图片文字提取之路-01预处理

最近突然对机器学习产生了兴趣, 而机器视觉是非常重要的一块。那么，首先来走出第一步，从图片中提取文字信息

图片文字提取是模式识别中一个细小的分支，偶尔看见了一篇叫做《基于高阶神经网络的文字识别》论文，让我有了一个大概的路线，让我们开始吧:

一.图片预处理阶段

以下的代码均采用golang

main方法实现：

package mainimport(    "fmt""os""image/png""image""image/color""sort""strconv")//定义最大处理图片像素const MAX_LENGTH  = 1000//定义图片的长度和高度var dx,dy int//定义图片灰度的二维数组var graySet [1000][1000]int//定义二值化后图片图片的情况，0代表背景，1代表目标var binarizationSet [1000][1000]intvar binarizationSetCopy [1000][1000]intfunc main() {//1.图片灰度化    newPath := Gray("a.png")    //2.对图片二值化    newPath = Binarization(newPath)    //3.对图片进行长线剔除    //4.平滑中值去噪    //newPath = Denoising(newPath)    //5.图片细化    Refinement(newPath)fmt.Println("图片宽:"+strconv.Itoa(dx)+",高:"+strconv.Itoa(dy));}

1.图片灰度化

/** * 加权平均法 　　 * 根据重要性及其它指标，将三个分量以不同的权值进行加权平均。 * 由于人眼对绿色的敏感最高，对蓝色敏感最低， * 因此，按下式对RGB三分量进行加权平均能得到较合理的灰度图像。 　　 * f(i,j)=0.30R(i,j)+0.59G(i,j)+0.11B(i,j)) */func Gray(path string) string {    file, err := os.Open(path)if err != nil {    fmt.Println(err)}defer file.Close()pngFile, _ := png.Decode( file )bounds := pngFile.Bounds()dx = bounds.Max.Xdy = bounds.Max.Ygray := image.NewGray(image.Rect(0, 0, dx, dy))var r32,g32,b32 uint32 var r8,g8,b8 float32var newGray bytefor x := 0; x < dx; x++ {for y := 0; y < dy; y++ {//计算该点的灰度r32, g32, b32, _  = pngFile.At(x, y).RGBA()r8,g8,b8 = float32(r32>>8), float32(g32>>8), float32(b32>>8)newGray = uint8( 0.299 * r8 + 0.587 * g8 + 0.114 * b8 )graySet[x][y] = int(newGray)gray.Set(x, y, color.Gray{newGray})}}newPath := "b.png"newFile, _ := os.Create(newPath)png.Encode(newFile, gray)    return newPath}

处理结果:

2.图片二值化

//图片二值化，阀值固定为180,固定<span style="font-family: Arial, Helvetica, sans-serif;">阀值，可以根据具体实验结果设置</span>func Binarization(path string) string {newPath := "c.png"file, err := os.Open(path)if err != nil {    fmt.Println(err)}defer file.Close()pngFile, _ := png.Decode( file )gray := image.NewGray(image.Rect(0, 0, dx, dy))var r32,g32,b32 uint32 var r,g,b intfor x := 0; x < dx; x++ {for y := 0; y < dy; y++ {//计算该点的灰度r32, g32, b32, _  = pngFile.At(x, y).RGBA()r,g,b = int(r32>>8), int(g32>>8), int(b32>>8)if ( (r+g+b)/3 > 180 ) {binarizationSet[x][y] = 0gray.Set(x, y, color.Gray{uint8(255)})} else {binarizationSet[x][y] = 1gray.Set(x, y, color.Gray{uint8(0)})}}}newFile, _ := os.Create(newPath)png.Encode(newFile, gray)    return newPath}

处理结果:

3.平滑中值去噪

//平滑中值去噪，3*3像素方阵中找灰度中值func Denoising(path string) string {var middleGray uint8    newPath := "d.png"gray := image.NewGray(image.Rect(0, 0, dx, dy))for x := 0; x < dx; x++ {for y := 0; y < dy; y++ {    middleGray = getMiddleGray( x, y )    gray.Set(x, y, color.Gray{middleGray})}}newFile, _ := os.Create(newPath)png.Encode(newFile, gray)    return newPath}//获取该点的灰度中值，如果数量为偶数，则去中间两个值得平均，否者直接去的中值func getMiddleGray( x int, y int) uint8 {    grayWindow := []int{0,0,0,0,0,0,0,0,0}    if(x!=0 && y!=0 && x!=dx-1 && y!=dy-1) {    grayWindow[0] = graySet[x-1][y-1]grayWindow[1] = graySet[x][y-1]grayWindow[2] = graySet[x+1][y-1]grayWindow[3] = graySet[x-1][y]grayWindow[4] = graySet[x][y]grayWindow[5] = graySet[x+1][y-1]grayWindow[6] = graySet[x-1][y+1]grayWindow[7] = graySet[x][y+1]grayWindow[8] = graySet[x+1][y-1]sort.Ints(grayWindow)return uint8(grayWindow[4])} else {    return uint8(graySet[x][y])}}

处理结果：

4.图片细化

//Hilditch算法, 见http://www.cnblogs.com/xiaotie/archive/2010/08/12/1797760.html//（I）：p 为1，即p不是背景；//（2）：x1,x3,x5,x7不全部为1（否则把p标记删除，图像空心了）；//（3）：x1~x8 中，至少有2个为1（若只有1个为1，则是线段的端点。若没有为1的，则为孤立点）；//（4）：p的8连通联结数为1；///      计算八联结的联结数，计算公式为： ///      (p6 - p6*p7*p0) + sigma(pk - pk*p(k+1)*p(k+2)), k = {0,2,4) //（5）假设x3已经标记删除，那么当x3为0时，p的8联通联结数为1；//（6）假设x5已经标记删除，那么当x5为0时，p的8联通联结数为1。// x4 x3 x2// x5 p  x1// x6 x7 x8//// p3 p2 p1// p4 p  p0// p5 p6 p7func Hilditch(x int, y int) int {    neighor := []int{0,0,0,0,0,0,0,0} if(x!=0 && y!=0 && x!=dx-1 && y!=dy-1) {neighor[0] = binarizationSet[x+1][y]  //x1neighor[1] = binarizationSet[x+1][y-1] //x2neighor[2] = binarizationSet[x][y-1] //x3neighor[3] = binarizationSet[x-1][y-1] //x4neighor[4] = binarizationSet[x-1][y] //x5neighor[5] = binarizationSet[x-1][y+1] //x6neighor[6] = binarizationSet[x][y+1] //x7neighor[7] = binarizationSet[x+1][y+1] //x8if( binarizationSetCopy[x][y-1] == 1 && binarizationSet[x][y-1] == 0 ) {neighor[2] = 1       }if( binarizationSetCopy[x-1][y] == 1 && binarizationSet[x-1][y] == 0 ) {neighor[4] = 1       }number := 0for i:= 0 ; i<8 ; i++ {if neighor[i] == 1 {number ++}}//获取联通连接数unicomConnection := unicomConnectionCalc( neighor )//条件2if neighor[0]*neighor[2]*neighor[4]*neighor[6] == 0 {  //条件3if number >= 2 {//条件4if unicomConnection == 1 {//条件5if( binarizationSetCopy[x][y-1] == 1 && binarizationSet[x][y-1] == 0 ) {neighor[2] = 0unicomConnection := unicomConnectionCalc( neighor )if( unicomConnection != 1 ) {    //条件不满足   return 1}neighor[2] = 1}//条件6if( binarizationSetCopy[x-1][y] == 1 && binarizationSet[x-1][y] == 0 ) {neighor[4] = 0unicomConnection := unicomConnectionCalc( neighor )if( unicomConnection != 1 ) {    //条件不满足   return 1}}return 0}}}}return 1}//Hilditch算法,用于获取联通数func unicomConnectionCalc( neighor []int ) int {    unicomConnection := neighor[6]-neighor[6]*neighor[7]*neighor[0]unicomConnection = unicomConnection + neighor[0]-neighor[0]*neighor[1]*neighor[2]unicomConnection = unicomConnection + neighor[2]-neighor[2]*neighor[3]*neighor[4]unicomConnection = unicomConnection + neighor[4]-neighor[4]*neighor[5]*neighor[6]return unicomConnection}

处理结果

如果有不正确的地方，还请指正，谢谢!

未完待续...