文本检测之OpenCV实现

目录

1、理论依据

2、程序实现

1）图像灰度化

 2）文本行分割

3）通过形态学腐蚀，将分割出来的文字字符连接在一起

4）提取文本行所在的轮廓

5）提取的文本行所在轮廓最小矩形框，并在原图标记检测矩形框

6）检测结果

3、VS2015工程

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运行环境：Windows 10 64 位 + Opencv3.4.1 + Visual Studio 2015

1、理论依据

针对水平方向排列分布的印刷体文本，因文本行中字符沿着近似水平直线分布。所以，沿着水平方向提取文本行所在矩形框，即可将文本行检测出来。如下图中的文本沿着红色直线水平分布：

2、程序实现

1）图像灰度化

将输入图像转换成灰度图像

//Color image to grayscale image
cvtColor(src, gray, CV_BGR2GRAY);

 2）文本行分割

利用 OTSU 二值化方法，将文本行与背景分割。

//Binary segmentation of text lines
threshold(gray, binary, 0, 255, THRESH_OTSU | THRESH_BINARY);

3）通过形态学腐蚀，将分割出来的文字字符连接在一起

//Define the erodent core
Mat rec = getStructuringElement(MORPH_RECT, Size(50, 3));

//Etch binary image along horizontal, join text line
Mat dilate0;
erode(binary, dilate0, rec);

Mat erode2;

//Image Reverse
bitwise_not(dilate0, erode2);

4）提取文本行所在的轮廓

vector<RotatedRect> rects;
vector<vector<Point>> counts;
vector<Vec4i> hierarchy;


//Extract the contour of the text line
findContours(erode2, counts, hierarchy, CV_RETR_LIST, CHAIN_APPROX_SIMPLE, Point(0, 0));

5）提取的文本行所在轮廓最小矩形框，并在原图标记检测矩形框

//Mark the detection rectangles in the original image
for (int i = 0; i<counts.size(); i++)
{
	//Culling of small contours
	if (contourArea(counts[i])<500)
			continue;
	//Calculates the smallest rectangle with a vertical boundary for an contour
	Rect  rect1 = boundingRect(counts[i]);
	char ch[256];

		
	cout << "hierarchy  num" << hierarchy[i] << endl << endl;
	/*	RotatedRect rect = minAreaRect(counts[i]);
	int width = rect.boundingRect().width;
	int heigh = rect.boundingRect().height;
	Point2f P[4];
	rect.points(P);
	for (int j = 0; j <= 3; j++)
	{
	line(input, P[j], P[(j + 1) % 4], Scalar(255,0,0), 1);
	}*/

	//Drawing Rectangular box
	rectangle(dst, rect1, Scalar(0, 0, 255), 1);

}

6）检测结果

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