使用Python+OpenCV2进行图片中的文字分割（支持竖版）

把图片中的文字，识别出来，并将每个字的图片抠出来；

import cv2
import numpy as np

HIOG = 50
VIOG = 3
Position = []
 
'''水平投影'''
def getHProjection(image):
    hProjection = np.zeros(image.shape,np.uint8)
    # 获取图像大小
    (h,w)=image.shape
    # 统计像素个数
    h_ = [0]*h
    for y in range(h):
        for x in range(w):
            if image[y,x] == 255:
                h_[y]+=1
    #绘制水平投影图像
    for y in range(h):
        for x in range(h_[y]):
            hProjection[y,x] = 255
    # cv2.imshow('hProjection2',cv2.resize(hProjection, None, fx=0.3, fy=0.5, interpolation=cv2.INTER_AREA))
    # cv2.waitKey(0)
    return h_
 
def getVProjection(image):
    vProjection = np.zeros(image.shape,np.uint8);
    (h,w) = image.shape
    w_ = [0]*w
    for x in range(w):
        for y in range(h):
            if image[y,x] == 255:
                w_[x]+=1
    for x in range(w):
        for y in range(h-w_[x],h):
            vProjection[y,x] = 255
    # cv2.imshow('vProjection',cv2.resize(vProjection, None, fx=1, fy=0.1, interpolation=cv2.INTER_AREA))
    # cv2.waitKey(0)
    return w_


def scan(vProjection, iog, pos = 0):
    start = 0
    V_start = []
    V_end = []

    for i in range(len(vProjection)):
        if vProjection[i] > iog and start == 0:
            V_start.append(i)
            start = 1
        if vProjection[i] <= iog and start == 1:
            if i - V_start[-1] < pos:
                continue
            V_end.append(i)
            start = 0
    return V_start, V_end


def checkSingle(image):
    h = getHProjection(image)
    start = 0
    end = 0

    for i in range(h):
        pass

#分割
def CropImage(image,dest,boxMin,boxMax):
    a=boxMin[1]
    b=boxMax[1]
    c=boxMin[0]
    d=boxMax[0]
    cropImg = image[a:b,c:d]
    cv2.imwrite(dest,cropImg)
    
#开始识别
def DOIT(rawPic):
        # 读入原始图像
    origineImage = cv2.imread(rawPic)
    # 图像灰度化   
    #image = cv2.imread('test.jpg',0)
    image = cv2.cvtColor(origineImage,cv2.COLOR_BGR2GRAY)
    
    # cv2.imshow('gray',image)
    # 将图片二值化
    retval, img = cv2.threshold(image,127,255,cv2.THRESH_BINARY_INV)
    # kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (5, 5))
    # img = cv2.erode(img, kernel)
    # cv2.imshow('binary',cv2.resize(img, None, fx=0.3, fy=0.3, interpolation=cv2.INTER_AREA))
    #图像高与宽
    (h,w)=img.shape
    #垂直投影
    V = getVProjection(img)
 
    start = 0
    V_start = []
    V_end = []

    # 对垂直投影水平分割
    V_start, V_end = scan(V, HIOG)
    if len(V_start) > len(V_end):
        V_end.append(w-5)

    # 分割行，分割之后再进行列分割并保存分割位置
    for i in range(len(V_end)):
        #获取行图像
        if V_end[i] - V_start[i] < 30:
            continue

        cropImg = img[0:h, V_start[i]:V_end[i]]
        # cv2.imshow('cropImg',cropImg)
        # cv2.waitKey(0)
        #对行图像进行垂直投影
        H = getHProjection(cropImg)
        H_start, H_end = scan(H, VIOG, 40)


        if len(H_start) > len(H_end):
            H_end.append(h-5)

        for pos in range(len(H_start)):
            # 再进行一次列扫描
            DcropImg = cropImg[H_start[pos]:H_end[pos], 0:w]
            d_h, d_w = DcropImg.shape
            # cv2.imshow("dcrop", DcropImg)
            sec_V = getVProjection(DcropImg)
            c1, c2 = scan(sec_V, 0)
            if len(c1) > len(c2):
                c2.append(d_w)

            x = 1
            while x < len(c1):
                if c1[x] - c2[x-1] < 12:
                    c2.pop(x-1)
                    c1.pop(x)
                    x -= 1
                x += 1

            # cv2.waitKey(0)
            if len(c1) == 1:
                Position.append([V_start[i],H_start[pos],V_end[i],H_end[pos]])
            else:
                for x in range(len(c1)):
                    Position.append([V_start[i]+c1[x], H_start[pos],V_start[i]+c2[x], H_end[pos]])

    #根据确定的位置分割字符
    number=0
    for m in range(len(Position)):
        rectMin =  (Position[m][0]-5,Position[m][1]-5)
        rectMax =  (Position[m][2]+5,Position[m][3]+5)
        cv2.rectangle(origineImage,rectMin, rectMax, (0 ,0 ,255), 2)
        number=number+1
        #start-crop
        CropImage(origineImage,'result/' + '%d.jpg' % number,rectMin,rectMax)
    # cv2.imshow('image',cv2.resize(origineImage, None, fx=0.6, fy=0.6, interpolation=cv2.INTER_AREA))
    cv2.imshow('image',origineImage)
    cv2.imwrite('result/' + 'ResultImage.jpg' , origineImage)
    cv2.waitKey(0)

#############################
rawPicPath = r"H:\TEMP\TEXT_PROCCESS\TEST05.jpg"
DOIT(rawPicPath)
#############################