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首页> 外文期刊>Pattern Recognition: The Journal of the Pattern Recognition Society >A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition
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A multi-objective approach towards cost effective isolated handwritten Bangla character and digit recognition

机译：具有成本效益的孤立手写孟加拉字符和数字识别的多目标方法

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摘要

Identifying the most informative local regions of a handwritten character image is necessary for a robust handwritten character recognition system. But identifying them from a character image is a difficult task. If this task were to be performed incurring minimum possible cost, it becomes more challenging due to having two independent, apparently contradicting objectives which need to be optimized simultaneously, i.e. maximizing the recognition accuracy and minimizing the associated recognition cost. To address the problem a multi-objective approach is required. In the present task, two popular multi objective optimization Algorithm (1) a Non-Dominated Sorting Harmony-Search Algorithm (NSHA) and (2) a Non-Dominated Sorting Genetic Algorithm-II (NSGA-Il, Deb et al., 2002 [18]) are employed for region sampling separately. The method objectively selects the most informative set of local regions using the framework of Axiomatic Fuzzy Set (AFS) theory, from the sets of pareto-optimal solutions provided by the multi-objective region sampling algorithms. The system has been evaluated on two isolated handwritten Bangla datasets, (1) a dataset of randomly mixed handwritten Bangla Basic and Compound characters and (2) a dataset of handwritten Bangla numerals separately, with SVM based classifier, using a feature set containing convex-hull based features and CG based quad-tree partitioned longest-run based local features extracted from the selected local regions. The results have shown a significant increase in recognition accuracy and decrease in recognition cost for all the datasets. Thus the present system introduces a cost effective approach towards isolated handwritten character recognition systems. (C) 2016 Elsevier Ltd. All rights reserved.
机译：对于健壮的手写字符识别系统来说，识别手写字符图像的信息最多的局部区域是必需的。但是从角色图像中识别它们是一项艰巨的任务。如果要执行此任务以使可能的成本最小化，则由于具有两个需要同时进行优化的独立的，明显矛盾的目标，即最大化识别精度并最小化相关的识别成本，这将变得更具挑战性。为了解决该问题，需要一种多目标方法。在本任务中，两种流行的多目标优化算法（1）非支配排序和声搜索算法（NSHA）和（2）非支配排序遗传算法-II（NSGA-11，Deb等，2002） [18]）分别用于区域采样。该方法使用公理模糊集（AFS）理论框架，从多目标区域采样算法提供的最佳最优解集中，客观地选择信息量最大的局部区域。系统已在两个孤立的手写Bangla数据集上进行了评估，（1）使用基于SVM的分类器，使用包含凸-从所选局部区域提取的基于船体的特征和基于CG的四叉树划分的基于最长运行时间的局部特征。结果表明，所有数据集的识别准确度均显着提高，识别成本降低。因此，本系统为隔离的手写字符识别系统引入了一种经济有效的方法。 （C）2016 Elsevier Ltd.保留所有权利。

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