Feature Selection for Recognition of Online Handwritten Bangla Characters
Feature Selection for Recognition of Online Handwritten Bangla Characters
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DOI：
10.1007/s11063-019-10010-2
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发表时间：
2019-02
影响因子：
3.1
通讯作者：
Shibaprasad Sen;M. Mitra;Ankan Bhattacharyya;R. Sarkar;F. Schwenker;K. Roy
中科院分区：
文献类型：
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作者：
Shibaprasad Sen;M. Mitra;Ankan Bhattacharyya;R. Sarkar;F. Schwenker;K. Roy
通过优化技术的特征选择提供了一种有趣的方法，以最大限度地减少计算时间，增强预测能力，并在任何模式识别应用中具有更好的数据认知。本文是先前发表的工作的扩展版本（Sen et al. in：7th IAPR TC 3 workshop on artificial neural networks in pattern recognition，乌尔姆，德国，pp 246-256，2016），其中讨论了基于四叉树的图像分割方法，以估计一些基于拓扑和形状的特征（192-属性），用于识别在线手写孟加拉语字符。之前的工作在由10，000个手写孟加拉字符组成的数据库上实现了98.5%的识别准确率。在本文中，参数，在以前的版本中使用的特征估计，调整，以提高整个系统的性能。此后，krill-herd生物启发式，元启发式算法已被应用到找到最佳的特征向量，通过减少原始特征向量的维数。减少的特征向量已被馈送到顺序最小优化分类识别相同的在线手写孟加拉字符数据库中使用的以前的工作。已经观察到，用该最佳特征集获得的结果几乎等同于由整个特征向量产生的结果。
Feature selection through optimization techniques provides an interesting approach to minimize computational time with enhanced prediction capability, and has better cognizance of data in any pattern recognition application. This paper is an extended version of previously published work (Sen et al. in: 7th IAPR TC3 workshop on artificial neural networks in pattern recognition, Ulm, Germany, pp 246–256, 2016), where a quad-tree based image segmentation approach has been discussed to estimate some topological and shape based features (192-attributed) for the recognition of online handwritten Bangla characters. The previous work achieved a recognition accuracy of 98.5% on a database consisting of 10,000 handwritten Bangla characters. In this paper, parameters, used in the previous version during feature estimation, are tuned to improve the performance of the overall system. Thereafter, krill-herd a bio-inspired, meta-heuristic algorithm has been applied to find the optimal feature vector by reducing the dimension of the original feature vector. The reduced feature vector has been fed to Sequential Minimal Optimization classifier for the recognition of the same online handwritten Bangla character database used in previous work. It has been observed that result obtained with this optimal feature set is almost equivalent as the result produced by the entire feature vector.