Learning regions of interest in postal automation
Learning regions of interest in postal automation
复制标题
学习邮政自动化感兴趣的领域
DOI：
10.1109/icdar.1999.791788
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发表时间：
1999
期刊：
影响因子：
--
通讯作者：
H. Walischewski
中科院分区：
文献类型：
--
作者：
H. Walischewski
邮件的总体布局取决于某些规则、发件人的国家、惯例等。邮政自动化的一个中心任务是邮件上目标地址的本地化。经过预处理后，邮件由每个区域的一组边界框组成。在本文中，一个正式的图形表示的邮件件将显示，其中相对星座的包围盒表示在一个定性的方式。从这样的图的标记集合，可以自动导出由训练集表示的域的模型图。模型图仅保持在训练集的邮件上发现的项目之间的定性关系。基于A* 算法的推理机制被用来找到学习模型和以前看不见的邮件之间的不精确子图同构。实现的系统进行了评估，对真实的字母和识别结果进行了讨论。
The general layout of mail pieces depends on certain rules, the sender's country, conventions and so on. One central task in postal automation is the localization of target addresses on mail pieces. After preprocessing, a mail piece consists of a set of bounding boxes for each region. In this paper a formal graph representation for mail pieces will be shown, in which relative constellations of bounding boxes are represented in a qualitative way. From a labeled set of such graphs, a model graph of the domain represented by the training set can be derived automatically. The model graphs only hold qualitative relations between items found on mail pieces of the training set. An inference mechanism based on the A* algorithm is used to find inexact subgraph isomorphism between a learned model and a formerly unseen mail piece. The implemented system has been evaluated on real letters and the recognition results are discussed.