Question

我正在使用OpenCV从iPhone相机准备OCR图像，我一直无法获得准确的OCR扫描所需的结果。这是我现在使用的代码。

    cv::cvtColor(cvImage, cvImage, CV_BGR2GRAY);
    cv::medianBlur(cvImage, cvImage, 0);
    cv::adaptiveThreshold(cvImage, cvImage, 255, CV_ADAPTIVE_THRESH_MEAN_C, CV_THRESH_BINARY, 5, 4);

这种方法花费的时间太长，并没有给我带来好的结果。 enter image description here

有关如何使其更有效的任何建议？这些图像来自iPhone相机。

使用安德里的建议后。

enter image description here

    cv::Mat cvImage = [self cvMatFromUIImage:image];
    cv::Mat res;
    cv::cvtColor(cvImage, cvImage, CV_RGB2GRAY);
    cvImage.convertTo(cvImage,CV_32FC1,1.0/255.0);
    CalcBlockMeanVariance(cvImage,res);
    res=1.0-res;
    res=cvImage+res;
    cv::threshold(res,res, 0.85, 1, cv::THRESH_BINARY);
    cv::resize(res, res, cv::Size(res.cols/2,res.rows/2));
    image = [self UIImageFromCVMat:cvImage];

方法：

void CalcBlockMeanVariance(cv::Mat Img,cv::Mat Res,float blockSide=21) // blockSide - the parameter (set greater for larger font on image)
{
    cv::Mat I;
    Img.convertTo(I,CV_32FC1);
    Res=cv::Mat::zeros(Img.rows/blockSide,Img.cols/blockSide,CV_32FC1);
    cv::Mat inpaintmask;
    cv::Mat patch;
    cv::Mat smallImg;
    cv::Scalar m,s;

    for(int i=0;i<Img.rows-blockSide;i+=blockSide)
    {
        for (int j=0;j<Img.cols-blockSide;j+=blockSide)
        {
             patch=I(cv::Rect(j,i,blockSide,blockSide));
            cv::meanStdDev(patch,m,s);
            if(s[0]>0.01) // Thresholding parameter (set smaller for lower contrast image)
            {
                Res.at<float>(i/blockSide,j/blockSide)=m[0];
            }else
            {
                Res.at<float>(i/blockSide,j/blockSide)=0;
            }
        }
    }

    cv::resize(I,smallImg,Res.size());

    cv::threshold(Res,inpaintmask,0.02,1.0,cv::THRESH_BINARY);

    cv::Mat inpainted;
    smallImg.convertTo(smallImg,CV_8UC1,255);

    inpaintmask.convertTo(inpaintmask,CV_8UC1);
    inpaint(smallImg, inpaintmask, inpainted, 5, cv::INPAINT_TELEA);

    cv::resize(inpainted,Res,Img.size());
    Res.convertTo(Res,CV_32FC1,1.0/255.0);

}

知道我为什么得到这个结果？ OCR的结果相当不错，但如果我能得到一张类似你得到的图像会更好。如果重要的话，我正在为iOS开发。我不得不使用cvtColor，因为该方法需要单个通道图像。

Answer 1

这是我的结果： enter image description here

以下是代码：

#include <iostream>
#include <vector>
#include <stdio.h>
#include <stdarg.h>
#include "opencv2/opencv.hpp"
#include "fstream"
#include "iostream"
using namespace std;
using namespace cv;

//-----------------------------------------------------------------------------------------------------
// 
//-----------------------------------------------------------------------------------------------------
void CalcBlockMeanVariance(Mat& Img,Mat& Res,float blockSide=21) // blockSide - the parameter (set greater for larger font on image)
{
    Mat I;
    Img.convertTo(I,CV_32FC1);
    Res=Mat::zeros(Img.rows/blockSide,Img.cols/blockSide,CV_32FC1);
    Mat inpaintmask;
    Mat patch;
    Mat smallImg;
    Scalar m,s;

    for(int i=0;i<Img.rows-blockSide;i+=blockSide)
    {       
        for (int j=0;j<Img.cols-blockSide;j+=blockSide)
        {
            patch=I(Range(i,i+blockSide+1),Range(j,j+blockSide+1));
            cv::meanStdDev(patch,m,s);
            if(s[0]>0.01) // Thresholding parameter (set smaller for lower contrast image)
            {
                Res.at<float>(i/blockSide,j/blockSide)=m[0];
            }else
            {
                Res.at<float>(i/blockSide,j/blockSide)=0;
            }           
        }
    }

    cv::resize(I,smallImg,Res.size());

    cv::threshold(Res,inpaintmask,0.02,1.0,cv::THRESH_BINARY);

    Mat inpainted;
    smallImg.convertTo(smallImg,CV_8UC1,255);

    inpaintmask.convertTo(inpaintmask,CV_8UC1);
    inpaint(smallImg, inpaintmask, inpainted, 5, INPAINT_TELEA);

    cv::resize(inpainted,Res,Img.size());
    Res.convertTo(Res,CV_32FC1,1.0/255.0);

}
//-----------------------------------------------------------------------------------------------------
// 
//-----------------------------------------------------------------------------------------------------
int main( int argc, char** argv )
{
    namedWindow("Img");
    namedWindow("Edges");
    //Mat Img=imread("D:\\ImagesForTest\\BookPage.JPG",0);
    Mat Img=imread("Test2.JPG",0);
    Mat res;
    Img.convertTo(Img,CV_32FC1,1.0/255.0);
    CalcBlockMeanVariance(Img,res); 
    res=1.0-res;
    res=Img+res;
    imshow("Img",Img);
    cv::threshold(res,res,0.85,1,cv::THRESH_BINARY);
    cv::resize(res,res,cv::Size(res.cols/2,res.rows/2));
    imwrite("result.jpg",res*255);
    imshow("Edges",res);
    waitKey(0);

    return 0;
}

Answer 2

JAVA CODE ：自问题提出以来已经过了很长时间，但我已将此代码从C ++重写为Java，以防有人需要它（我需要将其用于在android studio上开发应用程序。）

public Bitmap Thresholding(Bitmap bitmap)
{
    Mat imgMat = new Mat();
    Utils.bitmapToMat(bitmap, imgMat);
    imgMat.convertTo(imgMat, CvType.CV_32FC1, 1.0 / 255.0);

    Mat res = CalcBlockMeanVariance(imgMat, 21);
    Core.subtract(new MatOfDouble(1.0), res, res);
    Imgproc.cvtColor( imgMat, imgMat, Imgproc.COLOR_BGRA2BGR);
    Core.add(imgMat, res, res);

    Imgproc.threshold(res, res, 0.85, 1, Imgproc.THRESH_BINARY);

    res.convertTo(res, CvType.CV_8UC1, 255.0);
    Utils.matToBitmap(res, bitmap);

    return bitmap;
}

public Mat CalcBlockMeanVariance (Mat Img, int blockSide)
{
    Mat I = new Mat();
    Mat ResMat;
    Mat inpaintmask = new Mat();
    Mat patch;
    Mat smallImg = new Mat();
    MatOfDouble mean = new MatOfDouble();
    MatOfDouble stddev = new MatOfDouble();

    Img.convertTo(I, CvType.CV_32FC1);
    ResMat = Mat.zeros(Img.rows() / blockSide, Img.cols() / blockSide, CvType.CV_32FC1);

    for (int i = 0; i < Img.rows() - blockSide; i += blockSide)
    {
        for (int j = 0; j < Img.cols() - blockSide; j += blockSide)
        {
            patch = new Mat(I,new Rect(j,i, blockSide, blockSide));
            Core.meanStdDev(patch, mean, stddev);

            if (stddev.get(0,0)[0] > 0.01)
                ResMat.put(i / blockSide, j / blockSide, mean.get(0,0)[0]);
            else
                ResMat.put(i / blockSide, j / blockSide, 0);
        }
    }

    Imgproc.resize(I, smallImg, ResMat.size());
    Imgproc.threshold(ResMat, inpaintmask, 0.02, 1.0, Imgproc.THRESH_BINARY);

    Mat inpainted = new Mat();
    Imgproc.cvtColor(smallImg, smallImg, Imgproc.COLOR_RGBA2BGR);
    smallImg.convertTo(smallImg, CvType.CV_8UC1, 255.0);

    inpaintmask.convertTo(inpaintmask, CvType.CV_8UC1);
    Photo.inpaint(smallImg, inpaintmask, inpainted, 5, Photo.INPAINT_TELEA);

    Imgproc.resize(inpainted, ResMat, Img.size());
    ResMat.convertTo(ResMat, CvType.CV_32FC1, 1.0 / 255.0);

    return ResMat;
}

Answer 3

由于光几乎是均匀的，前景很容易与背景区分开来。所以我认为直接阈值（使用OTSU）对于OCR是可以的。（几乎与@ Andrey在文本区域中的答案相同）。

Python中的OpenCV 3代码：

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OpenCV自适应阈值OCR

3 个答案: