使用opencv获取图像中存在的所有文本的位置 [英] Get the location of all text present in image using opencv

问题描述

我有这张图像，其中包含文本(数字和字母).我想获取此图像中存在的所有文本和数字的位置.我也想提取所有文本.

I have this image that contains text(numbers and alphabets) in it. I want to get the location of all the text and numbers present in this image. Also I want to extract all the text as well.

如何获取坐标以及图像中的所有文本(数字和字母).例如10B，44、16、38、22B等

How do I get the cordinates as well as the all the text(numbers and alphabets) in my image. For eg 10B, 44, 16, 38, 22B etc

推荐答案

以下是使用

Here's a potential approach using morphological operations to filter out non-text contours. The idea is:

1. 获取二进制图像.，先加载灰度图像，然后 Otsu的阈值

1. Obtain binary image. Load image, grayscale, then Otsu's threshold

删除水平和垂直线.使用 删除对角线，圆形对象和弯曲轮廓.使用轮廓区域 cv2.approxPolyDP 隔离非文本轮廓

Remove diagonal lines, circle objects, and curved contours. Filter using contour area cv2.contourArea and contour approximation cv2.approxPolyDP to isolate non-text contours

提取文本ROI和OCR.查找轮廓并过滤ROI，然后使用OCR Pytesseract .

Extract text ROIs and OCR. Find contours and filter for ROIs then OCR using Pytesseract.

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删除了以绿色突出显示的水平线

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Removed horizontal lines highlighted in green

删除垂直线

删除了各种非文本轮廓(对角线，圆形对象和曲线)

Removed assorted non-text contours (diagonal lines, circular objects, and curves)

检测到的文本区域

import cv2
import numpy as np
import pytesseract

pytesseract.pytesseract.tesseract_cmd = r"C:\Program Files\Tesseract-OCR\tesseract.exe"

# Load image, grayscale, Otsu's threshold
image = cv2.imread('1.jpg')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 255, cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]
clean = thresh.copy()

# Remove horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (15,1))
detect_horizontal = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, horizontal_kernel, iterations=2)
cnts = cv2.findContours(detect_horizontal, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(clean, [c], -1, 0, 3)

# Remove vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,30))
detect_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=2)
cnts = cv2.findContours(detect_vertical, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    cv2.drawContours(clean, [c], -1, 0, 3)

cnts = cv2.findContours(clean, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    # Remove diagonal lines
    area = cv2.contourArea(c)
    if area < 100:
        cv2.drawContours(clean, [c], -1, 0, 3)
    # Remove circle objects
    elif area > 1000:
        cv2.drawContours(clean, [c], -1, 0, -1)
    # Remove curve stuff
    peri = cv2.arcLength(c, True)
    approx = cv2.approxPolyDP(c, 0.02 * peri, True)
    x,y,w,h = cv2.boundingRect(c)
    if len(approx) == 4:
        cv2.rectangle(clean, (x, y), (x + w, y + h), 0, -1)

open_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (2,2))
opening = cv2.morphologyEx(clean, cv2.MORPH_OPEN, open_kernel, iterations=2)
close_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (3,2))
close = cv2.morphologyEx(opening, cv2.MORPH_CLOSE, close_kernel, iterations=4)
cnts = cv2.findContours(close, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    x,y,w,h = cv2.boundingRect(c)
    area = cv2.contourArea(c)
    if area > 500:
        ROI = image[y:y+h, x:x+w]
        ROI = cv2.GaussianBlur(ROI, (3,3), 0)
        data = pytesseract.image_to_string(ROI, lang='eng',config='--psm 6')
        if data.isalnum():
            cv2.rectangle(image, (x, y), (x + w, y + h), (36,255,12), 2)
            print(data)

cv2.imwrite('image.png', image)
cv2.imwrite('clean.png', clean)
cv2.imwrite('close.png', close)
cv2.imwrite('opening.png', opening)
cv2.waitKey()

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