一种新的动态手写字符识别学习方案

F. Andrianasy, M. Milgram
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引用次数: 3

摘要

向量比较在模式识别中是必不可少的。有许多基于距离计算的方法可以进行这种比较。不幸的是,它们中的大多数只适用于向量长度相同或不考虑组件不对齐的情况。本文基于动态规划技术提出了一种新的两种表示之间的距离,称为弹性距离。对其性质进行了研究。我们表明,它导致了最小向量量化技术的一种变体,该技术可以学习一组原型的最佳代表。提出了一种新的质心计算算法。最后,利用预先计算的一组原型的质心,成功地将学习方案算法应用于在线数字手写字符识别问题。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A new learning scheme for the recognition of dynamical handwritten characters
Vector comparison is essential in pattern recognition. Numerous methods based on distance computation are available to carry out such comparison. Unfortunately most of them are applicable only if the vectors are of the same length or do not take into account components misalignment. This paper presents a new distance between two representations called the elastic distance and based on the dynamic programming technique. Properties are studied. We show that it leads to a variant of the least vector quantisation technique that learns the best representants of a group of prototypes. A new centroid computation algorithm is proposed. Finally, the learning scheme algorithm has been successfully applied on an online numerical handwritten character recognition problem using a previously computed centroid of a set of prototypes.
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