EAGER: Document Image Quality Estimation, Enhancement, Classification and Retrieval

传统的文档检索方法主要是将文档转换为电子文本，然后对文本内容进行索引。最近，社区中的一些工作集中在直接索引文档图像内容上。当文本内容有限或高度退化时，这种技术就失效了。文档质量评估的工作将扩展到图像质量，以解决结构质量问题，这是一个决定传统文档处理操作是否成功的重要因素。然后，该团队将探索增强对分类和检索的影响，并扩展现有工作以适应质量的变化。 这项研究的动机是分析师需要处理非常大的图像数据集。传统的目标是将所有文档转换为电子形式，并使用传统的文本分析方法，但在处理异构集合和非常嘈杂（可能是多语言）的内容时失败了。该方法将允许文档图像检索系统扩展到超出当前能力的数量级，并允许用户超越内容特征，并使用结构相似性来探索大型集合。 这将允许用户挖掘大量的集合，以获得类似内容的集群，而无需通过分类先验地具体知道集合包含什么。其结果将是自适应技术，可以从少量样本中学习，而无需了解退化的来源。

Traditional approaches to document retrieval focus on conversion to electronic text followed by indexing of the text content. Recently some work in the community has focused on indexing document image content directly. Such techniques break down when text content is limited or highly degraded. Work on document quality estimation will be extended image quality to address structural quality, a factor that is important for determining if traditional document processing operations will succeed or not. Then,the team will explore the effects of enhancement on classification and retrieval and extend existing work to adapt to changes in quality. The research is motivated by the need for analysts to deal with very large collections of image data. The traditional goal of converting all documents on an electronic form and using traditional text analysis methods fails when dealing with heterogeneous collections and very noisy (possibly multilingual) content. The approach will allow document image retrieval systems to scale to orders of magnitude beyond current capabilities, and permit users to move beyond content features and use structural similarity to explore large collections. This will permit the users to mine large collections for clusters of similar content without knowing a priori specifically what the collection contains through classification. The result will be adaptive techniques that can learn from small numbers of samples without knowledge of sources of degradation.