Text Localization, Detection and Recognition using Pytesseract

30 Nov, 2021
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. 
Python3
from pytesseract import*import argparseimport cv2  # We construct the argument parser# and parse the argumentsap = 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# imageimages = 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# localizationsfor 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 imagecv2.imshow("Image", images)cv2.waitKey(0)
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
Akanksha_Rai
surindertarika1234
python-modules
Python-OpenCV
python-utility
Python
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