打印的泰文字符分割和识别

P. Chomphuwiset
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引用次数: 5

摘要

本文提出了一种识别印刷泰文字符的技术。作品分为两部分。首先进行字符分割。采用连通分量分析技术,在图像中形成字符边界,提取字符段。其次,使用基于特征的技术和卷积神经网络(CNN)对分割字符进行分类/识别。在基于特征的方法中,将字符图像划分为9个区域。每个局部区域都会产生局部特征。将局部特征连接起来,形成用于分类的全局描述符。汉字有66种。数据是从黄金标准数据集BEST数据集收集的。该数据集包含泰文字符和一些特殊字符,共分为66类。实验结果表明,CNN在数据集上提供了最好的结果,准确率达到98%。此外,将分割和识别相结合,取得了良好的效果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Printed thai character segmentation and recognition
This paper presents a techniques for recognizing printed Thai-characters. The work is divided into 2 folds. Character segmentation is firstly carried out. A connected component analysis technique is implemented to form a character boundary and extract character segments in images. Secondly, segmented characters are classified/recognized using a feature-based technique and a Convolution Neural Network (CNN). In the feature-based approach, a character image is divided into 9 regions. Each local region generates local features. The local features are concatenated resulting a global descriptor for classification. There are 66 classes of the characters. The data is collected from a gold standard data set, BEST data set. The data set contains Thai characters and some special characters, which are divided into 66 classes. Experiments are conducted and the result shows that the CNN provide the best results on the data set — obtaining 98% of accuracy. In addition, the segmentation and recognition is combined and produces promising results.
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