這是基於opencv寫的數字識別程式 對於記事本里的宋體常規數字能夠完美的識別

思路:   放大圖片→灰度化→二值化→開運算→尋找外輪廓→輪廓排序→遍歷畫素與模板匹配→得到數字

最後為了驗證識別率，讀入txt檔案與識別圖片後的輸出結果對比

總結：

1.圖片放大可以把各個數字分開一點，防止數字黏在一起導致找輪廓時出錯

2.開運算，進一步把各個數字分開  開運算(膨脹->腐蝕)//去除影象中較小區域

3.尋找外輪廓，必須是外輪廓，不可以找所有輪廓，這樣就可以等到各個數字

4.根據輪廓找出各個輪廓的最小矩形

5.根據最小矩形左上角的座標為各個矩形排序，這樣就可以知道各個字元輸出順序了

6,.把事先分割好的模板圖片進行匹配（把這個程式改改就可以成一個分割數字的程式了）

7.最後讀入txt檔案與圖片輸出結果對比

#include<opencv2/highgui/highgui.hpp>
#include<opencv2/imgproc/imgproc.hpp>
#include<iostream>

#include <fstream>
#include <cassert>
#include <string>
using namespace std;
using namespace cv;

const int NUM = 10000;
#define WINDOW_NAME "二值圖"
int g_nThreshold = 200;
static void on_Threshold(int, void*);
void getNumber();//分割字元
char getNumChar(Mat&);//識別單個字元圖片

Mat g_srcImage, gray, image_threshold;
Mat open;

char result_num[NUM];//txt檔案中的數字
char result_true[NUM];//識別後的數字

/*讀取txt檔案*/
void readTxt(string file)
{
	ifstream infile;
	infile.open(file.data());   //將檔案流物件與檔案連線起來 
	assert(infile.is_open());   //若失敗,則輸出錯誤訊息,並終止程式執行 
	int i = 0;
	char c;
	while (!infile.eof())
	{
		infile >> c;
		result_true[i] = c;
		result_true[i + 1] = '\0';
		i++;
	}
	infile.close();
}
int main()
{
	readTxt("123.txt");
	cout << "檔案值為:";
	for (unsigned int i = 0; i < strlen(result_true)-1; i++)//讀取txt輸出數字
	{
		cout << result_true[i];
	}
	cout << endl;
	g_srcImage = imread("1.png");
	//放大原圖，圖片太小數字可能會連在一起
	resize(g_srcImage, g_srcImage, Size(g_srcImage.cols * 2, g_srcImage.rows * 2), 0, 0, INTER_LINEAR);
	cvtColor(g_srcImage, gray, COLOR_BGR2GRAY);
	namedWindow(WINDOW_NAME, WINDOW_AUTOSIZE);
	createTrackbar("閾值", WINDOW_NAME, &g_nThreshold, 255, on_Threshold);
	on_Threshold(g_nThreshold, 0);
	waitKey(0);
	return 0;
}

/*閾值操作*/
static void on_Threshold(int, void*)
{
	threshold(gray, image_threshold, g_nThreshold, 255, 1);
	imshow(WINDOW_NAME, image_threshold);
	Mat element = getStructuringElement(MORPH_RECT, Size(3, 3));
	morphologyEx(image_threshold, open, MORPH_OPEN, element);//開運算，防止圖片連在一起
	getNumber();
}

/*單字元識別*/
char getNumChar(Mat &temp)
{
	int min = 10000;
	int min_i = 0;
	int total = 0;
	for (int n = 0; n < 10; n++)//n對應圖片n.jpg
	{
		total = 0;
		char a[100];
		sprintf(a, "number/%d.jpg", n);//讀取number模板資料夾圖片，名字對應數字
		string name = a;
		Mat match = imread(name); 
		cvtColor(match, match, COLOR_BGR2GRAY);
		threshold(match, match, 254, 255, 1);
		resize(temp, temp, match.size(), 0, 0, INTER_LINEAR);//轉換圖片大小和模板圖片一樣
		for (int i = 0; i < match.rows; i++)//遍歷所有畫素與n.jpg對比
		{
			uchar *data_match = match.ptr<uchar>(i);
			uchar *data_temp = temp.ptr<uchar>(i);
			for (int j = 0; j < match.cols; j++)
			{
				if (*data_match == *data_temp)
				{
					total++;
				}
				*data_match++;
				*data_temp++;
			}
		}
		//讀入的圖片二值化後與原圖二值化相同畫素為0
		//讀入的圖片二值化與程式執行過程中二值化的產生圖片剛好相反
		//所以相同的畫素越少圖片相似度越高
		//在這裡match二值化後與temp二值化圖相反即黑白正好顛倒了
		if (total < min)
		{
			min = total;
			min_i = n;	
		}
	}
	char a = min_i+'0';
	return a;
}

/*字元分割*/
void getNumber()
{
	Mat image_threshold_clone = image_threshold.clone();
	vector<vector<Point>>contours;
	vector<Vec4i>hierarchy;
	findContours(image_threshold_clone, contours, hierarchy,
		CV_RETR_EXTERNAL, CV_CHAIN_APPROX_SIMPLE);//找出圖片外輪廓，不要連內輪廓也找出來
	int contour_size = contours.size();
	int average_height = 0;
	int count = 0;
	Rect rc[NUM];
	int min_y =100000;
	int max_y = 0;
	//找出字元平均高度，最上方的數字的y，最下方的數字的y
	for (int i = 0; i < contour_size; i++)
	{
		rc[i] = boundingRect(contours.at(i));
		
		if (rc[i].y<min_y)
		{
			min_y = rc[i].y;
		}
		if (rc[i].y>max_y)
		{
			max_y = rc[i].y;
		}
		average_height += rc[i].height;
		
	}
	average_height /= contour_size;
	Rect sort_rc[NUM];
	Rect rc_y[NUM];
	int _t = 0;
	//由於輪廓順序不是字元順序所以要排序，主要根據矩形左上方座標排序
	//排序從上到下，從左到右，每隔一個字元高度獲取一次，當超出圖片邊界時停止
	for (int n = 0;; n++)
	{
		int t = 0;
		for (int i = 0; i < contour_size; i++)
		{
			if (min_y - average_height*0.5 < rc[i].y&& rc[i].y < min_y + average_height*0.5)
			{
				rc_y[t] = rc[i];
				t++;
			}
		}
		if (min_y>max_y)
		{
			break;
		}
		if (t == 0)
		{
			_t += t;
			min_y += average_height;
			continue;
		}
		for (int i = 0; i < t; i++)
		{
			for (int j = i+1; j < t; j++)
			{
				if (rc_y[i].x>rc_y[j].x)
				{
					Rect temp = rc_y[i];
					rc_y[i] = rc_y[j];
					rc_y[j] = temp;
				}
			}
		}
		for (int i = 0; i < t; i++)
		{
			sort_rc[i + _t] = rc_y[i];
		}
		_t += t;
		min_y += average_height;
	}
	//把排序後的圖片一一識別
	for (int i = 0; i < contour_size; i++)
	{
		Mat ROI = image_threshold(sort_rc[i]);
		resize(ROI, ROI, Size(40, 56), 0, 0, INTER_LINEAR);
		result_num[i] = getNumChar(ROI);
		result_num[i + 1] = '\0';
	}
	//與txt檔案的數字進行比較
	int equal_num = 0;
	cout << "結果值為:";
	for (unsigned int i = 0; i < strlen(result_num); i++)
	{
		cout << result_num[i];
		if (result_num[i] == result_true[i])
			equal_num++;
	}
	cout << "\n字元數為:" << contour_size << endl;
	cout << "正確率為:" << (double)equal_num / contour_size *100<<"%"<< endl;
}




 

當然識別數字後你想幹嘛 就是你自己的是了