Translating pictures into words is generally called optical character recognition （Optical Character Recognition,OCR）. Can achieve OCR There are not many underlying Libraries , At present, many libraries use several common bottom layers OCR library , Or customize it .
easyocr Is based on torch Deep learning module
easyocr Occurs after the installation is invoked. opencv Version incompatibility problem , So give it up .
advantage ： The deployment of fast , Lightweight , Available offline , free
shortcoming ： The recognition rate of the built-in Chinese library is low , You need to build your own data for training
Tesseract It's a OCR library , At present by Google sponsorship （Google It is also a family with OCR And machine learning technology ）.Tesseract Is currently recognized as the best 、 The most accurate open source OCR System .
In addition to high accuracy ,Tesseract It also has high flexibility . It can recognize any font through training （ As long as the style of these fonts remains the same ）, You can also identify any Unicode character .
python Recognize the number on the picture , Use pytesseract Library extracts text from images , And identify The engine uses tesseract-ocr.
pytesseract yes python Wrappers , It provides for executable files pythonic API.
pip install pillow
pip install pytesseract
Download address of latest version ： https://github.com/UB-Mannheim/tesseract/wiki 
Or more tesseract Download address ：https://digi.bib.uni-mannheim.de/tesseract/
After the installation , need take Tesseract Add to system variable in .
environment variable ： My computer -> attribute -> Advanced system setup -> environment variable -> System variables , stay path Add The installation path .
And the trained model file chi_sim.traineddata Put it in this directory , This completes the installation .
In command line WIN+R Input cmd ： Input tesseract -v , Version information appears , Then the configuration is successful .
tesseract-ocr Chinese recognition is not supported by default . Support Chinese recognition .png
In the self installed pytesseract In bag , find pytesseract.py file 
open pytesseract.py file , modify tesseract_cmd Value ：tesseract.exe Installation path for .
To avoid other mistakes , Use double backslashes , Or slashes
import pytesseract
from PIL import Image
if __name__ == '__main__':
text = pytesseract.image_to_string(Image.open("D:\\test.png"),lang="eng")
# If you want to try Tesseract Identifying Chinese , Just put the... In the code eng Change it to chi_sim that will do
print(text)
The test image ：
Output results ：
use Tesseract Can recognize the text with standard format , It has the following characteristics ：
Then there is a slightly slanted text picture th.jpg, The identification is as follows ：
You can see that the recognition is not as good as the standard font , But it can also recognize most of the letters in the picture .
The last is to recognize simplified Chinese , You need to install the simplified Chinese language pack in advance , Download at ：https://github.com/tesseract-ocr/tessdata/find/master/chi_sim.traineddata , Let's talk about it again chi_sim.traineddata Put it in C:\Program Files (x86)\Tesseract-OCR\tessdata Under the table of contents . We use pictures timg.jpg For example ：
Enter the command ：
tesseract E://figures/other/timg.jpg E://figures/other/timg.txt -l chi_sim
The results are as follows ：
Only one wrong word was recognized , The recognition rate is good .
Add one last sentence ,Tesseract The recognition effect of color pictures is not as good as that of black-and-white pictures .
pytesseract yes Tesseract About Python The interface of , have access to pip install pytesseract install . After the installation , Can Use Python call Tesseract 了 , however , You need one more Python Image processing module , Can install pillow.
Enter the following code , The same as the above can be achieved Tesseract The same effect as command ：
import pytesseract
from PIL import Image
pytesseract.pytesseract.tesseract_cmd = 'C://Program Files (x86)/Tesseract-OCR/tesseract.exe'
text = pytesseract.image_to_string(Image.open('E://figures/other/poems.jpg'))
print(text)
The operation results are as follows ：
How to use the two tools and their comparative effects .
pip install cnocr
notice Successfully installed xxx The installation is successful .
If you only want to recognize the Chinese in the picture , that cnocr It's a good choice , You just need to install cnocr Bag can .
But if you want to try other languages OCR distinguish ,Tesseract It's a better choice .
cnocr It is mainly aimed at printed words and pictures with simple typesetting , As shown in the screenshot , Scanning copy, etc . At present, the built-in text detection and branch module can not handle complex text typesetting and positioning .
Although it provides single line recognition function and multi line recognition function respectively , But under my actual measurement , The effect of single line recognition function is very bad , Or the requirements are very harsh , Basically, I can't even recognize the words in the screenshot .
But the multiline recognition function is good , The code identified by using this function is as follows ：
from cnocr import CnOcr
ocr = CnOcr()
res = ocr.ocr('test.png')
print("Predicted Chars:", res)
Used to identify the text in this picture ：
The effect is as follows ：
If not very picky , This effect is already very good .
advantage ： Easy to use , Powerful
shortcoming ： There is a charge for heavy use
Baidu is my own call API The way , Here are my steps ：
Register baidu account , establish OCR Application can refer to other tutorials .
Use after purchase python Calling method
Mode one ： adopt urllib Call directly , Replace your own api_key and secret_key that will do
# coding=utf-8
import sys
import json
import base64
# Guarantee compatibility python2 as well as python3
IS_PY3 = sys.version_info.major == 3
if IS_PY3:
from urllib.request import urlopen
from urllib.request import Request
from urllib.error import URLError
from urllib.parse import urlencode
from urllib.parse import quote_plus
else:
import urllib2
from urllib import quote_plus
from urllib2 import urlopen
from urllib2 import Request
from urllib2 import URLError
from urllib import urlencode
# prevent https Certificate verification is incorrect
import ssl
ssl._create_default_https_context = ssl._create_unverified_context
API_KEY = 'YsZKG1wha34PlDOPYaIrIIKO'
SECRET_KEY = 'HPRZtdOHrdnnETVsZM2Nx7vbDkMfxrkD'
OCR_URL = "https://aip.baidubce.com/rest/2.0/ocr/v1/accurate_basic"
""" TOKEN start """
TOKEN_URL = 'https://aip.baidubce.com/oauth/2.0/token'
""" obtain token """
def fetch_token():
params = {
'grant_type': 'client_credentials',
'client_id': API_KEY,
'client_secret': SECRET_KEY}
post_data = urlencode(params)
if (IS_PY3):
post_data = post_data.encode('utf-8')
req = Request(TOKEN_URL, post_data)
try:
f = urlopen(req, timeout=5)
result_str = f.read()
except URLError as err:
print(err)
if (IS_PY3):
result_str = result_str.decode()
result = json.loads(result_str)
if ('access_token' in result.keys() and 'scope' in result.keys()):
if not 'brain_all_scope' in result['scope'].split(' '):
print ('please ensure has check the ability')
exit()
return result['access_token']
else:
print ('please overwrite the correct API_KEY and SECRET_KEY')
exit()
""" Read the file """
def read_file(image_path):
f = None
try:
f = open(image_path, 'rb')
return f.read()
except:
print('read image file fail')
return None
finally:
if f:
f.close()
""" Call remote service """
def request(url, data):
req = Request(url, data.encode('utf-8'))
has_error = False
try:
f = urlopen(req)
result_str = f.read()
if (IS_PY3):
result_str = result_str.decode()
return result_str
except URLError as err:
print(err)
if __name__ == '__main__':
# obtain access token
token = fetch_token()
# Splicing universal character recognition with high precision url
image_url = OCR_URL + "?access_token=" + token
text = ""
# Read the test picture
file_content = read_file('test.jpg')
# Call the character recognition service
result = request(image_url, urlencode({
'image': base64.b64encode(file_content)}))
# Parsing returns results
result_json = json.loads(result)
print(result_json)
for words_result in result_json["words_result"]:
text = text + words_result["words"]
# Print text
print(text)
Mode two ： adopt HTTP-SDK Module
from aip import AipOcr
APP_ID = '25**9878'
API_KEY = 'VGT8y***EBf2O8xNRxyHrPNr'
SECRET_KEY = 'ckDyzG*****N3t0MTgvyYaKUnSl6fSw'
client = AipOcr(APP_ID,API_KEY,SECRET_KEY)
def get_file_content(filePath):
with open(filePath, 'rb') as fp:
return fp.read()
image = get_file_content('test.jpg')
res = client.basicGeneral(image)
print(res)
#res = client.basicAccurate(image)
#print(res)
Directly recognize the text on the specified area of the screen
from aip import AipOcr
APP_ID = '25**9878'
API_KEY = 'VGT8y***EBf2O8xNRxyHrPNr'
SECRET_KEY = 'ckDyzG*****N3t0MTgvyYaKUnSl6fSw'
client = AipOcr(APP_ID,API_KEY,SECRET_KEY)
from io import BytesIO
from PIL import ImageGrab
out_buffer = BytesIO()
img = ImageGrab.grab((100,200,300,400))
img.save(out_buffer,format='PNG')
res = client.basicGeneral(out_buffer.getvalue())
print(res)
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