Handwritten numeral recognition has always been a very challenging task due to many variations in handwritten numerals with different writing styles. It is an active research area now a day. To tackle these variations and to get optimal recognition results, a hybrid feature set, which consists of multiple feature extraction approaches like Box Method, Mean, Standard Deviation and Centre of Gravity, has been used in this paper for recognizing the handwritten numerals. A Neural network has been used for successfully classifying 550 samples taken from “The Chars74” handwritten numerals dataset. The appropriate number of hidden neurons and different membership functions has been used to enhance the recognition results. The proposed recognition system is evaluated and compared with other methods.
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机译：手写数字识别一直是一项非常具有挑战性的任务，这是由于手写数字具有不同的书写样式而发生了许多变化。如今，这是一个活跃的研究领域。为了解决这些变化并获得最佳的识别结果，本文使用了一种混合特征集，该特征集由Box方法，均值，标准偏差和重心等多种特征提取方法组成，用于识别手写数字。一个神经网络已被成功地用于分类来自“ The Chars74”手写数字数据集的550个样本。适当数量的隐藏神经元和不同的隶属函数已用于增强识别结果。对提出的识别系统进行评估，并与其他方法进行比较。
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