首页> 外文会议>International Conference on Artificial Intelligence and Soft Computing;ICAISC 2014 >Offline Text-Independent Handwriting Identification and Shape Modeling via Probabilistic Nodes Combination
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Offline Text-Independent Handwriting Identification and Shape Modeling via Probabilistic Nodes Combination

机译:通过概率节点组合脱机文本独立的手写识别和形状建模

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Proposed method, called Probabilistic Nodes Combination (PNC), is the method of 2D curve modeling and handwriting identification by using the set of key points. Nodes are treated as characteristic points of signature or handwriting for modeling and writer recognition. Identification of handwritten letters or symbols need modeling and the model of each individual symbol or character is built by a choice of probability distribution function and nodes combination. PNC modeling via nodes combination and parameter γ as probability distribution function enables curve parameterization and interpolation for each specific letter or symbol. Two-dimensional curve is modeled and interpolated via nodes combination and different functions as continuous probability distribution functions: polynomial, sine, cosine, tangent, cotangent, logarithm, exponent, arc sin, arc cos, arc tan, arc cot or power function.
机译:所提出的方法,称为概率节点组合(PNC),是使用该组关键点的2D曲线建模和手写识别的方法。 节点被视为用于建模和作者识别的签名或手写的特征点。 识别手写字母或符号需要建模,并且通过选择概率分布函数和节点组合来构建每个单独符号或字符的模型。 PNC模型通过节点组合和参数γ作为概率分布函数使得每个特定字母或符号的曲线参数化和插值。 二维曲线通过节点组合和不同的功能为连续概率分布函数建模和插值:多项式,正弦,余弦,切线,Cotangent,对数,指数,弧线,弧形Cos,弧形棕褐色,弧线或电源功能。

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