数字化与搜索:高性能计算的非传统应用

Liana Diesendruck, Luigi Marini, R. Kooper, M. Kejriwal, Kenton McHenry
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引用次数: 2

摘要

手写内容的自动搜索是一个非常有趣和实用的主题,由于大型数字化文档集合的公共可用性,在今天尤其重要。我们描述了我们与国家档案馆(NARA)为提供1940年人口普查数据的可搜索访问所做的努力,并讨论了实施建议框架所需的高性能计算资源。我们没有尝试识别手写文本,这仍然是一项非常困难的任务,而是使用了一种基于内容的图像检索技术,即Word Spotting。通过这种模式,使用手写文本图像而不是ASCII文本来查询系统,并将相似图像的排名组呈现给用户。需要大量的计算能力来完成数据的预处理,以便在存档中使用这种搜索功能。讨论了所需的预处理步骤和开发的开源框架,重点是在准备提供可搜索访问大型集合(如美国人口普查)时相关的HPC考虑因素。在处理了1930年人口普查中北卡罗来纳州的98,000个SUs后,我们估计1940年处理整个国家可能需要多达250万个SUs。所提出的框架可用于为各种数字化纸质档案提供昂贵的手动转录的替代方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Digitization and search: A non-traditional use of HPC
Automated search of handwritten content is a highly interesting and applicative subject, especially important today due to the public availability of large digitized document collections. We describe our efforts with the National Archives (NARA) to provide searchable access to the 1940 Census data and discuss the HPC resources needed to implement the suggested framework. Instead of trying to recognize the handwritten text, a still very difficult task, we use a content based image retrieval technique known as Word Spotting. Through this paradigm, the system is queried by the use of handwritten text images instead of ASCII text and ranked groups of similar looking images are presented to the user. A significant amount of computing power is needed to accomplish the pre-processing of the data so to make this search capability available on an archive. The required preprocessing steps and the open source framework developed are discussed focusing specifically on HPC considerations that are relevant when preparing to provide searchable access to sizeable collections, such as the US Census. Having processed the state of North Carolina from the 1930 Census using 98,000 SUs we estimate the processing of the entire country for 1940 could require up to 2.5 million SUs. The proposed framework can be used to provide an alternative to costly manual transcriptions for a variety of digitized paper archives.
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