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Computer-implemented system and method for recognizing patterns in a digital image through document image decomposition

机译:通过文档图像分解识别数字图像中的图案的计算机实现的系统和方法

摘要

A computer-implemented system and method for retrieving a digital image through document image decomposition is provided. A stored digital image is retrieved. Generic visual features are extracted. The features are grouped into a primitive layer including word-graphs that each include words and features. The words are grouped into a layout layer including zone hypotheses that each include one or more of the words. Causal dependencies between the word-graphs and the zone hypotheses are expressed through zone models that include a joint probability defining a pair of probabilistic models generated through a learned binary edge classifier. Each pair of probabilistic models is expressed as an optimal set selection problem including a set of cost functions and constraints. The optimal set selection problem is evaluated through a heuristic search of the cost functions and constraints and a non-overlapping optimal set of the zone hypotheses is provided that characterize the stored digital image.
机译:提供了一种用于通过文档图像分解来检索数字图像的计算机实现的系统和方法。检索存储的数字图像。提取通用的视觉特征。这些特征被分组到一个包括单词图的原始层中,每个单词图都包含单词和特征。这些单词被分组到一个布局层中,该布局层包括区域假设,每个区域假设都包含一个或多个单词。字图与区域假设之间的因果相关性通过区域模型来表达,区域模型包括一个联合概率,该联合概率定义了通过学习的二进制边缘分类器生成的一对概率模型。每对概率模型都表示为包括一组成本函数和约束条件的最优集合选择问题。通过启发式搜索成本函数和约束条件来评估最佳集合选择问题,并提供表征存储的数字图像的区域假设的非重叠最佳集合。

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