Recognising letters in on-line handwriting using hierarchical fuzzy inference

A. Hennig, N. Sherkat, R. Whitrow
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引用次数: 9

Abstract

The recognition of unconstrained handwriting has to cope with the ambiguity and variability of cursive script. Preprocessing techniques are often applied to on-line data before representing the script as basic primitives, resulting in the propagation of errors introduced during pre-processing. This paper therefore combines pre-processing of the data (i.e. tangential smoothing) and encoding into primitives (Partial Strokes) in a single step. Finding the correct character at the correct place (i.e. letter spotting) is the main problem in non-holistic recognition approaches. Many cursive letters are composed of common shapes of varying complexity that can in turn consist of other subshapes. In this paper, we present a production rule system using Hierarchical Fuzzy Inference in order to exploit this hierarchical property of cursive script. Shapes of increasing complexity are found on a page of handwriting until letters are finally spotted. Zoning is then applied to verify their vertical position. The performance of letter spotting is compared with an alternative method.
基于层次模糊推理的在线手写字母识别
无约束笔迹的识别必须处理草书的歧义性和可变性。在将脚本表示为基本原语之前,通常对在线数据应用预处理技术,导致预处理过程中引入的错误传播。因此,本文将数据预处理(即切向平滑)和编码成原语(部分笔画)在一个步骤中结合起来。在正确的位置找到正确的字符(即字母定位)是非整体识别方法的主要问题。许多草书字母由不同复杂程度的普通形状组成,这些形状又可以由其他子形状组成。为了充分利用草书的这种层次性,本文提出了一种基于层次模糊推理的生成规则系统。在一页手写体上发现越来越复杂的形状,直到最后发现字母。然后应用分区来验证它们的垂直位置。将字母识别的性能与另一种方法进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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