基于加权贝叶斯的脱机手写阿文单词识别

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。

Offline Handwritten Arabic Word Recognition Based on Weighted Bayesian

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.