OpenCV+Tesseract进行OCR学习（一）文字提取

OpenCV+Tesseract进行OCR学习（一）文字提取

OpenCVTesseract进行OCR学习一文字提取

OCR简介

文字提取

code

OCR简介

熟悉OCR的人都了解，OCR大致分为两个部分：

-文字提取text extractor

-文字识别text recognition

其中，第一部分是属于图像处理部分，涉及到图像分割的知识，而第二部分则大多数利用谷歌的Tesseract来进行字符的识别，设计到的东西不多，当然也不难，难的是要能够做到非常准确的识别率，以及它的识别速率。

文字提取

这一部分工作是很关键的，因为文字提取的好坏，直接影响到最后的识别结果，相当于预处理部分，是非常重要的，其主要目的是为了分割出文字字符。

主要涉及工作有：

: -灰度化
-直方图均衡

-分块处理

-锐化

-Otsu

-处理0和1边界值

-如果有必要，还需要进行噪声去除，这里要涉及到找连通分量的相关计算；

code

你看，是不是很简单呀。。好辣，下面附上代码：

void TextDetector::segmentText(cv::Mat &spineImage, cv::Mat &segSpine, bool removeNoise){

cv::Mat spineGray;
cvtColor(spineImage, spineGray, CV_BGR2GRAY);
imshow("gray source" , spineGray);
spineGray = spineGray - 0.5;
//    WriteData("/Users/eternity/Desktop/未命名文件夹/gray1.txt", spineGray);
//    waitKey();
cv::Mat spineAhe;
adaptiveHistEqual(spineGray, spineAhe, 0.01);
imshow("ahe", spineAhe);
//    WriteData("/Users/eternity/Desktop/未命名文件夹/gray2.txt", spineAhe);

int window_num = 40;

double window_h = (spineImage.rows / (double)window_num + 1e-3);

int window_w = spineImage.cols;

cv::Mat spine_th = cv::Mat::zeros(spineGray.size(), CV_8U);

for (int i = 0; i < window_num; i ++) {
double cut_from_r = window_h * i;
double cut_to_r = window_h * (i+1);
cv::Mat window_img = cv::Mat::zeros(Size(cut_to_r-cut_from_r + 1, window_w), CV_8U);
cv::Rect rect = cv::Rect(0, cut_from_r, window_w-1, cut_to_r - cut_from_r + 1);
window_img = cv::Mat(spineGray, rect);
imshow("window section", window_img);

sharpenImage(window_img, window_img);
imshow("sharpen", window_img);
//        waitKey();
//        WriteData("/Users/eternity/Desktop/未命名文件夹/gray4.txt", window_img);
double max_local,min_local;
minMaxLoc(window_img, &min_local, &max_local);
double color_diff = max_local - min_local;
double thresh;
cv::Mat window_tmp;
if (color_diff > 50)
thresh = threshold(window_img, window_tmp, 1, 255, THRESH_OTSU);
else
thresh = 0;
//        cout<<thresh<<endl;
cv::Mat seg_window(window_img.size(), CV_64F);
imgQuantize(window_img, seg_window, thresh);
//        WriteData("/Users/eternity/Desktop/未命名文件夹/quantize2.txt", seg_window);
seg_window = seg_window == 1;
//        seg_window = seg_window / 255;
//处理0边界值
vector<int> cols1,cols2,rows1,rows2;
findKEdgeFirst(seg_window, 0, 5, rows1, cols1);
findKEdgeLast (seg_window, 0, 5, rows2, cols2);
float max_zero_dist, max_one_dist;
if(cols1.empty() || cols2.empty())
max_zero_dist = 0.0;
else{
float avg_right = (rows2[0]+rows2[1]+rows2[2]+rows2[3]+rows2[4]) / (float)sizeof(rows2);
float avg_left  = (rows1[0]+rows1[1]+rows1[2]+rows1[3]+rows1[4]) / (float)sizeof(rows1);
max_zero_dist = avg_right - avg_left;
}
cols1.clear();
cols2.clear();
rows1.clear();
rows2.clear();

//处理1边界值
findKEdgeFirst(seg_window, 255, 5, rows1, cols1);
findKEdgeLast (seg_window, 255, 5, rows2, cols2);
if(cols1.empty() || cols2.empty())
max_one_dist = 0;
else{
float avg_right = (rows2[0]+rows2[1]+rows2[2]+rows2[3]+rows2[4]) / (float)sizeof(rows2);
float avg_left  = (rows1[0]+rows1[1]+rows1[2]+rows1[3]+rows1[4]) / (float)sizeof(rows1);
max_one_dist = avg_right - avg_left;
}
cols1.clear();
cols2.clear();
rows1.clear();
rows2.clear();

cv::Mat idx;
findNonZero(seg_window, idx);
int one_count = (int)idx.total();
int zero_count = (int)seg_window.total() - one_count;

float one_zero_diff = max_one_dist - max_zero_dist;
float  dist_limit = 5;

if(one_zero_diff > dist_limit)
seg_window = ~ seg_window;
else{
if(one_zero_diff > -dist_limit && one_count > zero_count)
seg_window = ~ seg_window;
}

seg_window.copyTo(cv::Mat( spine_th, rect));
//        imshow("spine_th", spine_th);
//        waitKey();

}
//去除噪声
if (removeNoise) {
vector<vector<cv::Point>> contours;
imshow("spine_th", spine_th);
//        WriteData("/Users/eternity/Desktop/未命名文件夹/quantize1.txt", spine_th);
//        waitKey();
findContours(spine_th, contours, RETR_EXTERNAL, CHAIN_APPROX_NONE);

for (int i = 0; i < contours.size(); i ++) {
//compute bounding rect
cv::Rect rect = boundingRect(contours[i]);
double bbox_aspect = rect.width / (double)rect.height;
int bbox_area = rect.width * rect.height;
//compute solidity
vector<vector<Point>> hull(1);
convexHull( contours[i], hull[0] );
double convex_area = contourArea(hull[0]);
double solidity = bbox_area / convex_area;

for (int j = 0; j < contours[i].size(); j ++) {
if ( (rect.width > spineImage.cols / 1.001)
|| (rect.width > spineImage.cols / 1.4 && bbox_aspect > 5.0)
|| (rect.height > spineImage.cols / 1.1)
|| (bbox_area < pow(spineImage.cols/30, 2))
|| (bbox_aspect > 0.5 && bbox_aspect < 1.7 && solidity > 0.9) )

spine_th.at<int>(contours[i][j].x, contours[i][j].y) = 0;
//                WriteData("/Users/eternity/Desktop/未命名文件夹/quantize2.txt", spine_th);
}

}

}
segSpine = spine_th;
//    transpose(segSpine, segSpine);
//    flip(segSpine, segSpine, 0);
imshow("segspine", segSpine);
//    waitKey();
spine_th.release();

}
//对图片进行level量化
void TextDetector::imgQuantize(cv::Mat &src, cv::Mat &dst, double level){
dst = cv::Mat::zeros(src.rows, src.cols, CV_8U);
for (int i = 0; i < src.rows; i ++) {
uchar *data = src.ptr<uchar>(i);
uchar *data2 = dst.ptr<uchar>(i);
for (int j = 0; j < src.cols; j ++) {
if(data[j] <= level)
data2[j] = 1;
else
data2[j] = 2;

}
}

}
//找出最左边界处，前edgeValue个值为k的边界值
void TextDetector::findKEdgeFirst(cv::Mat &data, int edgeValue,int k,vector<int> &rows,vector<int> &cols){
int count = 0;
for (int i = 0; i < data.cols; i ++) {
uchar *u = data.ptr<uchar>(i);
for (int j = 0; j < data.rows; j ++) {
if(edgeValue == (int)u[j]){
if(count < k){
count ++;
cols.push_back(i);
rows.push_back(j);
}

}

}
}

}
//找出最右边界处，倒数edgeValue个值为k的边界值
void TextDetector::findKEdgeLast(cv::Mat &data, int edgeValue,int k,vector<int> &rows, vector<int> &cols){
int count = 0;
for (int i = data.cols - 1; i >= 0; i --) {
uchar *u = data.ptr<uchar>(i);
for (int j = data.rows - 1; j >= 0; j --) {
if(edgeValue == (int)u[j]){
if(count < k){
count ++;
cols.push_back(i);
rows.push_back(j);
}

}
}

}

}
//直方图均衡
void TextDetector::adaptiveHistEqual(cv::Mat &src,cv::Mat &dst,double clipLimit)
{
Ptr<cv::CLAHE> clahe = createCLAHE();
clahe->setClipLimit(clipLimit);
clahe->apply(src, dst);
}