Hmm-Based System for Recognizing Words in Historical Arabic Manuscript

M. Khorsheed
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引用次数: 6

Abstract

This paper presents an omni-font Arabic word recognition system. The system is based on multiple Hidden Markov Models (HMMs). Each word in the lexicon is represented with a distinct HMM. The proposed system first extracts a set of spectral features from word images, then uses those features to tune HMM parameters. The performance of the proposed system is assessed using a corpus that includes both handwritten and computer-generated scripts. The likelihood probability of the input pattern is calculated against each word model and the pattern is assigned to the model with the highest probability.
基于hmm的阿拉伯语历史手稿词识别系统
提出了一种全字体阿拉伯语单词识别系统。该系统基于多个隐马尔可夫模型(hmm)。词典中的每个单词都用一个不同的HMM表示。该系统首先从单词图像中提取一组光谱特征,然后利用这些特征对HMM参数进行调优。使用包含手写和计算机生成脚本的语料库来评估所建议系统的性能。根据每个单词模型计算输入模式的似然概率,并将模式分配给具有最高概率的模型。
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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