搜索资源列表
OpenCVHello
- OpenCV的轮廓识别，自己用MFC+OpenCV写的轮廓识别与字符分割小软件.软件可实现轮廓识别的准确分割.-OpenCV' s profile recognition, they used to write the outline of MFC+ OpenCV recognition and character segmentation in a small software. Software to achieve accurate segmentation contour dete
cvCarRecog
- 基于Opencv的汽车牌照定位与字符识别源代码，可以运行 -Vehicle License Plate Location Based on Opencv and character recognition source code, you can run
ocr
- 这是一个基于OFFICE组件的字符识别及OPENCV简单操作，图片路径需要自己进行更改，代码里面有详细说明-This is a component-based character recognition and OFFICE OPENCV simple operation, image paths need to make changes to their code which is described in detail
opencvCar
- 开发环境：vc6.0 ,opencv1.0.整体功能：实现车牌跟踪，检测，车牌定位，车牌分割，字符 分割，字符识别，其中车牌跟踪和车牌检测不是很好，但是对于已经拍好的图片，车牌定位， 车牌分割，字符分割和字符识别效果很好。程序中的核心算法，本人已加详细注释。具体详见程序说明。希望对你有所帮助。-Development Environment: vc6.0, opencv1.0. Overall functions: license plate tracking, detect
opencv
- opencv字符检测 字符识别检测算法使用vc-opencv char detect
opencvCar
- 基于opencv的车牌识别程序，能实现目标跟踪，截图，车牌识别包括了打开图像、图像二值化、车牌定位、字符分割、字符识别等。-Opencv-based license plate recognition program, to achieve the target tracking shots, license plate recognition, including the open image, image binarization, license plate localization, c
Demo
- 有关车牌识别的程序，是支持向量机和神经网络一起写的。包括车牌的预处理、定位、分割、字符识别等。-For license plate recognition program, support vector machines and neural networks wrote together. Pretreatment including the license plate, positioning, segmentation and character recognition.
SVM-test
- 这是SVM+水平竖直直方图特征的车牌字符识别。包括汉字、字母、数字的识别。-This is SVM+ horizontal vertical histogram license plate character recognition. Including Chinese characters, letters, numerals.
LPRtest1
- 这是有关libsvm中的HOG+SVM的一个程序，识别车牌字符。-It is about the HOG+SVM libsvm a program to identify the license plate characters.
tesseract-ORC-test1
- 这是把google中的OCR进行改进后的一个程序，识别光学字符-This is the google for improved OCR in a program, optical character recognition
conponentprojection
- 在vs2008中写的openCv代码，是对特定水表图像，利用RGB空间定位，然后分割字符，并进行识别，是几种方法的综合-it is a openCv code in vs2008, for a specific meter image, taking use of the RGB space location, then split characters and identification, several methods are integrated
opencv_1
- 车牌识别，从图像中截取车牌，并旋转至水平，最后分隔出字母和数字字符。-License plate recognition, license plate interception from the image and rotated to the horizontal, and finally separating the alpha and numeric characters.
Vehicle-license-recognition
- 车牌识别最基本的流程是：将采集后的图像二值化，然后依次经过车牌定位、字符分割、去除干扰，最后是字符识别-License Plate Recognition basic process is: after the acquisition of image binarization, followed through the license plate location, character segmentation, remove interference, and finally charact
lpr
- 基于Opencv的汽车牌照定位与字符识别源代码,最终版本,可以运行-License plate recognition
zifuchuantiqu
- 随着当今社会信息化的高速发展，信息的采集和瓶颈问题就显得日益突出。尤其在智能系统方面对字符信息的采集及识别要求越来越高。比如：智能交通系统中车牌号码的识别，计量刻度中刻度提取与识别等。而对字符的识别处理一般包括字符的特征提取方法和识别算法。其中字符的特征提取是这个过程的重点和难点。-With today s rapid development of information society, information gathering and bottlenecks becomes increas
plate-recognition
- 对自然场景中的字符识别，对车牌字符识别，随机抓取自然中的图像，可能包含数字、英文字符、汉字等-Character recognition in natural scenes, character recognition of license plate characters, random crawling of natural images, may include numbers, English characters, Chinese characters, etc.
LicenseplateRecognition
- 车牌牌照检测识别完整源代码 开发环境：vc6.0和opencv1.0 整体功能：是为了实现车牌自动跟踪，检测，并拍照，定位，识别，但是跟踪和拍照等于没有实现。 实现功能： 能实现视频中物体跟踪，但必须是手动圈住才行，摄像头是usb摄像头. 打开车牌图片，能进行定位，车牌分割，字符分割，字符识别。(License plate recognition)
车牌识别文档
- 车牌字符识别是字符识别的重要组成部分。车牌字符识别的最终目的就是将图像中的车牌字符转化成文本字符，车牌字符的识别属于印刷体识别范畴。 字符识别的基本思想是匹配判别。抽取待识别字符特征按照字符识别的原理和预先存储在计算机中的标准字符模式表达形式的集合逐一进行匹配，找出最接近输入字符模式的表达形式，该表达形式对应的字就是识别结果。(License plate character recognition is an important part of character recognition. T
字符特征提取
- 结合OpenCV在VS2015平台上对含有字符特征提取，例如可以进行车牌识别等(Combined with OpenCV on the VS2015 platform to contain character feature extraction, such as license plate recognition can be carried out)
Python-opencv车牌识别
- 算法思想来自于网上资源，先使用图像边缘和车牌颜色定位车牌，再识别字符。车牌定位在predict方法中，为说明清楚，完成代码和测试后，加了很多注释，请参看源码。车牌字符识别也在predict方法中，请参看源码中的注释，需要说明的是，车牌字符识别使用的算法是opencv的SVM， opencv的SVM使用代码来自于opencv附带的sample，StatModel类和SVM类都是sample中的代码。SVM训练使用的训练样本来自于github上的EasyPR的c++版本。由于训练样本有限，你测试时会