OpenCV tesserocr 水印检测

Question

所以我的 SQL table 中有大约 12000 个图像链接。重点是检测这些图像中哪些包含水印文本，哪些不包含。所有的文字和边框都是这样的。

我尝试过使用 OpenCV 和 tesserocr

img_data = requests.get(url).content
img = Image.open(BytesIO(img_data))
print(url, tesserocr.image_to_text(img).strip())

但它似乎根本无法识别图像上的文字。

我的第二种方法是使用一些外部开放 API 站点。

for url,id in alldata:
body = {'language':'eng',
        'isOverlayRequired':'false',
        'url':url,
        'iscreatesearchablepdf':'false',
        'issearchablepdfhidetextlayer':'false',
        'filetype':'jpeg'}

headers = {
    'apikey':'xxxxxxx'
}
rsp =  requests.post('https://api.ocr.space/parse/image', body, headers= headers)

它可以工作，但是超级慢。对于 11000 张图像，它会持续几天。

Answer 1

由于文本高度或文本大小较小，tesserocr 未检测到任何文本。通过裁剪文本区域并使用该图像，pytesseract 可以提取文本。由于文本尺寸较小，使用轮廓和扩张来检测文本区域也不起作用。为了检测文本区域，我使用了 EAST model to extract all regions using solution and combined all the regions. Passing the extracted combined region image to tesseract returns the text. To run this script, You need to download the model which can be found here 并安装了所需的依赖项。
Python 脚本:

import numpy as np
import cv2
from imutils.object_detection import non_max_suppression
import matplotlib.pyplot as plt
import pytesseract

pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe" # I am using windows

image1 = cv2.imread("r9Do4.png",cv2.IMREAD_COLOR) 
ima_org = image1.copy()
(height1, width1) = image1.shape[:2]
size = 640 #size must be multiple of 32. Haven't tested with smaller size which can increase speed but might decrease accuracy.
(height2, width2) = (size, size)  
image2 = cv2.resize(image1, (width2, height2))  


net = cv2.dnn.readNet("frozen_east_text_detection.pb")
blob = cv2.dnn.blobFromImage(image2, 1.0, (width2, height2), (123.68, 116.78, 103.94), swapRB=True, crop=False)
net.setInput(blob)

(scores, geometry) = net.forward(["feature_fusion/Conv_7/Sigmoid", "feature_fusion/concat_3"])
(rows, cols) = scores.shape[2:4]  # grab the rows and columns from score volume
rects = []  # stores the bounding box coordiantes for text regions
confidences = []  # stores the probability associated with each bounding box region in rects

for y in range(rows):
    scoresdata = scores[0, 0, y]
    xdata0 = geometry[0, 0, y]
    xdata1 = geometry[0, 1, y]
    xdata2 = geometry[0, 2, y]
    xdata3 = geometry[0, 3, y]
    angles = geometry[0, 4, y]

    for x in range(cols):

        if scoresdata[x] < 0.5:  # if score is less than min_confidence, ignore
            continue
        # print(scoresdata[x])
        offsetx = x * 4.0
        offsety = y * 4.0
        # EAST detector automatically reduces volume size as it passes through the network
        # extracting the rotation angle for the prediction and computing their sine and cos

        angle = angles[x]
        cos = np.cos(angle)
        sin = np.sin(angle)

        h = xdata0[x] + xdata2[x]
        w = xdata1[x] + xdata3[x]
        #  print(offsetx,offsety,xdata1[x],xdata2[x],cos)
        endx = int(offsetx + (cos * xdata1[x]) + (sin * xdata2[x]))
        endy = int(offsety + (sin * xdata1[x]) + (cos * xdata2[x]))
        startx = int(endx - w)
        starty = int(endy - h)

        # appending the confidence score and probabilities to list
        rects.append((startx, starty, endx, endy))
        confidences.append(scoresdata[x])


# applying non-maxima suppression to supppress weak and overlapping bounding boxes
boxes = non_max_suppression(np.array(rects), probs=confidences)

iti=[]
rW = width1 / float(width2)
rH = height1 / float(height2)


bb = []
for (startx, starty, endx, endy) in boxes:
    startx = int(startx * rW)
    starty = int(starty * rH)
    endx = int(endx * rW)
    endy = int(endy * rH)
    cv2.rectangle(image1, (startx, starty), (endx, endy), (255, 0,0), 2)
    
    bb.append([startx, starty, endx, endy])
    

#combining the bounding boxes to get the text region

csx = 0
cex = 0
csy = 0
cey = 0
for i,box in enumerate(bb[::-1]):
    if i==0:
        csx = box[0]
    else:
        cex = box[2]
        cey = box[3]+5
        esx = box[1]
    
#print(image1)
cv2.imshow("BB img",image1)
cv2.waitKey(0)

rects.append((startx, starty, endx, endy))
confidences.append(scoresdata[x])
it=ima_org[csy:cey, csx:cex]
cv2.imshow("Cropped Text Region",it)
cv2.waitKey(0)

pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
thr = cv2.threshold(src=cv2.cvtColor(it,cv2.COLOR_RGB2GRAY), thresh=0, maxval=255, type=cv2.THRESH_OTSU + cv2.THRESH_BINARY_INV)[1]
txt = pytesseract.image_to_string(thr,lang='eng',config='--psm 11')
print(txt.strip())

这是脚本的输出
文本区域的边界框

裁剪文本区域

提取的文本
\

如 post 中所述，您的所有图像都很相似，您可以通过重新利用此脚本从中提取文本。脚本相当快，这张图片只需要 2.1 秒。

OpenCV tesserocr 水印检测

OpenCV tesserocr watermark detection

python

opencv

image-processing

tesseract

python-tesseract