我想使用 OCR (pytesseract) 來識別圖像中的文本，如下所示：我有數千支這樣的箭頭。到目前為止，過程如下：我首先調整圖像大?。ㄓ糜诹硪粋€過程）。然后我裁剪圖像以去除箭頭的大部分。接下來，我繪制一個白色矩形作為框架以消除更多噪音，但文本和圖像邊框之間仍然有距離，以便更好地識別文本。我再次調整圖像大小，以確保大寫字母的高度達到約 30 像素，最后我用閾值 150 對圖像進行二值化。完整代碼：import cv2image_file = '001.jpg'# load the input image and grab the image dimensionsimage = cv2.imread(image_file, cv2.IMREAD_GRAYSCALE)(h_1, w_1) = image.shape[:2]# resize the image and grab the new image dimensionsimage = cv2.resize(image, (int(w_1*320/h_1), 320))(h_1, w_1) = image.shape# crop imageimage_2 = image[70:h_1-70, 20:w_1-20]# get image_2 height, width(h_2, w_2) = image_2.shape# draw white rectangle as a frame around the number -> remove noisecv2.rectangle(image_2, (0, 0), (w_2, h_2), (255, 255, 255), 40)# resize image, that capital letters are ~ 30 px in heightimage_2 = cv2.resize(image_2, (int(w_2*50/h_2), 50))# image binarizationret, image_2 = cv2.threshold(image_2, 150, 255, cv2.THRESH_BINARY)# save image to filecv2.imwrite('processed_' + image_file, image_2)# tesseract part can be commented outimport pytesseractconfig_7 = ("-c tessedit_char_whitelist=0123456789AB --oem 1 --psm 7")text = pytesseract.image_to_string(image_2, config=config_7)print("OCR TEXT: " + "{}\n".format(text))問題在于箭頭中的文本永遠不會居中。有時我會使用上述方法刪除部分文本（例如在圖像 50A 中）。圖像處理中是否有一種方法可以以更優雅的方式消除箭頭？例如使用輪廓檢測和刪除？我對 OpenCV 部分比識別文本的 tesseract 部分更感興趣。任何幫助表示贊賞。
2 回答
慕尼黑的夜晚無繁華
TA貢獻1864條經驗 獲得超6個贊
如果您查看圖片，您會發現圖像中有一個白色箭頭，這也是最大的輪廓（特別是如果您在圖像上繪制黑色邊框）。如果您制作一個空白蒙版并繪制箭頭（圖像上最大的輪廓），然后稍微腐蝕它，您可以對實際圖像和腐蝕蒙版執行每個元素的按位連接。如果不清楚，請查看底部代碼和注釋，您會發現它實際上非常簡單。
# imports
import cv2
import numpy as np
img = cv2.imread("number.png") # read image
# you can resize the image here if you like - it should still work for both sizes
h, w = img.shape[:2] # get the actual images height and width
img = cv2.resize(img, (int(w*320/h), 320))
h, w = img.shape[:2]
gray = cv2.cvtColor(img, cv2.COLOR_BGR2GRAY) # transform to grayscale
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY+cv2.THRESH_OTSU)[1] # perform OTSU threhold
cv2.rectangle(thresh, (0, 0), (w, h), (0, 0, 0), 2)
contours = cv2.findContours(thresh, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)[0] # search for contours
max_cnt = max(contours, key=cv2.contourArea) # select biggest one
mask = np.zeros((h, w), dtype=np.uint8) # create a black mask
cv2.drawContours(mask, [max_cnt], -1, (255, 255, 255), -1) # draw biggest contour on the mask
kernel = np.ones((15, 15), dtype=np.uint8) # make a kernel with appropriate values - in both cases (resized and original) 15 is ok
erosion = cv2.erode(mask, kernel, iterations=1) # erode the mask with given kernel
reverse = cv2.bitwise_not(img.copy()) # reversed image of the actual image 0 becomes 255 and 255 becomes 0
img = cv2.bitwise_and(reverse, reverse, mask=erosion) # per-element bit-wise conjunction of the actual image and eroded mask (erosion)
img = cv2.bitwise_not(img) # revers the image again
# save image to file and display
cv2.imwrite("res.png", img)
cv2.imshow("img", img)
cv2.waitKey(0)
cv2.destroyAllWindows()
結果：
慕尼黑5688855
TA貢獻1848條經驗 獲得超2個贊
你可以嘗試簡單的Python腳本：
import cv2
import numpy as np
img = cv2.imread('mmubS.png', cv2.IMREAD_GRAYSCALE)
thresh = cv2.threshold(img, 200, 255, cv2.THRESH_BINARY_INV )[1]
im_flood_fill = thresh.copy()
h, w = thresh.shape[:2]
im_flood_fill=cv2.rectangle(im_flood_fill, (0,0), (w-1,h-1), 255, 2)
mask = np.zeros((h + 2, w + 2), np.uint8)
cv2.floodFill(im_flood_fill, mask, (0, 0), 0)
im_flood_fill = cv2.bitwise_not(im_flood_fill)
cv2.imshow('clear text', im_flood_fill)
cv2.imwrite('text.png', im_flood_fill)
結果：
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