一种基于动态时间扭曲的自适应分区词识别技术

A. Papandreou, B. Gatos, Konstantinos Zagoris
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引用次数: 6

摘要

分区特征已被证明是最有效的统计特征之一,它提供了高速度和低复杂度的词匹配。它们是由图案框划分的几个区域中的像素密度或图案特征计算出来的。本文介绍了一种用于高效单词识别的自适应分区技术。其主要思想是,根据查询词的长度和像素沿水平轴的分布,在垂直区域切割查询词,并使用动态时间扭曲(Dynamic Time Warping, DTW)将这些边界与候选匹配词的相应区域进行优化调整,从而提取分区特征。这种调整是通过使用相应的扭曲矩阵将查询词的每个区域与每个候选匹配词的相应区域耦合来执行的。这个过程消除了查询和候选匹配词之间的歧义,因此它可以应用于机器打印和手写的文档图像。使用像素密度作为每个区域的特征特征对所提出的单词识别技术进行了测试,并与其他最先进的方法相比记录了改进。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
An Adaptive Zoning Technique for Word Spotting Using Dynamic Time Warping
Zoning features have been proved one of the most efficient statistical features which provide high speed and low complexity word matching. They are calculated by the density of pixels or pattern characteristics in several zones that the pattern frame is divided. In this paper, an adaptive zoning technique for efficient word spotting is introduced. The main idea is that the zoning features are extracted after cutting the query word in vertical zones, according to its length and pixel distribution along the horizontal axis, and adjusting these boundaries optimally with the corresponding zones in the candidate match-word using Dynamic Time Warping (DTW). This adjustment is performed by coupling every zone of the query word to the corresponding zone of each candidate match-word with the use of the corresponding warping matrix. This process absorbs the ambiguities between the query and the candidate match words and due to this fact it can be applied to both machine-printed and handwritten document images. The proposed word spotting technique is tested using the pixel density as a characteristic feature in every zone and an improvement is recorded compared to other state-of-the-art methods.
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