handwriting recognition

handwriting recognition的基本解释

-

手写识别

与handwriting recognition相关的网络例句 [注：此内容来源于网络，仅供参考]

First scanning the known handwriting materials then number them ,In pretreatment, we convert the valid part of the image into a standard size, as images carries out duotone and go throw off chirp handling in order to achieve better effect.draw features from the known handwriting materials with the Co-occurence,especially,we divided a copy of handwriting into 25 little pieces with the size of 128*128 ,drawing features from every little piecese with four directions(0 degree, 45 degrees, 90 degrees as well as 135 degrees) and calculate the four major feature values( veins and the statistical quantity of veins contrast and the statistical quantity of veins consistency Shang the statistical quantity of statistical quantity as well as the veins correlation of gray scale ), preservation all the feature value that drawn from all known ma terials to the handwriting characteristic database,then input the unknown handwriting materials, also using the method of the Co-occurence to draw those features, recycling the minimum European Distance law match the unknown writing material feature value with the handwriting characteristic feature database, export the label of the known hand writing materials which is most similar to the unknown material with minimum European Distance, and then we can confirm who is the author of the unknown material.

首先将笔迹材料作为图象扫描输入，并对其进行编号。预处理部分可将笔迹图象的有效部分规范化到一个统一尺寸，接着对其进行二值化和去除噪声的处理，以便于更好的提取图像的特征。在此我们采用了灰度共生矩阵法提取手写笔迹材料的纹理特征，与以往有所不同的是，我们将一份手写材料分割成64块大小为80*80象素的子图象，每个小块都从四个方向（0度、45度、90度以及135度）来更全面的提取特征，并计算出四个最主要的特征值（纹理一致性的统计量、纹理反差的统计量、纹理熵的统计量以及纹理灰度相关性的统计量），将从所有已知材料提取的特征值保存到纹理特征库中，对于待检手写材料，同样采用灰度共生矩阵的方法提取其纹理特征，再利用最小欧氏距离分类法将从待检手写材料中提取的特征值与纹理特征库中的特征值进行比对，与欧氏距离比对值最小的相匹配，输出匹配成功的原材料的标号，进而识别出待检材料书写者的身份。

This paper mainly discusses the Han Character Internal Codes recognition algorithms in the Multi-lingual Environment, and provides four recognition algorithms, such as Internal Code Bound Recognition Algorithm, Interpunction Recognition Algorithm, Han Character Frequency Recognition Algorithm and Semantic Recognition Algorithm.

在此基础上，本文对不同的识别算法进行分析和评估。在对目标样本的测试中，以上算法的识别率最高可以达到 99 9%以上。1 前言汉字内码向ISO/IEC 1 0 6 46过渡是必然的趋势，但这需要一个较长的过渡期，在这期间计算机内将存在多种标准不一，互不兼容的内码，称之为多文种的环境[1] 。

In this chapter, three kinds of target recognition methods are performed, which are:①Target recognition method based on the description of polarization parameter plane. The echo polarization states of target are projected onto the polarization state plane described by the ellipticity ε and the tilt angle τ of the polarization ellipse, the change of parameter following ferquency becomes the chart. According to the changing trait of the chart, the multidimensional polarization feature space of target has been contructed. Furthermore, a series of polarization feature parameters used in designing the structure of target recognition device are extracted, and they are insensible to the posture of target.②Target recognition method based on the description of Poincare polarization sphere. The echo polarization states of target expressed by Stokes vector are projected onto the Poincare polarization sphere. The conception of polarization ferquency stability, which is used in describing the dynamic distribution characteristics of the target echo polarization states on Poincare polarization sphere, has been defined. A group of polarization feature parameters used in designing the structure of target recognition device are extracted, and they are insensible to the posture of target.③Target recognition method based on the description of frequency sensitivity. In accordance with the conception of the polarization state distance defined on Poincare polarization sphere, the frequency sensibility of the physical structure property of target has been investigated, the frequency distribution feature curves in PSD domain are obtained, and targets'features are extracted by means of the curve-fitting method with Least Square Criterion.

这章具体研究了基于三种极化散射特性描述的相应的目标识别方法：①基于极化参数平面描述的目标识别方法，将目标回波极化状态投影到以极化椭圆参数，即椭圆率角ε和倾角τ表征的极化状态平面上，参数随观测频率的变化就形成了图，根据图的变化特点构造了目标的多维极化特征空间，并提取了不敏感目标姿态变化的极化特征参数组来设计目标的识别器结构；②基于Poincare极化球面描述的目标识别方法，采用Stokes矢量表征目标回波的极化状态，并将其投影到描述极化状态的Poincare极化球面上，定义了极化频率稳定度的概念用以刻画目标回波极化状态在Poincare极化球面上的动态分布信息，提取了准方位不变性的目标极化特征，最后设计了目标的识别器结构；③基于频率敏感性描述的目标识别方法，通过在Poincare极化球面上所定义的极化状态距离的概念，研究的是复杂目标物理结构特性对探测信号频率的敏感程度问题，获得了在极化状态距离下的频率分布特性曲线，采用最小二乘估计曲线拟合方法，它既用于极化特征的降维，同时又直接将拟合参数作为目标的分类特征。

与handwriting recognition相关的网络解释 [注：此内容来源于网络，仅供参考]

Handwriting recognition：手写体识别

手写体识别(Handwriting Recognition)是指计算机接收手写输入和智能地识别它为一些字符. 手写文本的图像可能被光学扫描从一张纸上"离线"感觉. 作为选择的,笔尖的移动可能被"在线"感觉,例如,通过一个基于笔的计算机屏幕表面.

Handwriting recognition：手写识别

handwriting recognision 手写识别,手写辩识 | Handwriting Recognition 手写识别 | hang 死机、当机

Handwriting recognition：手写辨识

手写体阅读机 handwriting reader | 手写辨识 handwriting recognition | 手写数字辨识 handwritten numeral recognition

Handwriting recognition：手写辨认

"handshaking","信号交换" | "handwriting recognition","手写辨认" | "hand-written Chinese input device","手写中文输入装置,手写中文输入器"

Chinese handwriting recognition：手写体汉字识别

电影语言:Chinese character education | 手写体汉字识别:Chinese handwriting recognition | 中文姓名识别:Chinese name recognition