Supervised Template Estimation for Document Image Decoding
Supervised Template Estimation for Document Image Decoding
复制标题
用于文档图像解码的监督模板估计
DOI：
10.1109/34.643891
复制
发表时间：
1997
期刊：
影响因子：
--
通讯作者：
Mauricio Lomelin
中科院分区：
文献类型：
--
作者：
G. Kopec;Mauricio Lomelin
提出了一种从页面图像和未对齐转录中监督训练字符模板的方法。模板训练问题被表述为文档图像解码框架内的约束最大似然参数估计之一。这导致了由转录对齐、对齐模板估计 (ATE) 和通道估计步骤组成的三阶段迭代训练算法。最大似然 ATE 问题被证明是 NP 完全问题，因此开发了一种近似解决方法。描述了使用华盛顿大学威斯康星大学第二分校扫描技术期刊文章数据库对特定文档解码任务中的训练过程进行评估。
An approach to supervised training of character templates from page images and unaligned transcriptions is proposed. The template training problem is formulated as one of constrained maximum likelihood parameter estimation within the document image decoding framework. This leads to a three-phase iterative training algorithm consisting of transcription alignment, aligned template estimation (ATE), and channel estimation steps. The maximum likelihood ATE problem is shown to be NP-complete and, thus, an approximate solution approach is developed. An evaluation of the training procedure in a document-specific decoding task, using the University of Washington UW-II database of scanned technical journal articles, is described.