Off-line handwritten word recognition (HWR) using a single contextual hidden Markov model

Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Pub Date : 1992-06-15 DOI:10.1109/CVPR.1992.223205

Mou-Yen Chen, A. Kundu, Jian Zhou

引用次数: 23

Abstract

A complete scheme for totally unconstrained handwritten word recognition based on a single contextual hidden Markov model (HMM) is proposed. The scheme includes a morphology- and heuristics-based segmentation algorithm and a modified Viterbi algorithm that searches the (l+1)st globally best path based on the previous l best paths. The results of detailed experiments for which the overall recognition rate is up to 89.4% are reported.<>

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离线手写字识别(HWR)使用单一上下文隐马尔可夫模型

提出了一种基于单一上下文隐马尔可夫模型(HMM)的完全无约束手写体单词识别方案。该方案包括基于形态学和启发式的分割算法和改进的Viterbi算法，该算法基于前l个最佳路径搜索(l+1)个全局最佳路径。详细的实验结果表明，总体识别率高达89.4%。

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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Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition

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