modern handwriting styles when trained on larger and more diverse collections of transcribed data. However, their limitations make these techniques impractical for large-scale transcription of Islamicate texts for two reasons: (1) scribal hand variation across Islamicate manuscripts is much more pronounced than stylistic variation in modern handwriting; and (2) transcriptions of Islamicate manuscripts that can be used as supervised training data are extremely scarce because accurate manual transcription requires expert training. This project will develop a new unsupervised learning framework for Islamicate HTR centered around a novel pretraining task: lacuna reconstruction. The new approach trains a neural encoder for images of manuscript text lines by learning to reconstruct masked regions -- i.e. lacaunae -- of unlabeled manuscript images. This completely unsupervised training criterion implicitly incentivizes the model to discover and encode discreteThis award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

该奖项致力于伊斯兰手稿的手写文本识别（HTR，自动将手写手稿图像转录为符号文本的任务），这一领域包括起源于前现代伊斯兰世界（7 -19世纪）的波斯和阿拉伯文字传统。现代文本的HTR本身就是一个具有挑战性的问题，已经受到了机器学习（ML）和人工智能（AI）领域的大量关注。然而，在某种程度上，现代文本在HTR研究中的主导地位正在减弱：当前的技术在现代数据上相对强大，当代书面媒体生产几乎已经完全数字化。相比之下，历史手稿受到ML和AI的关注相对较少，同时也代表了ML技术产生影响的特殊机会和一系列独特挑战。具体来说，伊斯兰世界的书面传统共同构成了前现代世界人类文化生产的最大——如果不是最大——档案之一。在过去的十年中，扫描和数字化的努力使公众可以获得大量收藏的伊斯兰手稿的图像。然而，对于大多数学术用途来说，这些数据仍然是“锁定”的，因为它没有被转录成许多类型分析所需的符号文本。事实上，伊斯兰手稿中使用的文字风格——“抄写手”——差异如此之大，与现代形式差异如此之大，以至于即使是手工细读这些文本也需要专家培训，因此仅限于一小部分研究人员。该项目的主要成果将是通过准确地转录伊斯兰文字传统来“解锁”它的新技术。因此，该项目有可能对伊斯兰和近东研究等人文学科产生变革，使图书馆能够准确地转录整个收藏，进一步说，允许个人研究人员准确地转录西方正典以外的手稿。最后，这项研究还将支持加州大学圣地亚哥分校和马里兰大学的研究生的跨学科培训。当前的HTR技术需要大量的域内监督训练数据，以产生高度准确的转录。在某种程度上，这些现代方法背后的神经架构能够在更大、更多样化的转录数据集合上进行训练，从而泛化现代手写风格。然而，它们的局限性使这些技术不适合大规模抄写伊斯兰文本，原因有二：(1)伊斯兰手稿的抄写笔迹变化比现代笔迹的风格变化要明显得多；(2)可作为监督训练数据的伊斯兰手稿转录极其稀缺，因为准确的人工转录需要专家培训。该项目将为伊斯兰HTR开发一个新的无监督学习框架，围绕一个新的预训练任务：空隙重建。新方法通过学习重建未标记的手稿图像的掩膜区域（即lacacaunae）来训练手稿文本行图像的神经编码器。这个完全无监督的训练标准隐含地激励了模型去发现和编码离散。这个奖项反映了NSF的法定使命，并且通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。