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我正在尝试识别车牌，但出现了错误，例如错误/未读取字符
以下是每个步骤的可视化：
从颜色阈值+变形关闭获得遮罩
以绿色突出显示的车牌轮廓过滤器
将板轮廓粘贴到空白遮罩上
Tesseract OCR的预期结果
BP 1309 GD
但我得到的结果是
BP 1309 6D
我试着把轮廓切成3片
是的，它是有效的，但如果我在这个方法中插入差异图像，一些图像就无法识别，比如这个
字母N不可识别，但如果使用第一种方法，它会起作用
这是程序
import numpy as np
import pytesseract
import cv2
import ospytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"
image_path = "data"for nama_file in sorted(os.listdir(image_path)):print(nama_file)# Load image, create blank mask, convert to HSV, define thresholds, color thresholdI = cv2.imread(os.path.join(image_path, nama_file))dim = (500, 120)I = cv2.resize(I, dim, interpolation = cv2.INTER_AREA)(thresh, image) = cv2.threshold(I, 127, 255, cv2.THRESH_BINARY)result = np.zeros(image.shape, dtype=np.uint8)result = 255 - resulthsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)lower = np.array([0,0,0])upper = np.array([179,100,130])mask = cv2.inRange(hsv, lower, upper)slices = []slices.append(result.copy())slices.append(result.copy())slices.append(result.copy())i = 0j = 0xs = []# Perform morph close and merge for 3-channel ROI extractionkernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,3))close = cv2.morphologyEx(mask, cv2.MORPH_CLOSE, kernel, iterations=1)extract = cv2.merge([close,close,close])# Find contours, filter using contour area, and extract using Numpy slicingcnts = cv2.findContours(close, cv2.RETR_TREE, cv2.CHAIN_APPROX_SIMPLE)cnts = cnts[0] if len(cnts) == 2 else cnts[1]boundingBoxes = [cv2.boundingRect(c) for c in cnts](cnts, boundingBoxes) = zip(*sorted(zip(cnts, boundingBoxes),key=lambda b:b[1][0], reverse=False))for c in cnts:x,y,w,h = cv2.boundingRect(c)area = w * hras = format(w / h, '.2f')if h >= 40 and h <= 70 and w >= 10 and w <= 65 and float(ras) <= 1.3:cv2.rectangle(I, (x, y), (x + w, y + h), (36,255,12), 3)result[y:y+h, x:x+w] = extract[y:y+h, x:x+w]# Slicexs.append(x)if i > 0:if (xs[i] - xs[i-1]) > 63:j = j+1i = i + 1slices[j][y:y+h, x:x+w] = extract[y:y+h, x:x+w]# Split throw into Pytesseractj=0for s in slices:cv2.imshow('result', s)cv2.waitKey()if j != 1 :data = pytesseract.image_to_string(s, lang='eng',config='--psm 6 _char_whitelist=ABCDEFGHIJKLMNOPQRTUVWXYZ')else :data = pytesseract.image_to_string(s, lang='eng',config='--psm 6 _char_whitelist=1234567890')print(data)# Block throw into Pytesseractdata = pytesseract.image_to_string(result, lang='eng',config='--psm 6')print(data)cv2.imshow('image', I)cv2.imshow('close', close)cv2.imshow('extract', extract)cv2.imshow('result', result)cv2.waitKey()
我尝试了很多方法，找到了一些解决方案：
应用扩张形态学操作使字母变薄：
# Split throw into Pytesseract
j=0
for s in slices:cv2.imshow('result', s)cv2.waitKey(1)if j != 1:data = pytesseract.image_to_string(s, config="-c tessedit""_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890"" psm 6"" ")if data=='': s = cv2.dilate(s, cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (5,5)))cv2.imshow('cv2.dilate(s)', s)cv2.waitKey(1)data = pytesseract.image_to_string(s, config="-c tessedit""_char_whitelist=ABCDEFGHIJKLMNOPQRSTUVWXYZ1234567890"" psm 6"" ")else:pytesseract.pytesseract.tessedit_char_whitelist = '1234567890'data = pytesseract.image_to_string(s, lang='eng',config=' psm 6 _char_whitelist=1234567890')print(data)
这种行为很奇怪。
有很多投诉，建议的解决方案不起作用
至少我学会了如何使用_char_whitelist选项（您需要添加-c tessedit）
我认为该解决方案不够健壮（可能是偶然工作）。
我认为在当前版本的Tesseract中没有简单的解决方案