RI: Small: Print and Probability - A Statistical Approach to Analysis of Clandestine Publication

RI：小：印刷品和概率 - 秘密出版物分析的统计方法

• 批准号: 1816311
• 负责人: Taylor Berg-Kirkpatrick
• 金额: $ 49.98万
• 依托单位: Carnegie-Mellon University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2019-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1816311&HistoricalAwards=false
• 关键词: RI; Small; Print; Probability; Statistical

This Print and Probability project develops novel machine learning and computer vision techniques to infer thousands of book and pamphlet printers whose identities have eluded scholars for roughly 500 years. Before the modern era, the book trade was often dangerous and secretive. For fear of persecution and punishment, printers between 1473-1800 declined to attach their names to about a quarter of all known books and pamphlets. However, now that over 130,000 books have been digitized by the Early English Books Online (EEBO) project, defects and variations in the printing tools of this era may hold the key to identifying these printers. Once an individual piece of metal type is damaged, it creates unique stamps. Since typesets belonged to specific printers, impressions of damaged type can thus serve as the fingerprints to identify the printers of tens of thousands of clandestine publications. The Print and Probability project automatically detects and tracks these unique pieces of damaged type in order to uncover new information about the history of books. The methods developed in this project could be generalizable to other important tasks and domains - for example, digital forensics and authorship attribution. In addition, the Print and Probability project will train students in a multidisciplinary way, engaging them in collaboration across multiple fields.By developing new techniques for visual anomaly detection, the Print and Probability project detects damaged letterforms that create consistent aberrations. Based on these damaged type extractions, the project develops probabilistic models of both printer and damaged letter form identifications that allow direct inference of printers at scale. This framework also incorporates other sources of evidence into the identification model - most significantly, the spelling, punctuation, and spacing habits of individual press-house compositors, whose distinctive practices lend themselves to clustering and automatic attributions across all pages of text in the collection. Integrating a new method for automatic compositor attribution, this project develops a statistical model for printer identification that leverages the same sources of evidence compiled manually by scholars of rare books, but at a scale and speed never before possible.This 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.

这个打印和概率项目开发了新颖的机器学习和计算机视觉技术，以推断数千本书籍和小册子的打印机，这些打印机的身份已经躲避了学者大约500年。 在现代之前，图书贸易往往是危险和秘密的。由于担心受到迫害和惩罚，1473年至1800年期间，印刷商拒绝在大约四分之一的已知书籍和小册子上附上他们的名字。然而，现在已经有超过130，000本书被早期英语图书在线（EEBO）项目数字化，这个时代印刷工具的缺陷和变化可能是识别这些印刷商的关键。一旦金属类型的一个单独的一块被损坏，它创建独特的邮票。由于排字机属于特定的印刷厂，因此，损坏的铅字可以作为指纹，用来识别成千上万种秘密出版物的印刷厂。印刷和概率项目自动检测和跟踪这些独特的损坏类型，以揭示有关书籍历史的新信息。在这个项目中开发的方法可以推广到其他重要的任务和领域-例如，数字取证和作者归属。此外，印刷和概率项目将以多学科的方式培养学生，让他们在多个领域进行合作。通过开发视觉异常检测的新技术，印刷和概率项目检测产生一致畸变的受损字体。基于这些损坏的类型提取，该项目开发了打印机和损坏的信件形式识别的概率模型，允许直接推断打印机的规模。该框架还将其他证据来源纳入识别模型--最重要的是，各个印刷厂排版人员的拼写、标点和间距习惯，他们独特的做法有助于对集合中所有文本页面进行聚类和自动归属。 该项目整合了一种新的自动排序器归属方法，开发了一种用于打印机识别的统计模型，该模型利用了稀有书籍学者手动编制的相同证据来源，但规模和速度前所未有。该奖项反映了NSF的法定使命，并被认为值得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来支持。