基于加权贝叶斯的脱机手写阿文单词识别  

Offline Handwritten Arabic Word Recognition Based on Weighted Bayesian

作　　者：许亚美 何继爱[1] XU Yamei;HE Ji'ai(School of Computer and Communication,Lanzhou University of Technology,Lanzhou,Gansu 730050,China)

机构地区：[1]兰州理工大学计算机与通信学院,甘肃兰州730050

出　　处：《中文信息学报》2021年第2期133-140,共8页Journal of Chinese Information Processing

基　　金：国家自然科学基金(61562058,61561031)。

摘　　要：针对手写阿拉伯单词书写连笔,且相似词较多的特点,该文提出一种新的脱机手写文字识别算法。该算法以固定组件为成分拆分阿拉伯单词,构建自组件特征至单词类别的加权贝叶斯推理模型。算法结合单词组件分割、多级混合式组件识别、组件加权系数估计等,计算单词类别的后验概率并得到单词识别结果。在IFN/ENIT库上的实验,获得了90.03%的单词识别率,证实组件分解对笔画连写具有鲁棒性,组件识别能提高相似词的辨别能力,而且该算法所需训练类别少,易向大词汇量识别扩展。A new offline handwritten Arabic word recognition algorithm is proposed to deal with its connected writing strokes and more similar words. The algorithm first establishes a structure model with fixed graphemes for each Arabic word category to be recognized, then segments the word samples into graphemes. Then a weighted Bayesian inference model is constructed from the grapheme features to word categories. The word recognition results are obtained by calculating the posterior probabilities of word categories. On the IFN/ENIT database, the proposed algorithm achieves as high as 90.03% accuracy.

关 键 词：手写文字识别 阿拉伯文 单词识别 加权贝叶斯 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]