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如何使用GCP Vision或OpenCV提取图像中的自动文本和手写文本

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我已经编写了一段代码，其中自动文本和手写文本一起出现在图像中。 GCP视觉API无法给我正确的响应。有人可以建议我解决吗？

这是我的图片

如何使用GCP Vision或OpenCV提取图像中的自动文本和手写文本

这是我的代码：

import os
import io
from google.cloud import vision
from google.cloud.vision import types
import pandas as pd

os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = r'key.json'
client = vision.ImageAnnotatorClient()

IMAGE_FILE = 'datasetone.png'
FILE_PATH = os.path.join(IMAGE_FILE)

with io.open(FILE_PATH,'rb') as image_file:
    content = image_file.read()

image = vision.types.Image(content=content)
response = client.document_text_detection(image=image)

docText = response.full_text_annotation.text
print(docText)


pages = response.full_text_annotation.pages
for page in pages:
    for block in page.blocks:
        print('block confidence:',block.confidence)

        for paragraph in block.paragraphs:
            print('paragraph confidence:',paragraph.confidence)

            for word in paragraph.words:
                word_text = ''.join([symbol.text for symbol in word.symbols])

                print('Word text: {0} (confidence: {1}'.format(
                    word_text,word.confidence))

                for symbol in word.symbols:
                    print('\tSymbol: {0} (confidence: {1}'.format(
                        symbol.text,symbol.confidence))

输出是

Worksheet
Car number: - 1002110021
Data:- Endter
Shell
Qilchange
Phone: 982838238823
email:xyz@xyz.com
name
pod
pos
Technician
name
desc
code
112121223 Benz
10-10-19
10-10-20
Jhondoe
nil
12244334
Baleno
20-10-10
| 20-20-10
Machel
nil
D901.27
'RIOR

block confidence: 0.9800000190734863
paragraph confidence: 0.9800000190734863
Word text: Worksheet (confidence: 0.9900000095367432
        Symbol: W (confidence: 0.9900000095367432
        Symbol: o (confidence: 0.9900000095367432
        Symbol: r (confidence: 1.0
        Symbol: k (confidence: 0.9900000095367432
        Symbol: s (confidence: 1.0
        Symbol: h (confidence: 1.0
        Symbol: e (confidence: 0.9900000095367432
        Symbol: e (confidence: 1.0
        Symbol: t (confidence: 1.0
Word text: Car (confidence: 0.9800000190734863
        Symbol: C (confidence: 0.9800000190734863
        Symbol: a (confidence: 0.9900000095367432
        Symbol: r (confidence: 0.9900000095367432
Word text: number (confidence: 0.9800000190734863
        Symbol: n (confidence: 0.9599999785423279
        Symbol: u (confidence: 0.9800000190734863
        Symbol: m (confidence: 0.9800000190734863
        Symbol: b (confidence: 0.9800000190734863
        Symbol: e (confidence: 1.0
        Symbol: r (confidence: 1.0
Word text: : (confidence: 0.9900000095367432
        Symbol: : (confidence: 0.9900000095367432
Word text: - (confidence: 0.949999988079071
        Symbol: - (confidence: 0.949999988079071
Word text: 1002110021 (confidence: 0.9700000286102295
        Symbol: 1 (confidence: 0.9599999785423279
        Symbol: 0 (confidence: 0.9800000190734863
        Symbol: 0 (confidence: 0.9800000190734863
        Symbol: 2 (confidence: 0.9399999976158142
        Symbol: 1 (confidence: 0.9599999785423279
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 0.9599999785423279
        Symbol: 0 (confidence: 0.9900000095367432
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
Word text: Data (confidence: 0.9900000095367432
        Symbol: D (confidence: 0.9900000095367432
        Symbol: a (confidence: 0.9900000095367432
        Symbol: t (confidence: 0.9900000095367432
        Symbol: a (confidence: 1.0
Word text: : (confidence: 0.9900000095367432
        Symbol: : (confidence: 0.9900000095367432
Word text: - (confidence: 0.9900000095367432
        Symbol: - (confidence: 0.9900000095367432
Word text: Endter (confidence: 0.9900000095367432
        Symbol: E (confidence: 0.9900000095367432
        Symbol: n (confidence: 1.0
        Symbol: d (confidence: 0.9900000095367432
        Symbol: t (confidence: 0.9900000095367432
        Symbol: e (confidence: 1.0
        Symbol: r (confidence: 1.0
block confidence: 0.9700000286102295
paragraph confidence: 0.9700000286102295
Word text: Shell (confidence: 0.9900000095367432
        Symbol: S (confidence: 0.9900000095367432
        Symbol: h (confidence: 0.9900000095367432
        Symbol: e (confidence: 0.9900000095367432
        Symbol: l (confidence: 1.0
        Symbol: l (confidence: 1.0
Word text: Qilchange (confidence: 0.8999999761581421
        Symbol: Q (confidence: 0.6100000143051147
        Symbol: i (confidence: 0.9100000262260437
        Symbol: l (confidence: 0.9100000262260437
        Symbol: c (confidence: 0.8500000238418579
        Symbol: h (confidence: 0.9200000166893005
        Symbol: a (confidence: 0.9900000095367432
        Symbol: n (confidence: 0.9900000095367432
        Symbol: g (confidence: 1.0
        Symbol: e (confidence: 1.0
Word text: Phone (confidence: 0.9900000095367432
        Symbol: P (confidence: 0.9900000095367432
        Symbol: h (confidence: 0.9900000095367432
        Symbol: o (confidence: 1.0
        Symbol: n (confidence: 1.0
        Symbol: e (confidence: 1.0
Word text: : (confidence: 1.0
        Symbol: : (confidence: 1.0
Word text: 982838238823 (confidence: 0.9800000190734863
        Symbol: 9 (confidence: 0.9599999785423279
        Symbol: 8 (confidence: 0.9900000095367432
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 8 (confidence: 0.9900000095367432
        Symbol: 3 (confidence: 0.9900000095367432
        Symbol: 8 (confidence: 0.9900000095367432
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 3 (confidence: 0.9900000095367432
        Symbol: 8 (confidence: 0.9900000095367432
        Symbol: 8 (confidence: 0.9900000095367432
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 3 (confidence: 0.9900000095367432
Word text: email (confidence: 0.9900000095367432
        Symbol: e (confidence: 0.9900000095367432
        Symbol: m (confidence: 0.9900000095367432
        Symbol: a (confidence: 0.9900000095367432
        Symbol: i (confidence: 1.0
        Symbol: l (confidence: 0.9900000095367432
Word text: : (confidence: 0.9900000095367432
        Symbol: : (confidence: 0.9900000095367432
Word text: xyz (confidence: 0.9900000095367432
        Symbol: x (confidence: 0.9900000095367432
        Symbol: y (confidence: 0.9900000095367432
        Symbol: z (confidence: 0.9900000095367432
Word text: @ (confidence: 0.9900000095367432
        Symbol: @ (confidence: 0.9900000095367432
Word text: xyz (confidence: 0.9700000286102295
        Symbol: x (confidence: 0.949999988079071
        Symbol: y (confidence: 0.9900000095367432
        Symbol: z (confidence: 0.9900000095367432
Word text: . (confidence: 0.9900000095367432
        Symbol: . (confidence: 0.9900000095367432
Word text: com (confidence: 0.9900000095367432
        Symbol: c (confidence: 0.9900000095367432
        Symbol: o (confidence: 1.0
        Symbol: m (confidence: 1.0
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: name (confidence: 0.9900000095367432
        Symbol: n (confidence: 0.9900000095367432
        Symbol: a (confidence: 1.0
        Symbol: m (confidence: 0.9900000095367432
        Symbol: e (confidence: 1.0
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: pod (confidence: 0.9900000095367432
        Symbol: p (confidence: 0.9900000095367432
        Symbol: o (confidence: 1.0
        Symbol: d (confidence: 0.9900000095367432
block confidence: 0.5699999928474426
paragraph confidence: 0.5699999928474426
Word text: pos (confidence: 0.5699999928474426
        Symbol: p (confidence: 0.20000000298023224
        Symbol: o (confidence: 0.9900000095367432
        Symbol: s (confidence: 0.5400000214576721
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: Technician (confidence: 0.9900000095367432
        Symbol: T (confidence: 0.9900000095367432
        Symbol: e (confidence: 1.0
        Symbol: c (confidence: 0.9900000095367432
        Symbol: h (confidence: 1.0
        Symbol: n (confidence: 1.0
        Symbol: i (confidence: 1.0
        Symbol: c (confidence: 1.0
        Symbol: i (confidence: 0.9900000095367432
        Symbol: a (confidence: 1.0
        Symbol: n (confidence: 1.0
Word text: name (confidence: 0.9900000095367432
        Symbol: n (confidence: 0.9900000095367432
        Symbol: a (confidence: 1.0
        Symbol: m (confidence: 0.9900000095367432
        Symbol: e (confidence: 1.0
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: desc (confidence: 0.9900000095367432
        Symbol: d (confidence: 0.9900000095367432
        Symbol: e (confidence: 1.0
        Symbol: s (confidence: 1.0
        Symbol: c (confidence: 1.0
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: code (confidence: 0.9900000095367432
        Symbol: c (confidence: 0.9900000095367432
        Symbol: o (confidence: 1.0
        Symbol: d (confidence: 0.9900000095367432
        Symbol: e (confidence: 1.0
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: 112121223 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 1.0
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 2 (confidence: 1.0
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 3 (confidence: 0.9900000095367432
Word text: Benz (confidence: 0.9900000095367432
        Symbol: B (confidence: 0.9900000095367432
        Symbol: e (confidence: 0.9900000095367432
        Symbol: n (confidence: 0.9900000095367432
        Symbol: z (confidence: 0.9900000095367432
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: 10 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 0.9900000095367432
Word text: - (confidence: 0.9900000095367432
        Symbol: - (confidence: 0.9900000095367432
Word text: 10 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 1.0
        Symbol: 0 (confidence: 0.9900000095367432
Word text: - (confidence: 0.9900000095367432
        Symbol: - (confidence: 0.9900000095367432
Word text: 19 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 9 (confidence: 0.9900000095367432
block confidence: 0.9800000190734863
paragraph confidence: 0.9800000190734863
Word text: 10 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 0.9900000095367432
Word text: - (confidence: 0.9900000095367432
        Symbol: - (confidence: 0.9900000095367432
Word text: 10 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 0.9900000095367432
Word text: - (confidence: 0.9900000095367432
        Symbol: - (confidence: 0.9900000095367432
Word text: 20 (confidence: 0.9800000190734863
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 0.9800000190734863
block confidence: 0.9599999785423279
paragraph confidence: 0.9599999785423279
Word text: Jhondoe (confidence: 0.9599999785423279
        Symbol: J (confidence: 0.8100000023841858
        Symbol: h (confidence: 0.9900000095367432
        Symbol: o (confidence: 1.0
        Symbol: n (confidence: 0.9900000095367432
        Symbol: d (confidence: 0.9900000095367432
        Symbol: o (confidence: 0.9900000095367432
        Symbol: e (confidence: 0.9800000190734863
block confidence: 0.9700000286102295
paragraph confidence: 0.9700000286102295
Word text: nil (confidence: 0.9700000286102295
        Symbol: n (confidence: 0.9900000095367432
        Symbol: i (confidence: 0.9900000095367432
        Symbol: l (confidence: 0.9399999976158142
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: 12244334 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 2 (confidence: 1.0
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 4 (confidence: 0.9900000095367432
        Symbol: 4 (confidence: 0.9900000095367432
        Symbol: 3 (confidence: 0.9900000095367432
        Symbol: 3 (confidence: 0.9900000095367432
        Symbol: 4 (confidence: 0.9900000095367432
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: Baleno (confidence: 0.9900000095367432
        Symbol: B (confidence: 0.9900000095367432
        Symbol: a (confidence: 1.0
        Symbol: l (confidence: 0.9900000095367432
        Symbol: e (confidence: 0.9900000095367432
        Symbol: n (confidence: 0.9900000095367432
        Symbol: o (confidence: 0.9900000095367432
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: 20 (confidence: 0.9900000095367432
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 0.9900000095367432
Word text: - (confidence: 0.9900000095367432
        Symbol: - (confidence: 0.9900000095367432
Word text: 10 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 0.9900000095367432
Word text: - (confidence: 0.9900000095367432
        Symbol: - (confidence: 0.9900000095367432
Word text: 10 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 0.9900000095367432
block confidence: 0.8899999856948853
paragraph confidence: 0.8899999856948853
Word text: | (confidence: 0.4300000071525574
        Symbol: | (confidence: 0.4300000071525574
Word text: 20 (confidence: 0.8399999737739563
        Symbol: 2 (confidence: 0.699999988079071
        Symbol: 0 (confidence: 0.9800000190734863
Word text: - (confidence: 0.949999988079071
        Symbol: - (confidence: 0.949999988079071
Word text: 20 (confidence: 0.9800000190734863
        Symbol: 2 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 0.9800000190734863
Word text: - (confidence: 1.0
        Symbol: - (confidence: 1.0
Word text: 10 (confidence: 0.9900000095367432
        Symbol: 1 (confidence: 0.9900000095367432
        Symbol: 0 (confidence: 1.0
block confidence: 0.9300000071525574
paragraph confidence: 0.9300000071525574
Word text: Machel (confidence: 0.9300000071525574
        Symbol: M (confidence: 0.9200000166893005
        Symbol: a (confidence: 0.949999988079071
        Symbol: c (confidence: 0.9200000166893005
        Symbol: h (confidence: 0.9599999785423279
        Symbol: e (confidence: 0.949999988079071
        Symbol: l (confidence: 0.9200000166893005
block confidence: 0.9900000095367432
paragraph confidence: 0.9900000095367432
Word text: nil (confidence: 0.9900000095367432
        Symbol: n (confidence: 0.9900000095367432
        Symbol: i (confidence: 1.0
        Symbol: l (confidence: 0.9900000095367432
block confidence: 0.7599999904632568
paragraph confidence: 0.7599999904632568
Word text: D901 (confidence: 0.7200000286102295
        Symbol: D (confidence: 0.6499999761581421
        Symbol: 9 (confidence: 0.8700000047683716
        Symbol: 0 (confidence: 0.49000000953674316
        Symbol: 1 (confidence: 0.8999999761581421
Word text: . (confidence: 0.6399999856948853
        Symbol: . (confidence: 0.6399999856948853
Word text: 27 (confidence: 0.8600000143051147
        Symbol: 2 (confidence: 0.9399999976158142
        Symbol: 7 (confidence: 0.7799999713897705
Word text: ' (confidence: 0.9300000071525574
        Symbol: ' (confidence: 0.9300000071525574
Word text: RIOR (confidence: 0.7400000095367432
        Symbol: R (confidence: 0.9599999785423279
        Symbol: I (confidence: 0.7799999713897705
        Symbol: O (confidence: 0.8399999737739563
        Symbol: R (confidence: 0.4000000059604645

存在代码的数据集中的最后一列是手写的，因此当出现手写和自动文本时，谁能帮助我识别文本？

如果不是Google Cloud Vision，还有什么方法可以找到并打印opencv中每一行的数据？

TOTTI128 回答：如何使用GCP Vision或OpenCV提取图像中的自动文本和手写文本

您可以尝试去除水平和垂直线来对图像进行预处理，这可能会带来更好的输出效果

方法1：水平/垂直内核，并在轮廓中“填充”以去除线条

enter image description here 

import cv2

image = cv2.imread('1.png')
gray = cv2.cvtColor(image,cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray,255,cv2.THRESH_BINARY_INV + cv2.THRESH_OTSU)[1]

# Remove horizontal lines
horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(30,1))
detect_horizontal = cv2.morphologyEx(thresh,cv2.MORPH_OPEN,horizontal_kernel,iterations=1)
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(thresh,[c],-1,(0,0),1)

# Remove vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(1,30))
detect_vertical = cv2.morphologyEx(thresh,vertical_kernel,iterations=1)
cnts = cv2.findContours(detect_vertical,1)

# Remove small noise
cnts = cv2.findContours(thresh,cv2.CHAIN_APPROX_SIMPLE)
cnts = cnts[0] if len(cnts) == 2 else cnts[1]
for c in cnts:
    area = cv2.contourArea(c)
    if area < 10:
        cv2.drawContours(thresh,-1)

# Bitwise mask with input image
result = cv2.bitwise_and(image,image,mask=thresh)
result[thresh==0] = (255,255)

cv2.imshow('result',result)
cv2.imwrite('result.png',result)
cv2.waitKey()

方法2：具有屏蔽和按位运算的水平/垂直内核

enter image description here 

import cv2

image = cv2.imread('1.png')

kernel_vertical = cv2.getStructuringElement(cv2.MORPH_RECT,50))
remove_vertical = 255 - cv2.morphologyEx(image,cv2.MORPH_CLOSE,kernel_vertical)

horizontal_kernel = cv2.getStructuringElement(cv2.MORPH_RECT,(50,1))
remove_horizontal = 255 - cv2.morphologyEx(image,horizontal_kernel)

remove_both = cv2.add(remove_vertical,remove_horizontal)
result = cv2.add(remove_both,image)

cv2.imshow('result',result)
cv2.waitKey()

使用Pytesseract OCR的结果 

Worksheet
Car number - 1002110021
Data - Endter
Shell
Qilchange
Phone 982838238823
email xvz@xyz com
id name pod pos Technician desc code

name
112121223 Benz 10-10-19 10-10-20 Jhondoe nil

ve O \

12244334 Baleno 20-10-10 20-20-10 Machel nil \ \

它似乎无法捕获手写文本，您可能需要使用机器/深度学习来训练自己的分类器，但是它对于自动文本非常有效。我们使用--psm 6配置选项来假设一个统一的文本块。在here中查找更多配置选项

import cv2
import pytesseract

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

image = cv2.imread('result.png')
data = pytesseract.image_to_string(image,lang='eng',config='--psm 6')
print(data)
deep-learninggoogle-cloud-platformimage-processingopencv
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