EAGER: Large Scale Document Image Triage, Indexing and Retrieval

包括印刷文本和手写文本的图像中的结构相似性搜索和检索仍然是一个具有挑战性的问题，特别是对于嘈杂和异构的集合。目前使用的方法通常在过滤之前转换文档。这项工作提供了一种分类方法，通过与已知属性的结构相似性来过滤非常大的集合，然后使用更广泛的术语和散列进行新的聚类，以扩展所考虑的集合的规模。这项工作将为文档图像检索提供新的方向，特别是在结构和布局变化很大的情况下，并将在云环境中进行扩展。另一种扩展方法，特别是在重复检测领域，将扩展多级位置敏感散列，并将其推广到其他分析索引和检索问题。除了包括研究生之外，成果和软件将通过知识共享许可提供，以提供成果的复制和扩展。

Structural similarity search and retrieval in images that include both printed text and handwritten text remains a challenging problem, especially with collections that are noisy, and heterogeneous. Approaches currently in use generally convert documents before filtering. This work provides triage as a way to filter very large collections through structural similarity with known attributes, then new clustering with broader terms and hashing to extend the scale of collections considered. The work will provide new directions for document image retrieval, especially in conditions where there is a wide variation in structure and layout and will be made scalable in cloud environments. Another approach to scaling, especially in the area of duplicate detection, will extend multi-level locality sensitive hashing and generalize it to other analysis indexing and retrieval issues. In addition to including graduate students, results and software will be made available through Creative Commons licensing to provide for replication and extension of the results.