搜索资源列表
EPH1100_driver
- 手写识别芯片EPH1100的驱动源码，基于Linux平台.-handwriting recognition chip EPH1100 driver source code, based on the Linux platform.
handwriting
- 一个联机手写数字识别的实例,可以在windows下编译通过
MULTIBOX
- Windows Mobile平台的日文输入法，支持手写。A sample UI for Japanese handwriting input that works with IME 3.1 and with Pocket IME.
neuralnetwork_source
- A Neural Netwok Project - With Illustration And Code - Learn Neural Network Programming Step By Step And Develop a Simple Handwriting Detection System
kanjipad-1.2.1
- 一个日文手写识别源码，有较高的参考价值。-a Japanese handwriting recognition source, a high reference value.
用C++开发Web 商用程序
- 《用C++开发Web 商用程序》，pdf文件，字迹不是太清楚，但很有用。-"C Web development business procedures" pdf documents, handwriting is not very clear, but very useful.
手写数字识别
- 这个手写数字识别系统是基于模板匹配法建立的。它是我买的图书的光盘上的。-the handwriting recognition system figures are based on template matching method to establish. It is the books I buy on a CD-ROM.
手写识别
- 利用VB开发的手写数字识别系统，实现由1-10的手写识别-using VB's handwriting recognition system figures, the 1-10 achieved by the handwriting recognition
arith-n
- 本设计的目的是：通过分析中文手写笔迹图象特有的灰度以及二维空间分布的统计特性，采用二维游程Hufman编码方法对图象进行压缩与解压缩处理，编写压缩与解压缩应用程序。-the purpose of this design is : Chinese handwriting analysis specific to the gray images of the two-dimensional spatial distribution of s
Text_Recognition_v201_beta_src
- 手写识别-Handwriting recognition
Chinput-2.1.tar
- 手写识别Chinput源码-handwriting recognition Chinput FOSS
hw_num_ocr
- 联机手写数字简易识别系统源代码-on-line handwriting recognition system figures simple source code
bj
- 笔迹识别笔迹识别-handwriting recognition handwriting recognition
kanjipad-1.2.1
- 一个日文手写识别源码，有较高的参考价值。-a Japanese handwriting recognition source, a high reference value.
用C++开发Web 商用程序
- 《用C++开发Web 商用程序》，pdf文件，字迹不是太清楚，但很有用。-"C Web development business procedures" pdf documents, handwriting is not very clear, but very useful.
temp22222222112
- 1联机手写数字识别，可以运行的 (2005-1-14，VC,69KB,下载1-a number of on-line handwriting recognition, can run (2005-1-14, VC, 69KB download a
文字识别程序
- 目前该手写体识别系统主要分为 预处理模块： 主要包括训练数据和识别数据的读取，归一化，二值化 特征提取模块：主要包括笔划方向特征和网格密度特征，还可以根据对识别率的要求继续增加其他特征 识别（分类器）模块：主要包括SVM方法和BP神经网络的方法，其中SVM方法的识别率较高，SVM＋网格密度特征， 在小字符集情况下，达到了识别率97％以上 采用OO思想编写，适合做二次开发-currently the handwriting recogni
手写体数字的识别程序
- 运用神经网络 算法所写的手写数字的识别程序-networks using neural network algorithm written in the handwriting digit identification procedures
联机手写数字识别1
- vc++6.0开发的联机手写数字识别系统-vc 6.0 development of on-line handwriting recognition system figures
HandWritingRecognition
- 通过Java编写的手写数字识别器源代码，功能为能自主学习和调整以适应不同使用者，可以识别数字0-9-Java handwritten figures prepared by the reader source code, in order to function independently and learning to adjust to different users, identification number 0-9