python - 如何使用 GCP Vision 或 OpenCV 提取图像中的自动和手写文本
问题描述
我编写了一段代码，其中自动文本和书面文本出现在图像中。GCP 视觉 API 无法给我正确的响应。任何人都可以建议我解决任何问题吗？
这是我的形象
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
数据集中存在代码的最后一列是手写的，所以任何人都可以帮助我在存在手写和自动文本时如何识别文本？
如果不是谷歌云视觉，还有什么方法可以根据 opencv 中的每一行查找和打印数据？
解决方案
您可以尝试通过删除水平和垂直线来预处理图像，这可能会导致更好的输出
方法#1：水平/垂直内核和轮廓中的“填充”以删除线条
import cv2
image = cv2.imread('1.png')
gray = cv2.cvtColor(image, cv2.COLOR_BGR2GRAY)
thresh = cv2.threshold(gray, 0, 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, (0,0,0), 1)
# Remove vertical lines
vertical_kernel = cv2.getStructuringElement(cv2.MORPH_RECT, (1,30))
detect_vertical = cv2.morphologyEx(thresh, cv2.MORPH_OPEN, vertical_kernel, iterations=1)
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(thresh, [c], -1, (0,0,0), 1)
# Remove small noise
cnts = cv2.findContours(thresh, cv2.RETR_EXTERNAL, 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, [c], -1, (0,0,0), -1)
# Bitwise mask with input image
result = cv2.bitwise_and(image, image, mask=thresh)
result[thresh==0] = (255,255,255)
cv2.imshow('result', result)
cv2.imwrite('result.png', result)
cv2.waitKey()
方法 #2：具有掩码和按位运算的水平/垂直内核
import cv2
image = cv2.imread('1.png')
kernel_vertical = cv2.getStructuringElement(cv2.MORPH_RECT, (1,50))
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, cv2.MORPH_CLOSE, 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。在此处查看更多配置选项
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)
推荐阅读
- postgresql - 使用 Python 代码将带有偏移量的日期插入 PostgreSQL 数据库
- javascript - 使用 ajax 和 laravel 提交表单
- javascript - AngularJS 重定向控制器和历史回溯问题
- html - 面临使用 VBA 从网站中提取数据的问题
- python - 在同一个表的报告中添加更多 id
- javascript - 使用 Gauge / Taiko 通过 removeAttribute('readonly') 自动化日期选择器
- amazon-web-services - 尝试使用 awsmobile init 初始化项目
- ios - Xamarion.iOS - statusBar 如何改变文本颜色？
- azure-log-analytics - 在 Azure Log Analytics 的查询中解析 IIS 日志
- fortran - 为什么不引发浮点异常 (FPE)？