python - Pytesseract 没有给出预期的输出

Question

我是 pyhton 的新手，我正在使用 haar 级联制作车牌识别系统。我的代码可以很好地检测车牌并制作轮廓，但 pytesseract ocr 无法识别字符并给出奇怪的结果。请帮忙。

The detected plate using haar cascades

contours made on the detected region

This is the output

import cv2
import numpy as np
import pytesseract

plate_cascade = cv2.CascadeClassifier('C:/Users/Jai/Desktop/Test/haarcascade_plate.xml')

img = cv2.imread('C:/Users/Jai/Desktop/Test/images/NumberPlates/IMG_20181029_194221.jpg', cv2.IMREAD_COLOR)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.array(gray, dtype='uint8')
cv2.imshow('gray', gray)

plates = plate_cascade.detectMultiScale(gray, 1.3, 5)

for (x,y,w,h) in plates:
    cv2.rectangle(img, (x,y), (x+w,y+h),(255,0,0),5)
    roiGray = gray[y:y+h, x:x+w]
    roiImg = img[y:y+h, x:x+w]

roiUGray = cv2.bitwise_not(roiGray)
cv2.imshow('img', img)
cv2.imshow('roi_gray', roiUGray)

ret, thresh = cv2.threshold(roiUGray, 127, 255,0)
cv2.imshow("img", img)
height, width = thresh.shape
newImage = np.zeros((height, width, 3), np.uint8)
newImage[:, :] = (0,0,0)
im2, contours, hierarchy = cv2.findContours(thresh, cv2.RETR_TREE, cv2.CHAIN_APPROX_NONE)

num = 0
area = 0

for j in range(len(contours)):
    if hierarchy[0][j][3] != -1:
        area = area + cv2.contourArea(contours[j])
        num += 1

if num != 0:
    avgArea = float(area)/float(num)

for j in range(len(contours)):
    if hierarchy[0][j][3] != -1 and cv2.contourArea(contours[j]) > 0.2*avgArea:
        cv2.drawContours(newImage, contours[j], -1, (255,255,255), 1)

blur = cv2.GaussianBlur(newImage, (1, 1), 0)
cv2.imshow("roi", blur)

ocr_result = pytesseract.image_to_string(blur, lang='eng')
print(ocr_result)

cv2.waitKey(0)
cv2.destroyAllWindows()

Answer 1

该方法使用轮廓检测​​找到车牌区域，并对其进行透视变换。然后，它使用自适应阈值检测数字并执行 medianBlur 以消除扰乱 pytesseract 操作的噪声。

蓝色框来自原始图像。红框来 self 的车牌识别haar级联。绿色为车牌轮廓检测。

这是透视变换的输出。我使用 imutils 模块来执行此操作。

这是自适应阈值化和模糊化的输出，我使用了 skimage 模块。

使用它我得到了输出:

\fUP1ADN7120

并删除所有不是大写或数字的字符得到:

UP1ADN7120

只错了一个字

这个方法还不错，但你可以做得更好using another method.如果这对您不起作用，那么您可以创建一个 CNN。

代码如下:

import cv2
import numpy as np
import pytesseract
import imutils.perspective
from skimage.filters import threshold_local

plate_cascade = cv2.CascadeClassifier('/usr/local/lib/python3.5/dist-packages/cv2/data/haarcascade_russian_plate_number.xml')

img = cv2.imread('GNA0d.jpg', cv2.IMREAD_COLOR)

gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
gray = np.array(gray, dtype='uint8')

plates = plate_cascade.detectMultiScale(gray, 1.3, 5)

for (x,y,w,h) in plates:
    cv2.rectangle(img, (x,y), (x+w,y+h),(0,0,255),2)
    roiGray = gray[y:y+h, x:x+w]
    roiImg = img[y:y+h, x:x+w]

blur = cv2.GaussianBlur(roiGray, (5, 5), 0)
edges = cv2.Canny(blur, 75, 200)

contours = cv2.findContours(edges.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[1]
contours = sorted(contours, key = cv2.contourArea, reverse = True)[:5]  #sort the contours, only getting the biggest to improve speed

for contour in contours:
    peri = cv2.arcLength(contour, True)
    approx = cv2.approxPolyDP(contour, 0.02 * peri, True)

    #find contours with 4 edges
    if len(approx) == 4:
        screenCnt = approx
        break

orig = roiImg.copy()
cv2.drawContours(roiImg, [screenCnt], -1, (0, 255, 0), 2)

#do a perspective transform
warped = imutils.perspective.four_point_transform(orig, screenCnt.reshape(4, 2))
graywarp = imutils.perspective.four_point_transform(roiGray, screenCnt.reshape(4, 2))

#threshold using adaptive thresholding
T = threshold_local(graywarp, 11, offset = 10, method = "gaussian")
graywarp = (graywarp > T).astype("uint8") * 255

#do a median blur to remove noise
graywarp = cv2.medianBlur(graywarp, 3)

text = pytesseract.image_to_string(graywarp)
print(text)
print("".join([c for c in text if c.isupper() or c.isdigit()]))


cv2.imshow("warped", warped)
cv2.imshow("graywarp", graywarp)
cv2.imshow('img', img)


cv2.waitKey(0)
cv2.destroyAllWindows()

相当一部分来自pyimagesearch ，谁比我解释得更好。