手写体识别（基于Opencv）

最新推荐文章于 2021-11-21 16:06:22 发布

Ericyinmw

最新推荐文章于 2021-11-21 16:06:22 发布

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CC 4.0 BY-SA版权

本文链接：https://blog.youkuaiyun.com/qq_26340121/article/details/51804667

该博客介绍了一种使用OpenCV进行手写体识别的方法。首先通过HoughLines找到图像中的直线，确定旋转角度，然后进行边缘检测、线段交点查找，最后通过旋转、形态学操作和区域分割对手写体图像进行预处理，为识别做好准备。

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#include "cv.h"
#include "highgui.h"
#include "iostream"
#include "string"
#include "cmath"
#include <sstream>
#define PI 3.1415926
using namespace std;
using namespace cv;
FILE* fp;
struct Lines{
	double k;
	double b;
	bool per;
};
double rotationAngle = 0.0;
bool LineCmp(const Vec2f& a, const Vec2f& b) {
	double threshold = 0.1;
	if (abs(a[1] - b[1]) < threshold) {
		return a[0] < b[0];
	}
	return a[1] < b[1];
}

bool PointCmp(const Point2f& a, const Point2f& b) {
	return a.x + a.y < b.x + b.y;
}
vector<Lines> Findlines(Mat img) {
	vector<Vec2f> lines;
	vector<Lines> mls;
	//rotationAngle = 0.0;
	HoughLines(img, lines, 1, CV_PI / 100, 80, 0, 0);
	sort(lines.begin(), lines.end(), LineCmp);
	int last = lines.size() - 1;
	const double minTheta = 0.1;
	const double maxTheta = 3.1;
	const double rhoThreshold = 50;<a target=_blank href="https://github.com/yinmingwang/identifyhandwriting">github链接</a>

	while (lines[0][1] <= minTheta && lines[last][1] >= maxTheta) {
		vector<Vec2f>::iterator it = lines.begin() + 1;
		for (size_t i = 1; i < last; ++i) {
			if (lines[i][1] <= minTheta && abs(lines[0][0] - lines[i][0]) < rhoThreshold) {
				++it;
			}
		}
		lines.insert(it, lines[last]);
		it = lines.end() - 1;
		lines.erase(it);
	}

	rotationAngle = lines[0][1] * 180 / CV_PI;
	const double rotationThreshold = 0.1;
	if (lines[0][1] > rotationThreshold) {
		rotationAngle -= 90;
	}
	for (size_t i = 0; i < lines.size(); i++) {
		float rho = lines[i][0], theta = lines[i][1];
		const float deltaRho = 200;
		const float deltaTheta = 0.25;
		if (i > 0 && abs(abs(rho) - abs(lines[i - 1][0])) < deltaRho &&
			(abs(CV_PI - (theta + lines[i - 1][1])) < deltaTheta || abs(theta - lines[i - 1][1]) < deltaTheta)) {
			vector<Vec2f&g