Pytesseract or Python-tesseract is an Optical Character Recognition (OCR) tool for Python. It will read and recognize the text in images, license plates etc. Python-tesseract is actually a wrapper class or a package for Google’s Tesseract-OCR Engine. It is also useful and regarded as a stand-alone invocation script to tesseract, as it can easily read all image types supported by the Pillow and Leptonica imaging libraries, which mainly includes –
- jpg
- png
- gif
- bmp
- tiff etc
Also additionally, if it is used as a script, Python-tesseract will also print the recognized text instead of writing it to a file. Python-tesseract can be installed using pip as shown below –
pip install pytesseract
If you are using Anaconda Cloud, Python-tesseract can be installed as shown below:-
conda install -c conda-forge/label/cf202003 pytesseract
or
conda install -c conda-forge pytesseract
Note: tesseract should be installed in the system before running the below script.
Below is the implementation.
from pytesseract import *
import argparse
import cv2
# We construct the argument parser # and parse the arguments ap = argparse.ArgumentParser()
ap.add_argument( "-i" , "--image" ,
required = True ,
help = "path to input image to be OCR'd" )
ap.add_argument( "-c" , "--min-conf" ,
type = int , default = 0 ,
help = "minimum confidence value to filter weak text detection" )
args = vars (ap.parse_args())
# We load the input image and then convert # it to RGB from BGR. We then use Tesseract # to localize each area of text in the input # image images = cv2.imread(args[ "image" ])
rgb = cv2.cvtColor(images, cv2.COLOR_BGR2RGB)
results = pytesseract.image_to_data(rgb, output_type = Output. DICT )
# Then loop over each of the individual text # localizations for i in range ( 0 , len (results[ "text" ])):
# We can then extract the bounding box coordinates
# of the text region from the current result
x = results[ "left" ][i]
y = results[ "top" ][i]
w = results[ "width" ][i]
h = results[ "height" ][i]
# We will also extract the OCR text itself along
# with the confidence of the text localization
text = results[ "text" ][i]
conf = int (results[ "conf" ][i])
# filter out weak confidence text localizations
if conf > args[ "min_conf" ]:
# We will display the confidence and text to
# our terminal
print ( "Confidence: {}" . format (conf))
print ( "Text: {}" . format (text))
print ("")
# We then strip out non-ASCII text so we can
# draw the text on the image We will be using
# OpenCV, then draw a bounding box around the
# text along with the text itself
text = "".join(text).strip()
cv2.rectangle(images,
(x, y),
(x + w, y + h),
( 0 , 0 , 255 ), 2 )
cv2.putText(images,
text,
(x, y - 10 ),
cv2.FONT_HERSHEY_SIMPLEX,
1.2 , ( 0 , 255 , 255 ), 3 )
# After all, we will show the output image cv2.imshow( "Image" , images)
cv2.waitKey( 0 )
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Output:
Execute the command below to view the Output
python ocr.py --image ocr.png
In addition to Output, we will see the Confidence Level and the Text In Command Prompt as shown below –
Confidence: 93 Text: I Confidence: 93 Text: LOVE Confidence: 91 Text: TESSERACT