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Online Handwriting Recognition Using Multi Convolution Neural Networks

机译:使用多卷积神经网络的在线手写识别

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This paper presents a library written by C# language for the online handwriting recognition system using UNIPEN-online handwritten training set. The recognition engine based on convolution neural networks and yields recognition rates to 99% to MNIST training set, 97% to UNlPEN's digit training set (la), 89% to a collection of 44022 capital letters and digits (1a.1b) and 89% to lower case letters (1c). These networks are combined to create a larger system which can recognize 62 English characters and digits. A proposed handwriting segmentation algorithm is carried out which can extract sentences, words and characters from handwritten text. The characters then are given as the input to the network.
机译:本文介绍了一种使用C#语言编写的,用于使用UNIPEN在线手写训练集的在线手写识别系统的库。基于卷积神经网络的识别引擎,对MNIST训练集的识别率高达99%,对UNlPEN的数字训练集(la)的识别率高达97%,对44022个大写字母和数字的集合(1a.1b)的识别率高达89%小写字母(1c)。将这些网络组合在一起,可以创建一个更大的系统,该系统可以识别62个英文字符和数字。提出了一种可以从手写文本中提取句子,单词和字符的手写分割算法。然后将这些字符作为网络的输入。

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