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2023-07-31 13:49| 来源: 网络整理| 查看: 265

OCR识别

文章目录 OCR识别项目概述项目实现①边缘检测代码效果 ②轮廓检测代码效果 ③透视变换代码效果 ④识别文字代码效果项目提醒

项目概述

目标：对购物小票进行扫描并识别其上的文字

原图：在这里插入图片描述

步骤：

①首先，因为小票拍摄角度任意，所以需要通过边缘检测，轮廓检测以及透视变换，将小票摆放成一个规整，平铺的位置，将图片周围的无用部分去除

②其次，对小票上的文字进行识别，并将扫描后的结果存储到txt文件中

项目实现

先实现第一步，将小票平铺

①边缘检测代码 # 导入工具包 import numpy as np import argparse import cv2 def order_points(pts): # 一共4个坐标点 rect = np.zeros((4, 2), dtype = "float32") # 按顺序找到对应坐标0123分别是左上，右上，右下，左下 # 计算左上，右下 s = pts.sum(axis = 1) rect[0] = pts[np.argmin(s)] rect[2] = pts[np.argmax(s)] # 计算右上和左下 diff = np.diff(pts, axis = 1) rect[1] = pts[np.argmin(diff)] rect[3] = pts[np.argmax(diff)] return rect def four_point_transform(image, pts): # 获取输入坐标点 rect = order_points(pts) (tl, tr, br, bl) = rect # 计算输入的w和h值 widthA = np.sqrt(((br[0] - bl[0]) ** 2) + ((br[1] - bl[1]) ** 2)) widthB = np.sqrt(((tr[0] - tl[0]) ** 2) + ((tr[1] - tl[1]) ** 2)) maxWidth = max(int(widthA), int(widthB)) heightA = np.sqrt(((tr[0] - br[0]) ** 2) + ((tr[1] - br[1]) ** 2)) heightB = np.sqrt(((tl[0] - bl[0]) ** 2) + ((tl[1] - bl[1]) ** 2)) maxHeight = max(int(heightA), int(heightB)) # 变换后对应坐标位置 dst = np.array([ [0, 0], [maxWidth - 1, 0], [maxWidth - 1, maxHeight - 1], [0, maxHeight - 1]], dtype = "float32") # 计算变换矩阵 M = cv2.getPerspectiveTransform(rect, dst) warped = cv2.warpPerspective(image, M, (maxWidth, maxHeight)) # 返回变换后结果 return warped def resize(image, width=None, height=None, inter=cv2.INTER_AREA): dim = None (h, w) = image.shape[:2] if width is None and height is None: return image if width is None: r = height / float(h) dim = (int(w * r), height) else: r = width / float(w) dim = (width, int(h * r)) resized = cv2.resize(image, dim, interpolation=inter) return resized # 读取输入 # image = cv2.imread(args["image"]) image = cv2.imread('./images/receipt.jpg') #坐标也会相同变化 ratio = image.shape[0] / 500.0 orig = image.copy() image = resize(orig, height = 500) # 预处理 gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) gray = cv2.GaussianBlur(gray, (5, 5), 0) edged = cv2.Canny(gray, 75, 200) # 展示预处理结果 print("STEP 1: 边缘检测") cv2.imshow("Image", image) cv2.imshow("Edged", edged) cv2.waitKey(0) cv2.destroyAllWindows() 效果

在这里插入图片描述

②轮廓检测代码 # 轮廓检测 cnts = cv2.findContours(edged.copy(), cv2.RETR_LIST, cv2.CHAIN_APPROX_SIMPLE)[0] cnts = sorted(cnts, key = cv2.contourArea, reverse = True)[:5] # 遍历轮廓 for c in cnts: # 计算轮廓近似 peri = cv2.arcLength(c, True) # C表示输入的点集 # epsilon表示从原始轮廓到近似轮廓的最大距离，它是一个准确度参数 # True表示封闭的 approx = cv2.approxPolyDP(c, 0.02 * peri, True) # 4个点的时候就拿出来 if len(approx) == 4: screenCnt = approx break # 展示结果 print("STEP 2: 获取轮廓") cv2.drawContours(image, [screenCnt], -1, (0, 255, 0), 2) cv2.imshow("Outline", image) cv2.waitKey(0) cv2.destroyAllWindows() 效果

在这里插入图片描述

③透视变换代码 # 透视变换 warped = four_point_transform(orig, screenCnt.reshape(4, 2) * ratio) # 二值处理 warped = cv2.cvtColor(warped, cv2.COLOR_BGR2GRAY) ref = cv2.threshold(warped, 100, 255, cv2.THRESH_BINARY)[1] cv2.imwrite('scan.jpg', ref) # 展示结果 print("STEP 3: 变换") cv2.imshow("Original", resize(orig, height = 650)) cv2.imshow("Scanned", resize(ref, height = 650)) cv2.waitKey(0) 效果

在这里插入图片描述

实现第二步，识别文字

④识别文字代码 from PIL import Image import pytesseract import cv2 from cv2 import cv2 as cv import os preprocess = 'blur' #thresh image = cv2.imread('scan.jpg') gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) if preprocess == "thresh": gray = cv2.threshold(gray, 0, 255,cv2.THRESH_BINARY | cv2.THRESH_OTSU)[1] if preprocess == "blur": gray = cv2.medianBlur(gray, 3) filename = "{}.png".format(os.getpid()) cv2.imwrite(filename, gray) text = pytesseract.image_to_string(Image.open(filename)) print(text) os.remove(filename) with open("result.txt","w+") as f: f.write(text) cv2.imshow("Image", image) cv2.imshow("Output", gray) cv2.waitKey(0) 效果

在这里插入图片描述

项目提醒

需配置tesseract

tesseract -v进行测试

tesseract XXX.png 得到结果

pip install pytesseract

anaconda lib site-packges pytesseract pytesseract.py

tesseract_cmd 一定修改为绝对路径

【本文地址】

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