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手写字符的英文

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"手写字符"怎么读用"手写字符"造句

英文翻译手机手机版

  • handwritten character

例句与用法

  • Handwritten character recognition based on bp neural network
    神经网络的手写字符识别
  • Secondly , we use the hmm neural network to recognize on - line handwritten character
    概率神经网络用于联机手写字符的识别。
  • But the results have shown that the hmm neural network can work successfully on the recognition of handwritten characters
    但是我们的试验结果表明,概率神经网络可以成功的用于联机手写字符的识别问题。
  • Support vector machine ( svm ) is used as the implementation basis , which is a tool of statistical learning theory ( slt )
    在探索手写字符识别的方法上采用了统计学习理论,利用支撑向量机svm作为基本的识别工具。
  • Handwritten character recognition research is a branch of the area of optical character recognition ( ocr ) , which deals with the recognition of handwritten english character or digits using computer
    手写字符识别是ocr的一个分支,它的研究对象是:如何利用电子计算机自动辨认手写的英文字符和阿拉伯数字。
  • Recognizing small handwritten character set with this new method and constructing an integrated reorganization system with subspace classifier and bp classifier are the main objects in this paper
    本文研究的目的就是将这一新的识别方法应用于手写字符的小字符集识别,并结合神经网络识别方法构造一个实用的集成识别系统。
  • Research on off - line handwritten character recognition has great worth . the information of off - line handwritten character is very scarce ( only 2d pixel matrix ) , and the handwritten characters have so many classes , complex strokes and shape distortion . all of above have become the obstacle of character recognition
    由于在脱机手写体字符识别中,处理的仅是二维的字符点阵图像,而且存在字符类别多、字形结构复杂、手写字符的变形很大等问题,目前脱机手写字符识别技术还很不成熟,仍处于实验室研究阶段。
  • The recognition research of handwritten character sometimes cold and sometimes hot over the past dozens of years , people propose a lot of recognition method and recognition technology , but because the key technology of recognition was not solved , in addition , such reasons of the aspect as the products make a reservation , which made the existing recognition system be unable to meet the requirement of practical application
    手写字符的识别研究时冷时热,在过去几十年里,人们提出了许多识别方法和识别技术,但由于识别的关键技术没有解决,再加上产品定位等方面的原因,使得已有的识别系统远不能达到实际应用的要求,这其中有理论研究和技术实现等多方面因素。
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